Data

source("0_helpers.R")

load("data/cleaned_selected_wrangled.rdata")

data = data %>%
  filter(!(contraception_method %contains% "hormonal_other"))

Effects of Hormonal Contraceptives

Attractiveness of Partner

Model

m_hc_atrr = brm(attractiveness_partner ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_atrr_robust")

Summary

summary(m_hc_atrr, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: attractiveness_partner ~ contraception_hormonal 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                           Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## Intercept                     4.21      0.04     4.15     4.27 1.00     3949     3006
## contraception_hormonalyes     0.09      0.05     0.00     0.18 1.00     4278     2945
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.73      0.02     0.70     0.76 1.00     4609     2981
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00741
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.07      0.07           0.333 Undecided        0.00284    0.178 fixed   conditio~

Plots

conditional_effects(m_hc_atrr, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_atrr %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

Relationship Satisfaction

Model

m_hc_relsat = brm(relationship_satisfaction ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_relsat_robust")

Summary

summary(m_hc_relsat, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: relationship_satisfaction ~ contraception_hormonal 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                           Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## Intercept                     3.35      0.02     3.32     3.39 1.00     4081     2899
## contraception_hormonalyes     0.09      0.03     0.04     0.15 1.00     4051     2820
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.42      0.01     0.40     0.44 1.00     4667     2871
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00443
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.04      0.04          0.0119 Undecided         0.0377    0.144 fixed   conditio~

Plots

conditional_effects(m_hc_relsat, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_relsat %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

Sexual Satisfaction

Model

m_hc_sexsat = brm(satisfaction_sexual_intercourse ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_sexsat_robust")

Summary

summary(m_hc_sexsat, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: satisfaction_sexual_intercourse ~ contraception_hormonal 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                           Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## Intercept                     3.93      0.05     3.85     4.02 1.00     4760     3121
## contraception_hormonalyes     0.19      0.08     0.06     0.32 1.00     4380     3040
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     1.04      0.03     0.99     1.09 1.00     4047     2956
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.0109
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.11      0.11           0.143 Undecided         0.0480    0.306 fixed   conditio~

Plots

conditional_effects(m_hc_sexsat, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_sexsat %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

Libido

Model

m_hc_libido = brm(diary_libido_mean ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_libido_robust")

Summary

summary(m_hc_libido, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: diary_libido_mean ~ contraception_hormonal 
##    Data: data (Number of observations: 910) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                           Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## Intercept                     1.18      0.02     1.14     1.22 1.00     4384     3207
## contraception_hormonalyes    -0.01      0.04    -0.08     0.06 1.00     4191     2611
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.59      0.01     0.57     0.62 1.00     3977     3271
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00577
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.06      0.06           0.928 Undecided        -0.0765   0.0604 fixed   conditio~

Plots

conditional_effects(m_hc_libido, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_libido %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

Sexual Frequency (Penetrative Intercourse)

Model

m_hc_sexfreqpen = brm(diary_sex_active_sex_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal,
                data = data, family = poisson(),
                file = "m_hc_sexfreqpen_robust")

Summary

summary(m_hc_sexfreqpen, conf.int = T, prob = 0.90)
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_sex_active_sex_sum ~ offset(log(number_of_days)) + contraception_hormonal 
##    Data: data (Number of observations: 839) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                           Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## Intercept                    -2.09      0.02    -2.12    -2.06 1.00     3055     2548
## contraception_hormonalyes     0.20      0.03     0.16     0.25 1.00     3383     2771
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_sexfreqpen, range = c(-0.05, 0.05)), ci = 0.90,
                      parameters = "contraception")
## Picking joint bandwidth of 0.00389
## Warning: Removed 199 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_sexfreqpen, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.05      0.05               0 Rejected           0.161    0.245 fixed   conditio~

Plots

conditional_effects(m_hc_sexfreqpen,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_hc_sexfreqpen %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Masturbation Frequency

Model

m_hc_masfreq = brm(diary_masturbation_sum ~ offset(log(number_of_days)) +
                     contraception_hormonal,
                data = data, family = poisson(),
                file = "m_hc_masfreq_robust")

Summary

summary(m_hc_masfreq, conf.int = T, prob = 0.90)
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_masturbation_sum ~ offset(log(number_of_days)) + contraception_hormonal 
##    Data: data (Number of observations: 839) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                           Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## Intercept                    -1.88      0.02    -1.90    -1.85 1.00     3763     2771
## contraception_hormonalyes    -0.44      0.03    -0.49    -0.39 1.00     2664     2128
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00402
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.05      0.05               0 Rejected          -0.485   -0.389 fixed   conditio~

Plots

conditional_effects(m_hc_masfreq,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_hc_masfreq %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Controlled Models: Effects of Hormonal Contraceptives

Attractiveness of Partner

Model

m_hc_atrr_controlled = brm(attractiveness_partner ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_atrr_controlled_robust")

Summary

summary(m_hc_atrr_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: attractiveness_partner ~ contraception_hormonal + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                 66.77    410.67  -573.74   702.13 2.75        5
## contraception_hormonalyes                                  0.10      0.06     0.01     0.21 1.03       52
## age                                                        0.00      0.01    -0.01     0.01 1.05       66
## net_incomeeuro_500_1000                                    0.01      0.07    -0.11     0.13 1.04       65
## net_incomeeuro_1000_2000                                   0.10      0.08    -0.02     0.23 1.04       76
## net_incomeeuro_2000_3000                                   0.10      0.11    -0.10     0.27 1.05       65
## net_incomeeuro_gt_3000                                     0.04      0.19    -0.29     0.37 1.16       22
## net_incomedont_tell                                        0.06      0.18    -0.24     0.37 1.14       32
## relationship_duration_factorPartnered_upto12months       -63.34    410.63  -699.05   576.66 2.75        5
## relationship_duration_factorPartnered_upto28months       -63.24    410.63  -698.98   576.79 2.75        5
## relationship_duration_factorPartnered_upto52months       -63.41    410.63  -699.14   576.62 2.75        5
## relationship_duration_factorPartnered_morethan52months   -63.46    410.63  -699.18   576.49 2.75        5
## education_years                                            0.00      0.01    -0.01     0.01 1.09       40
## bfi_extra                                                  0.04      0.04    -0.02     0.10 1.17       19
## bfi_neuro                                                  0.01      0.04    -0.06     0.08 1.38        9
## bfi_agree                                                  0.09      0.05     0.01     0.18 1.23       15
## bfi_consc                                                  0.02      0.04    -0.04     0.08 1.09       61
## bfi_open                                                   0.02      0.04    -0.05     0.09 1.06       64
## religiosity                                                0.01      0.02    -0.03     0.04 1.06       76
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.73      0.02     0.70     0.76 1.11       30       NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00787
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.07      0.07           0.264 Undecided        0.00146    0.199 fixed   conditio~

Plots

conditional_effects(m_hc_atrr, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_atrr_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Relationship Satisfaction

Model

m_hc_relsat_controlled = brm(relationship_satisfaction ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_relsat_controlled_robust")

Summary

summary(m_hc_relsat_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: relationship_satisfaction ~ contraception_hormonal + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                -99.95    348.02  -703.79   347.52 3.31        4
## contraception_hormonalyes                                  0.07      0.03     0.01     0.12 1.10       39
## age                                                       -0.00      0.00    -0.01     0.00 1.20       16
## net_incomeeuro_500_1000                                    0.04      0.04    -0.02     0.10 1.06       43
## net_incomeeuro_1000_2000                                  -0.02      0.05    -0.12     0.07 1.19       15
## net_incomeeuro_2000_3000                                  -0.00      0.08    -0.13     0.12 1.15       19
## net_incomeeuro_gt_3000                                     0.06      0.13    -0.17     0.26 1.12       26
## net_incomedont_tell                                       -0.09      0.10    -0.26     0.07 1.21       16
## relationship_duration_factorPartnered_upto12months       103.24    348.03  -344.11   707.08 3.31        4
## relationship_duration_factorPartnered_upto28months       103.45    348.03  -343.88   707.37 3.31        4
## relationship_duration_factorPartnered_upto52months       103.41    348.03  -343.93   707.32 3.31        4
## relationship_duration_factorPartnered_morethan52months   103.40    348.04  -343.96   707.31 3.31        4
## education_years                                           -0.00      0.00    -0.01     0.00 1.05       84
## bfi_extra                                                  0.02      0.02    -0.01     0.06 1.10       30
## bfi_neuro                                                  0.03      0.02    -0.01     0.07 1.04       51
## bfi_agree                                                 -0.03      0.03    -0.08     0.03 1.13       21
## bfi_consc                                                  0.01      0.02    -0.04     0.04 1.16       20
## bfi_open                                                  -0.02      0.02    -0.05     0.02 1.08       69
## religiosity                                                0.03      0.01     0.01     0.05 1.06       41
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.42      0.01     0.40     0.44 1.31       11       NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.0044
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.04      0.04           0.138 Undecided         0.0189    0.127 fixed   conditio~

Plots

conditional_effects(m_hc_relsat_controlled, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_relsat_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Sexual Satisfaction

Model

m_hc_sexsat_controlled = brm(satisfaction_sexual_intercourse ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_sexsat_controlled_robust")

Summary

summary(m_hc_sexsat_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: satisfaction_sexual_intercourse ~ contraception_hormonal + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                852.36    669.50  -311.85  1751.11 2.83        5
## contraception_hormonalyes                                  0.19      0.08     0.06     0.32 1.08       40
## age                                                        0.01      0.01    -0.01     0.02 1.18       15
## net_incomeeuro_500_1000                                    0.05      0.09    -0.10     0.19 1.18       16
## net_incomeeuro_1000_2000                                  -0.01      0.13    -0.24     0.22 1.24       13
## net_incomeeuro_2000_3000                                   0.00      0.20    -0.30     0.33 1.19       15
## net_incomeeuro_gt_3000                                    -0.28      0.27    -0.75     0.15 1.05       63
## net_incomedont_tell                                        0.17      0.22    -0.19     0.54 1.12       30
## relationship_duration_factorPartnered_upto12months      -849.16    669.45 -1748.14   315.21 2.83        5
## relationship_duration_factorPartnered_upto28months      -849.17    669.45 -1748.01   315.05 2.83        5
## relationship_duration_factorPartnered_upto52months      -849.40    669.45 -1748.29   314.80 2.83        5
## relationship_duration_factorPartnered_morethan52months  -849.45    669.45 -1748.27   314.90 2.83        5
## education_years                                           -0.00      0.01    -0.02     0.01 1.05       65
## bfi_extra                                                  0.10      0.05     0.02     0.18 1.11       27
## bfi_neuro                                                 -0.08      0.06    -0.16     0.02 1.07       42
## bfi_agree                                                  0.14      0.08     0.02     0.27 1.13       26
## bfi_consc                                                  0.11      0.06     0.01     0.20 1.07       52
## bfi_open                                                  -0.09      0.06    -0.18     0.01 1.17       22
## religiosity                                                0.01      0.03    -0.04     0.05 1.29       14
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     1.02      0.03     0.98     1.07 1.14       48       NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.0108
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.11      0.11           0.109 Undecided         0.0656    0.320 fixed   conditio~

Plots

conditional_effects(m_hc_sexsat_controlled, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_sexsat_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Libido

Model

m_hc_libido_controlled = brm(diary_libido_mean ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_libido_controlled_robust")

Summary

summary(m_hc_libido_controlled, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: diary_libido_mean ~ contraception_hormonal + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 910) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                  0.27      0.27    -0.17     0.72 1.00     4476
## contraception_hormonalyes                                 -0.02      0.04    -0.09     0.04 1.00     4296
## age                                                        0.00      0.01    -0.01     0.01 1.00     3178
## net_incomeeuro_500_1000                                    0.09      0.05     0.01     0.17 1.00     3072
## net_incomeeuro_1000_2000                                   0.14      0.07     0.03     0.25 1.00     2401
## net_incomeeuro_2000_3000                                   0.15      0.10    -0.01     0.31 1.00     3091
## net_incomeeuro_gt_3000                                    -0.04      0.18    -0.33     0.26 1.00     3994
## net_incomedont_tell                                        0.13      0.13    -0.08     0.34 1.00     3293
## relationship_duration_factorPartnered_upto12months         0.42      0.06     0.32     0.52 1.00     3251
## relationship_duration_factorPartnered_upto28months         0.30      0.06     0.21     0.39 1.00     3187
## relationship_duration_factorPartnered_upto52months         0.25      0.06     0.16     0.35 1.00     3194
## relationship_duration_factorPartnered_morethan52months     0.21      0.06     0.10     0.31 1.00     3320
## education_years                                           -0.00      0.00    -0.01     0.01 1.00     6750
## bfi_extra                                                  0.09      0.03     0.05     0.13 1.00     3806
## bfi_neuro                                                 -0.02      0.03    -0.07     0.03 1.00     4144
## bfi_agree                                                  0.08      0.03     0.02     0.13 1.00     3691
## bfi_consc                                                 -0.09      0.03    -0.14    -0.04 1.00     4333
## bfi_open                                                   0.11      0.03     0.06     0.17 1.00     4333
## religiosity                                               -0.01      0.01    -0.04     0.01 1.00     4897
##                                                        Tail_ESS
## Intercept                                                  2910
## contraception_hormonalyes                                  3003
## age                                                        3047
## net_incomeeuro_500_1000                                    3148
## net_incomeeuro_1000_2000                                   2943
## net_incomeeuro_2000_3000                                   2944
## net_incomeeuro_gt_3000                                     2673
## net_incomedont_tell                                        3015
## relationship_duration_factorPartnered_upto12months         3161
## relationship_duration_factorPartnered_upto28months         2865
## relationship_duration_factorPartnered_upto52months         3012
## relationship_duration_factorPartnered_morethan52months     3191
## education_years                                            3328
## bfi_extra                                                  2998
## bfi_neuro                                                  3293
## bfi_agree                                                  2940
## bfi_consc                                                  3292
## bfi_open                                                   2777
## religiosity                                                3206
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.56      0.01     0.54     0.58 1.00     4843     3101
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00551
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.06      0.06           0.845 Undecided        -0.0887   0.0409 fixed   conditio~

Plots

conditional_effects(m_hc_libido_controlled, ask = FALSE)

Forest Plot for Effect Sizes

m_hc_libido_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Sexual Frequency (Penetrative Intercourse)

Model

m_hc_sexfreqpen_controlled = brm(diary_sex_active_sex_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_hc_sexfreqpen_controlled_robust")

Summary

summary(m_hc_sexfreqpen_controlled, conf.int = T, prob = 0.90)
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_sex_active_sex_sum ~ offset(log(number_of_days)) + contraception_hormonal + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 839) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                 -3.33      0.19    -3.64    -3.02 1.00     3797
## contraception_hormonalyes                                  0.13      0.03     0.08     0.17 1.00     3695
## age                                                       -0.00      0.00    -0.01     0.01 1.00     3364
## net_incomeeuro_500_1000                                    0.14      0.03     0.09     0.20 1.00     2738
## net_incomeeuro_1000_2000                                   0.15      0.05     0.08     0.23 1.00     2541
## net_incomeeuro_2000_3000                                   0.43      0.06     0.34     0.53 1.00     2749
## net_incomeeuro_gt_3000                                     0.05      0.11    -0.14     0.23 1.00     3661
## net_incomedont_tell                                        0.46      0.08     0.34     0.59 1.00     3269
## relationship_duration_factorPartnered_upto12months         1.34      0.04     1.26     1.41 1.00     1999
## relationship_duration_factorPartnered_upto28months         1.20      0.04     1.13     1.27 1.00     1983
## relationship_duration_factorPartnered_upto52months         0.96      0.05     0.87     1.04 1.00     2222
## relationship_duration_factorPartnered_morethan52months     0.94      0.05     0.86     1.03 1.00     2336
## education_years                                           -0.01      0.00    -0.02    -0.01 1.00     6946
## bfi_extra                                                  0.01      0.02    -0.01     0.04 1.00     3961
## bfi_neuro                                                 -0.01      0.02    -0.04     0.03 1.00     3594
## bfi_agree                                                  0.08      0.02     0.05     0.12 1.00     3603
## bfi_consc                                                 -0.00      0.02    -0.04     0.03 1.00     4598
## bfi_open                                                   0.04      0.02    -0.00     0.07 1.00     4100
## religiosity                                               -0.02      0.01    -0.04    -0.00 1.00     4343
##                                                        Tail_ESS
## Intercept                                                  3224
## contraception_hormonalyes                                  3008
## age                                                        3179
## net_incomeeuro_500_1000                                    2725
## net_incomeeuro_1000_2000                                   2658
## net_incomeeuro_2000_3000                                   3009
## net_incomeeuro_gt_3000                                     3008
## net_incomedont_tell                                        2846
## relationship_duration_factorPartnered_upto12months         3039
## relationship_duration_factorPartnered_upto28months         3068
## relationship_duration_factorPartnered_upto52months         2773
## relationship_duration_factorPartnered_morethan52months     2765
## education_years                                            3211
## bfi_extra                                                  3172
## bfi_neuro                                                  3131
## bfi_agree                                                  2756
## bfi_consc                                                  2968
## bfi_open                                                   3040
## religiosity                                                2910
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_sexfreqpen_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00384
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_sexfreqpen_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.05      0.05               0 Rejected          0.0788    0.171 fixed   conditio~

Plots

conditional_effects(m_hc_sexfreqpen_controlled,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_hc_sexfreqpen_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Masturabation Frequency

Model

m_hc_masfreq_controlled = brm(diary_masturbation_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_hc_masfreq_controlled_robust")

Summary

summary(m_hc_masfreq_controlled, conf.int = T, prob = 0.90)
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_masturbation_sum ~ offset(log(number_of_days)) + contraception_hormonal + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 839) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                 -1.68      0.19    -2.01    -1.37 1.00     4868
## contraception_hormonalyes                                 -0.35      0.03    -0.40    -0.30 1.00     4571
## age                                                       -0.00      0.00    -0.01     0.00 1.00     4025
## net_incomeeuro_500_1000                                    0.18      0.03     0.12     0.23 1.00     2981
## net_incomeeuro_1000_2000                                   0.20      0.05     0.12     0.28 1.00     2711
## net_incomeeuro_2000_3000                                  -0.02      0.08    -0.15     0.10 1.00     3262
## net_incomeeuro_gt_3000                                    -0.17      0.14    -0.41     0.06 1.00     4590
## net_incomedont_tell                                       -0.22      0.11    -0.40    -0.05 1.00     4038
## relationship_duration_factorPartnered_upto12months        -0.21      0.04    -0.28    -0.15 1.00     3947
## relationship_duration_factorPartnered_upto28months        -0.17      0.04    -0.23    -0.10 1.00     3959
## relationship_duration_factorPartnered_upto52months        -0.29      0.04    -0.36    -0.22 1.00     4035
## relationship_duration_factorPartnered_morethan52months    -0.45      0.04    -0.52    -0.38 1.00     3655
## education_years                                            0.00      0.00    -0.00     0.01 1.00     8241
## bfi_extra                                                 -0.03      0.02    -0.06     0.00 1.00     4570
## bfi_neuro                                                 -0.01      0.02    -0.04     0.02 1.00     4061
## bfi_agree                                                  0.00      0.02    -0.04     0.04 1.00     4591
## bfi_consc                                                 -0.20      0.02    -0.23    -0.17 1.00     5059
## bfi_open                                                   0.21      0.02     0.17     0.25 1.00     5451
## religiosity                                               -0.05      0.01    -0.07    -0.04 1.00     5192
##                                                        Tail_ESS
## Intercept                                                  3108
## contraception_hormonalyes                                  3352
## age                                                        2837
## net_incomeeuro_500_1000                                    2962
## net_incomeeuro_1000_2000                                   2814
## net_incomeeuro_2000_3000                                   3012
## net_incomeeuro_gt_3000                                     3336
## net_incomedont_tell                                        2804
## relationship_duration_factorPartnered_upto12months         3046
## relationship_duration_factorPartnered_upto28months         3115
## relationship_duration_factorPartnered_upto52months         2804
## relationship_duration_factorPartnered_morethan52months     3208
## education_years                                            3059
## bfi_extra                                                  3306
## bfi_neuro                                                  3165
## bfi_agree                                                  3149
## bfi_consc                                                  3354
## bfi_open                                                   3300
## religiosity                                                3090
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_hc_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00416
## Warning: Removed 399 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_hc_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 1 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.05      0.05               0 Rejected          -0.400   -0.301 fixed   conditio~

Plots

conditional_effects(m_hc_masfreq_controlled,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_hc_masfreq_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Effects of (In)Congruenct HC Use

Attractiveness of Partner

Model

m_congruency_atrr = brm(attractiveness_partner ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_atrr_robust")

Summary

summary(m_congruency_atrr, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: attractiveness_partner ~ contraception_hormonal * congruent_contraception 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                    Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                              4.13      0.06     4.03     4.22 1.00     2505
## contraception_hormonalyes                              0.15      0.09     0.00     0.30 1.00     2166
## congruent_contraception1                               0.14      0.07     0.02     0.26 1.00     2448
## contraception_hormonalyes:congruent_contraception1    -0.10      0.11    -0.28     0.09 1.00     1917
##                                                    Tail_ESS
## Intercept                                              2989
## contraception_hormonalyes                              2464
## congruent_contraception1                               2750
## contraception_hormonalyes:congruent_contraception1     2333
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.73      0.02     0.69     0.76 1.00     3711     2781
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.8). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.0129
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.8). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter         CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence  HDI_low HDI_high Effects Component
##   <chr>          <dbl>    <dbl>     <dbl>           <dbl> <chr>               <dbl>    <dbl> <chr>   <chr>    
## 1 b_contracepti~   0.9    -0.07      0.07           0.157 Undecided        -9.35e-4   0.296  fixed   conditio~
## 2 b_congruent_c~   0.9    -0.07      0.07           0.135 Undecided         2.27e-2   0.266  fixed   conditio~
## 3 b_contracepti~   0.9    -0.07      0.07           0.377 Undecided        -2.85e-1   0.0872 fixed   conditio~

Plots

conditional_effects(m_congruency_atrr, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_atrr %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

Relationship Satisfaction

Model

m_congruency_relsat = brm(relationship_satisfaction ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_relsat_robust")

Summary

summary(m_congruency_relsat, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: relationship_satisfaction ~ contraception_hormonal * congruent_contraception 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                    Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                              3.42      0.03     3.36     3.47 1.00     2468
## contraception_hormonalyes                              0.06      0.05    -0.03     0.15 1.00     2218
## congruent_contraception1                              -0.10      0.04    -0.17    -0.03 1.00     2157
## contraception_hormonalyes:congruent_contraception1     0.05      0.07    -0.05     0.16 1.00     2043
##                                                    Tail_ESS
## Intercept                                              2991
## contraception_hormonalyes                              2582
## congruent_contraception1                               2541
## contraception_hormonalyes:congruent_contraception1     2705
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.42      0.01     0.40     0.44 1.00     3775     2635
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.79). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.00733
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.79). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.04      0.04          0.306  Undecided        -0.0182   0.154  fixed   conditio~
## 2 b_congruent_co~   0.9    -0.04      0.04          0.0322 Undecided        -0.168   -0.0301 fixed   conditio~
## 3 b_contraceptio~   0.9    -0.04      0.04          0.381  Undecided        -0.0520   0.158  fixed   conditio~

Plots

conditional_effects(m_congruency_relsat, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_relsat %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

Sexual Satisfaction

Model

m_congruency_sexsat = brm(satisfaction_sexual_intercourse ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_sexsat_robust")

Summary

summary(m_congruency_sexsat, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: satisfaction_sexual_intercourse ~ contraception_hormonal * congruent_contraception 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                    Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                              3.84      0.08     3.70     3.97 1.00     2313
## contraception_hormonalyes                              0.27      0.13     0.05     0.49 1.00     1842
## congruent_contraception1                               0.16      0.11    -0.01     0.33 1.00     2085
## contraception_hormonalyes:congruent_contraception1    -0.14      0.16    -0.41     0.13 1.00     1756
##                                                    Tail_ESS
## Intercept                                              2741
## contraception_hormonalyes                              2335
## congruent_contraception1                               2770
## contraception_hormonalyes:congruent_contraception1     2313
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     1.04      0.03     0.99     1.08 1.00     3597     3011
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.8). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.0183
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.8). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter         CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence  HDI_low HDI_high Effects Component
##   <chr>          <dbl>    <dbl>     <dbl>           <dbl> <chr>               <dbl>    <dbl> <chr>   <chr>    
## 1 b_contracepti~   0.9    -0.11      0.11          0.0611 Undecided         0.0601     0.492 fixed   conditio~
## 2 b_congruent_c~   0.9    -0.11      0.11          0.290  Undecided        -0.00253    0.343 fixed   conditio~
## 3 b_contracepti~   0.9    -0.11      0.11          0.419  Undecided        -0.419      0.119 fixed   conditio~

Plots

conditional_effects(m_congruency_sexsat, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_sexsat %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)
## Warning: Removed 41 rows containing missing values (stat_slabinterval).

Libido

Model

m_congruency_libido = brm(diary_libido_mean ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_libido_robust")

Summary

summary(m_congruency_libido, conf.int = T, prob = 0.90)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: diary_libido_mean ~ contraception_hormonal * congruent_contraception 
##    Data: data (Number of observations: 586) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                    Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                              1.24      0.05     1.16     1.32 1.00     2215
## contraception_hormonalyes                             -0.03      0.08    -0.15     0.10 1.00     2021
## congruent_contraception1                               0.08      0.06    -0.02     0.18 1.00     2065
## contraception_hormonalyes:congruent_contraception1    -0.02      0.09    -0.18     0.12 1.00     1824
##                                                    Tail_ESS
## Intercept                                              2780
## contraception_hormonalyes                              2389
## congruent_contraception1                               2393
## contraception_hormonalyes:congruent_contraception1     2302
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.54      0.02     0.52     0.57 1.00     3484     2977
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.79). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.0106
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.79). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.06      0.06           0.585 Undecided        -0.160    0.0889 fixed   conditio~
## 2 b_congruent_co~   0.9    -0.06      0.06           0.375 Undecided        -0.0207   0.175  fixed   conditio~
## 3 b_contraceptio~   0.9    -0.06      0.06           0.505 Undecided        -0.179    0.128  fixed   conditio~

Plots

conditional_effects(m_congruency_libido, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_libido %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

Sexual Frequency (Penetrative Intercourse)

Model

m_congruency_sexfreqpen = brm(diary_sex_active_sex_sum ~ offset(log(number_of_days)) +
                     contraception_hormonal * congruent_contraception,
                data = data, family = poisson(),
                file = "m_congruency_sexfreqpen_robust")

Summary

summary(m_congruency_sexfreqpen, conf.int = T, prob = 0.90)
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_sex_active_sex_sum ~ offset(log(number_of_days)) + contraception_hormonal * congruent_contraception 
##    Data: data (Number of observations: 576) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                    Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                             -1.87      0.03    -1.92    -1.82 1.00     1711
## contraception_hormonalyes                              0.14      0.05     0.06     0.22 1.00     1579
## congruent_contraception1                               0.09      0.04     0.02     0.15 1.00     1787
## contraception_hormonalyes:congruent_contraception1    -0.05      0.06    -0.15     0.05 1.00     1542
##                                                    Tail_ESS
## Intercept                                              2547
## contraception_hormonalyes                              2111
## congruent_contraception1                               2485
## contraception_hormonalyes:congruent_contraception1     2150
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_sexfreqpen, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.82). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.00692
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_sexfreqpen, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.82). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.05      0.05           0     Rejected          0.0562   0.215  fixed   conditio~
## 2 b_congruent_co~   0.9    -0.05      0.05           0.135 Undecided         0.0224   0.152  fixed   conditio~
## 3 b_contraceptio~   0.9    -0.05      0.05           0.483 Undecided        -0.150    0.0494 fixed   conditio~

Plots

conditional_effects(m_congruency_sexfreqpen,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_congruency_sexfreqpen %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.051), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Masturbation Frequency

Model

m_congruency_masfreq = brm(diary_masturbation_sum ~ offset(log(number_of_days)) +
                             contraception_hormonal * congruent_contraception,
                data = data, family = poisson(),
                file = "m_congruency_masfreq_robust")

Summary

summary(m_congruency_masfreq, conf.int = T, prob = 0.90)
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_masturbation_sum ~ offset(log(number_of_days)) + contraception_hormonal * congruent_contraception 
##    Data: data (Number of observations: 576) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                    Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                             -2.09      0.03    -2.14    -2.03 1.01     2047
## contraception_hormonalyes                             -0.41      0.06    -0.51    -0.31 1.01     1674
## congruent_contraception1                               0.11      0.04     0.04     0.18 1.01     1793
## contraception_hormonalyes:congruent_contraception1     0.03      0.07    -0.09     0.16 1.00     1599
##                                                    Tail_ESS
## Intercept                                              2746
## contraception_hormonalyes                              2131
## congruent_contraception1                               2070
## contraception_hormonalyes:congruent_contraception1     2354
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.82). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.00834
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.82). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.05      0.05          0      Rejected         -0.506    -0.302 fixed   conditio~
## 2 b_congruent_co~   0.9    -0.05      0.05          0.0294 Undecided         0.0426    0.180 fixed   conditio~
## 3 b_contraceptio~   0.9    -0.05      0.05          0.510  Undecided        -0.0927    0.153 fixed   conditio~

Plots

conditional_effects(m_congruency_masfreq,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_congruency_masfreq %>%
spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Controlled Models: Effects of (In)Congruenct HC Use

Attractiveness of Partner

Model

m_congruency_atrr_controlled = brm(attractiveness_partner ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_atrr_controlled_robust")

Summary

summary(m_congruency_atrr_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: attractiveness_partner ~ contraception_hormonal * congruent_contraception + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                 63.96    307.30  -545.89   528.00 2.43        5
## contraception_hormonalyes                                  0.10      0.11    -0.06     0.29 1.33       10
## congruent_contraception1                                   0.08      0.08    -0.04     0.22 1.22       14
## age                                                       -0.00      0.01    -0.01     0.01 1.09       34
## net_incomeeuro_500_1000                                   -0.00      0.05    -0.09     0.09 1.15       22
## net_incomeeuro_1000_2000                                   0.10      0.08    -0.03     0.21 1.08       42
## net_incomeeuro_2000_3000                                   0.10      0.11    -0.08     0.27 1.05       42
## net_incomeeuro_gt_3000                                     0.03      0.21    -0.30     0.37 1.07       46
## net_incomedont_tell                                        0.00      0.19    -0.28     0.31 1.10       31
## relationship_duration_factorPartnered_upto12months       -60.50    307.37  -524.70   549.69 2.43        5
## relationship_duration_factorPartnered_upto28months       -60.39    307.37  -524.58   549.79 2.43        5
## relationship_duration_factorPartnered_upto52months       -60.53    307.37  -524.72   549.69 2.43        5
## relationship_duration_factorPartnered_morethan52months   -60.58    307.37  -524.78   549.47 2.43        5
## education_years                                            0.01      0.01    -0.01     0.02 1.15       20
## bfi_extra                                                  0.03      0.04    -0.03     0.09 1.13       33
## bfi_neuro                                                 -0.01      0.04    -0.08     0.06 1.09       33
## bfi_agree                                                  0.08      0.05    -0.00     0.16 1.13       21
## bfi_consc                                                  0.02      0.05    -0.06     0.11 1.27       12
## bfi_open                                                   0.06      0.04    -0.01     0.13 1.17       17
## religiosity                                                0.00      0.02    -0.03     0.03 1.24       13
## contraception_hormonalyes:congruent_contraception1        -0.02      0.13    -0.25     0.18 1.31       11
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## congruent_contraception1                                     NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## contraception_hormonalyes:congruent_contraception1           NA
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.72      0.02     0.70     0.76 1.24       15       NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.81), b_contraception_hormonalyes:congruent_contraception1 and b_congruent_contraception1 (r = 0.71). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.0148
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.81), b_contraception_hormonalyes:congruent_contraception1 and b_congruent_contraception1 (r = 0.71). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.07      0.07           0.427 Undecided        -0.0769    0.266 fixed   conditio~
## 2 b_congruent_co~   0.9    -0.07      0.07           0.460 Undecided        -0.0417    0.218 fixed   conditio~
## 3 b_contraceptio~   0.9    -0.07      0.07           0.447 Undecided        -0.239     0.187 fixed   conditio~

Plots

conditional_effects(m_congruency_atrr_controlled, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_atrr_controlled %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Relationship Satisfaction

Model

m_congruency_relsat_controlled = brm(relationship_satisfaction ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_relsat_controlled_robust")

Summary

summary(m_congruency_relsat_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: relationship_satisfaction ~ contraception_hormonal * congruent_contraception + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                203.22    279.83  -156.84   609.50 2.65        5
## contraception_hormonalyes                                  0.04      0.07    -0.07     0.14 1.35       10
## congruent_contraception1                                  -0.07      0.05    -0.15     0.01 1.43        8
## age                                                       -0.00      0.00    -0.01     0.00 1.16       17
## net_incomeeuro_500_1000                                    0.06      0.04    -0.01     0.12 1.36       10
## net_incomeeuro_1000_2000                                   0.00      0.05    -0.08     0.09 1.20       18
## net_incomeeuro_2000_3000                                   0.03      0.08    -0.10     0.15 1.52        8
## net_incomeeuro_gt_3000                                     0.09      0.14    -0.15     0.33 1.17       17
## net_incomedont_tell                                       -0.06      0.09    -0.21     0.10 1.24       14
## relationship_duration_factorPartnered_upto12months      -199.82    279.77  -605.95   160.08 2.65        5
## relationship_duration_factorPartnered_upto28months      -199.62    279.77  -605.76   160.29 2.65        5
## relationship_duration_factorPartnered_upto52months      -199.68    279.77  -605.83   160.25 2.65        5
## relationship_duration_factorPartnered_morethan52months  -199.68    279.77  -605.83   160.18 2.65        5
## education_years                                           -0.00      0.00    -0.01     0.00 1.08       40
## bfi_extra                                                  0.02      0.02    -0.01     0.05 1.09       44
## bfi_neuro                                                  0.02      0.02    -0.02     0.06 1.16       19
## bfi_agree                                                 -0.03      0.02    -0.07     0.01 1.06       54
## bfi_consc                                                  0.00      0.03    -0.05     0.04 1.32       10
## bfi_open                                                  -0.02      0.03    -0.07     0.03 1.19       19
## religiosity                                                0.03      0.01     0.01     0.05 1.08       38
## contraception_hormonalyes:congruent_contraception1         0.05      0.08    -0.08     0.18 1.53        7
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## congruent_contraception1                                     NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## contraception_hormonalyes:congruent_contraception1           NA
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.41      0.01     0.40     0.43 1.22       15       NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.84), b_contraception_hormonalyes:congruent_contraception1 and b_congruent_contraception1 (r = 0.72). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.00938
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.84), b_contraception_hormonalyes:congruent_contraception1 and b_congruent_contraception1 (r = 0.72). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.04      0.04           0.450 Undecided        -0.0768  0.135   fixed   conditio~
## 2 b_congruent_co~   0.9    -0.04      0.04           0.277 Undecided        -0.143   0.00709 fixed   conditio~
## 3 b_contraceptio~   0.9    -0.04      0.04           0.359 Undecided        -0.0599  0.191   fixed   conditio~

Plots

conditional_effects(m_congruency_relsat_controlled, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_relsat_controlled %>%
  
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Sexual Satisfaction

Model

m_congruency_sexsat_controlled = brm(satisfaction_sexual_intercourse ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_sexsat_controlled_robust")

Summary

summary(m_congruency_sexsat_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: satisfaction_sexual_intercourse ~ contraception_hormonal * congruent_contraception + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 710) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                 33.95    887.32 -1276.75  1540.41 3.09        5
## contraception_hormonalyes                                  0.22      0.12     0.04     0.42 1.14       21
## congruent_contraception1                                   0.08      0.10    -0.08     0.25 1.07       42
## age                                                        0.01      0.01    -0.01     0.02 1.09       38
## net_incomeeuro_500_1000                                    0.03      0.12    -0.17     0.23 1.29       11
## net_incomeeuro_1000_2000                                  -0.03      0.14    -0.27     0.20 1.28       11
## net_incomeeuro_2000_3000                                  -0.03      0.19    -0.34     0.30 1.20       15
## net_incomeeuro_gt_3000                                    -0.19      0.31    -0.70     0.31 1.15       21
## net_incomedont_tell                                        0.15      0.24    -0.25     0.51 1.17       18
## relationship_duration_factorPartnered_upto12months       -30.83    887.31 -1537.35  1279.32 3.09        5
## relationship_duration_factorPartnered_upto28months       -30.83    887.31 -1537.40  1279.37 3.09        5
## relationship_duration_factorPartnered_upto52months       -31.05    887.31 -1537.57  1279.02 3.09        5
## relationship_duration_factorPartnered_morethan52months   -31.09    887.31 -1537.58  1278.98 3.09        5
## education_years                                           -0.00      0.01    -0.02     0.01 1.13       23
## bfi_extra                                                  0.11      0.06     0.02     0.21 1.14       30
## bfi_neuro                                                 -0.06      0.06    -0.16     0.04 1.04       54
## bfi_agree                                                  0.16      0.07     0.03     0.28 1.06       43
## bfi_consc                                                  0.13      0.06     0.02     0.22 1.06       52
## bfi_open                                                  -0.12      0.07    -0.22    -0.01 1.04       63
## religiosity                                                0.01      0.03    -0.04     0.06 1.02       74
## contraception_hormonalyes:congruent_contraception1        -0.12      0.14    -0.35     0.10 1.16       19
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## congruent_contraception1                                     NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                    125
## religiosity                                                 157
## contraception_hormonalyes:congruent_contraception1           NA
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     1.02      0.02     0.98     1.06 1.02       85       NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.73). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.0172
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.73). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.11      0.11           0.139 Undecided         0.0396    0.418 fixed   conditio~
## 2 b_congruent_co~   0.9    -0.11      0.11           0.660 Undecided        -0.0801    0.254 fixed   conditio~
## 3 b_contraceptio~   0.9    -0.11      0.11           0.469 Undecided        -0.354     0.101 fixed   conditio~

Plots

conditional_effects(m_congruency_sexsat_controlled, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_sexsat_controlled %>%
  
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Libido

Model

m_congruency_libido_controlled = brm(diary_libido_mean ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_libido_controlled_robust")

Summary

summary(m_congruency_libido_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: diary_libido_mean ~ contraception_hormonal * congruent_contraception + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 586) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                343.12    243.60   -30.14   848.28 2.77        5
## contraception_hormonalyes                                 -0.04      0.06    -0.13     0.05 1.13       23
## congruent_contraception1                                   0.02      0.06    -0.07     0.11 1.08       39
## age                                                       -0.01      0.01    -0.02     0.00 1.10       42
## net_incomeeuro_500_1000                                    0.12      0.05     0.04     0.20 1.12       23
## net_incomeeuro_1000_2000                                   0.13      0.08     0.01     0.26 1.10       30
## net_incomeeuro_2000_3000                                   0.18      0.10     0.02     0.34 1.10       30
## net_incomeeuro_gt_3000                                     0.03      0.17    -0.23     0.31 1.07       56
## net_incomedont_tell                                        0.32      0.15     0.09     0.58 1.11       34
## relationship_duration_factorPartnered_upto12months      -341.97    243.66  -846.77    31.44 2.77        5
## relationship_duration_factorPartnered_upto28months      -342.09    243.66  -846.88    31.42 2.77        5
## relationship_duration_factorPartnered_upto52months      -342.11    243.66  -846.88    31.37 2.77        5
## relationship_duration_factorPartnered_morethan52months  -342.14    243.67  -846.87    31.33 2.77        5
## education_years                                           -0.00      0.01    -0.01     0.00 1.03       71
## bfi_extra                                                  0.06      0.03     0.02     0.11 1.16       19
## bfi_neuro                                                 -0.04      0.03    -0.10     0.01 1.05       42
## bfi_agree                                                  0.05      0.04    -0.01     0.12 1.11       38
## bfi_consc                                                 -0.09      0.03    -0.15    -0.03 1.15       23
## bfi_open                                                   0.11      0.03     0.05     0.17 1.14       27
## religiosity                                               -0.01      0.02    -0.03     0.02 1.02       72
## contraception_hormonalyes:congruent_contraception1         0.04      0.07    -0.09     0.15 1.14       20
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## congruent_contraception1                                     NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                             130
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## contraception_hormonalyes:congruent_contraception1           NA
## 
## Family Specific Parameters: 
##       Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.53      0.01     0.51     0.55 1.16       21       NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
## Picking joint bandwidth of 0.00878
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.06      0.06           0.620 Undecided        -0.134    0.0475 fixed   conditio~
## 2 b_congruent_co~   0.9    -0.06      0.06           0.791 Undecided        -0.0741   0.111  fixed   conditio~
## 3 b_contraceptio~   0.9    -0.06      0.06           0.536 Undecided        -0.0829   0.156  fixed   conditio~

Plots

conditional_effects(m_congruency_libido_controlled, ask = FALSE)

Forest Plot for Effect Sizes

m_congruency_libido_controlled %>%
  
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Sexual Frequency (Penetrative Intercourse)

Model

m_congruency_sexfreqpen_controlled = brm(diary_sex_active_sex_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal * congruent_contraception +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_congruency_sexfreqpen_controlled_robust")

Summary

summary(m_congruency_sexfreqpen_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_sex_active_sex_sum ~ offset(log(number_of_days)) + contraception_hormonal * congruent_contraception + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 576) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                -84.42    261.19  -531.41   229.72 2.47        5
## contraception_hormonalyes                                  0.10      0.05     0.02     0.18 1.08       58
## congruent_contraception1                                  -0.04      0.04    -0.10     0.02 1.12       22
## age                                                       -0.00      0.00    -0.01     0.00 1.09       30
## net_incomeeuro_500_1000                                    0.13      0.03     0.07     0.19 1.09       35
## net_incomeeuro_1000_2000                                   0.12      0.05     0.04     0.21 1.07       33
## net_incomeeuro_2000_3000                                   0.43      0.06     0.34     0.54 1.05       42
## net_incomeeuro_gt_3000                                     0.05      0.10    -0.11     0.21 1.08       44
## net_incomedont_tell                                        0.41      0.09     0.25     0.54 1.12       29
## relationship_duration_factorPartnered_upto12months        82.59    261.21  -231.73   529.39 2.47        5
## relationship_duration_factorPartnered_upto28months        82.45    261.21  -231.84   529.20 2.47        5
## relationship_duration_factorPartnered_upto52months        82.20    261.21  -232.12   529.01 2.47        5
## relationship_duration_factorPartnered_morethan52months    82.20    261.21  -232.08   529.00 2.47        5
## education_years                                           -0.01      0.00    -0.02    -0.01 1.08       74
## bfi_extra                                                  0.00      0.02    -0.03     0.03 1.08       52
## bfi_neuro                                                 -0.01      0.02    -0.04     0.03 1.02       82
## bfi_agree                                                  0.09      0.02     0.05     0.13 1.04       64
## bfi_consc                                                 -0.01      0.02    -0.05     0.02 1.07       44
## bfi_open                                                   0.04      0.02     0.00     0.08 1.07       54
## religiosity                                               -0.01      0.01    -0.03     0.00 1.09       55
## contraception_hormonalyes:congruent_contraception1         0.04      0.06    -0.05     0.14 1.10       49
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## congruent_contraception1                                     NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## contraception_hormonalyes:congruent_contraception1           NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_sexfreqpen_controlled, range = c(-0.05, 0.05),
                      ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.79). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.00639
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_sexfreqpen_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.79). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter          CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence HDI_low HDI_high Effects Component
##   <chr>           <dbl>    <dbl>     <dbl>           <dbl> <chr>              <dbl>    <dbl> <chr>   <chr>    
## 1 b_contraceptio~   0.9    -0.05      0.05           0.102 Undecided         0.0194   0.179  fixed   conditio~
## 2 b_congruent_co~   0.9    -0.05      0.05           0.558 Undecided        -0.103    0.0173 fixed   conditio~
## 3 b_contraceptio~   0.9    -0.05      0.05           0.605 Undecided        -0.0543   0.128  fixed   conditio~

Plots

conditional_effects(m_congruency_sexfreqpen_controlled,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_congruency_sexfreqpen_controlled %>%

  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Masturbation Frequency

Model

m_congruency_masfreq_controlled = brm(diary_masturbation_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal * congruent_contraception +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_congruency_masfreq_controlled_robust")

Summary

summary(m_congruency_masfreq_controlled, conf.int = T, prob = 0.90)
## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be careful when analysing the results!
## We recommend running more iterations and/or setting stronger priors.
##  Family: poisson 
##   Links: mu = log 
## Formula: diary_masturbation_sum ~ offset(log(number_of_days)) + contraception_hormonal * congruent_contraception + age + net_income + relationship_duration_factor + education_years + bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open + religiosity 
##    Data: data (Number of observations: 576) 
##   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup draws = 4000
## 
## Population-Level Effects: 
##                                                        Estimate Est.Error l-90% CI u-90% CI Rhat Bulk_ESS
## Intercept                                                293.71    229.91   -68.01   653.35 3.26        4
## contraception_hormonalyes                                 -0.43      0.06    -0.54    -0.34 1.24       12
## congruent_contraception1                                   0.03      0.05    -0.05     0.10 1.18       17
## age                                                       -0.01      0.00    -0.02    -0.00 1.13       29
## net_incomeeuro_500_1000                                    0.18      0.04     0.13     0.25 1.13       22
## net_incomeeuro_1000_2000                                   0.23      0.06     0.14     0.33 1.14       37
## net_incomeeuro_2000_3000                                  -0.01      0.08    -0.15     0.13 1.13       35
## net_incomeeuro_gt_3000                                    -0.11      0.16    -0.39     0.13 1.16       22
## net_incomedont_tell                                       -0.17      0.13    -0.40     0.06 1.12       25
## relationship_duration_factorPartnered_upto12months      -295.30    229.88  -655.14    66.12 3.26        4
## relationship_duration_factorPartnered_upto28months      -295.26    229.88  -655.06    66.24 3.26        4
## relationship_duration_factorPartnered_upto52months      -295.36    229.88  -655.19    66.12 3.26        4
## relationship_duration_factorPartnered_morethan52months  -295.50    229.88  -655.36    65.93 3.26        4
## education_years                                            0.00      0.00    -0.00     0.01 1.09       43
## bfi_extra                                                 -0.03      0.02    -0.07     0.00 1.09       41
## bfi_neuro                                                 -0.01      0.02    -0.06     0.03 1.15       19
## bfi_agree                                                 -0.00      0.03    -0.06     0.04 1.18       19
## bfi_consc                                                 -0.23      0.02    -0.27    -0.19 1.34       10
## bfi_open                                                   0.22      0.03     0.17     0.26 1.19       18
## religiosity                                               -0.04      0.01    -0.06    -0.02 1.11       29
## contraception_hormonalyes:congruent_contraception1         0.12      0.07    -0.00     0.25 1.27       12
##                                                        Tail_ESS
## Intercept                                                    NA
## contraception_hormonalyes                                    NA
## congruent_contraception1                                     NA
## age                                                          NA
## net_incomeeuro_500_1000                                      NA
## net_incomeeuro_1000_2000                                     NA
## net_incomeeuro_2000_3000                                     NA
## net_incomeeuro_gt_3000                                       NA
## net_incomedont_tell                                          NA
## relationship_duration_factorPartnered_upto12months           NA
## relationship_duration_factorPartnered_upto28months           NA
## relationship_duration_factorPartnered_upto52months           NA
## relationship_duration_factorPartnered_morethan52months       NA
## education_years                                              NA
## bfi_extra                                                    NA
## bfi_neuro                                                    NA
## bfi_agree                                                    NA
## bfi_consc                                                    NA
## bfi_open                                                     NA
## religiosity                                                  NA
## contraception_hormonalyes:congruent_contraception1           NA
## 
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Comparison with ROPE

plot(equivalence_test(m_congruency_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.76). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## Picking joint bandwidth of 0.00785
## Warning: Removed 1197 rows containing non-finite values (stat_density_ridges).

equivalence_test(m_congruency_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
## Possible multicollinearity between b_contraception_hormonalyes:congruent_contraception1 and b_contraception_hormonalyes (r = 0.76). This might lead to inappropriate results. See 'Details' in '?equivalence_test'.
## # A tibble: 3 x 10
##   Parameter         CI ROPE_low ROPE_high ROPE_Percentage ROPE_Equivalence  HDI_low HDI_high Effects Component
##   <chr>          <dbl>    <dbl>     <dbl>           <dbl> <chr>               <dbl>    <dbl> <chr>   <chr>    
## 1 b_contracepti~   0.9    -0.05      0.05           0     Rejected         -0.522    -0.333  fixed   conditio~
## 2 b_congruent_c~   0.9    -0.05      0.05           0.671 Undecided        -0.0431    0.0992 fixed   conditio~
## 3 b_contracepti~   0.9    -0.05      0.05           0.116 Undecided         0.00524   0.251  fixed   conditio~

Plots

conditional_effects(m_congruency_masfreq_controlled,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))

Forest Plot for Effect Sizes

m_congruency_masfreq_controlled %>%

  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")

Model Comparison

Attractiveness of partner

compare_models = loo(m_hc_atrr, m_congruency_atrr)

LooIC first model: 1563.68

LooIC second model: 1564.32

Model Comparisons: The difference between models is -0.32 compared to a standard error of 1.99

compare_models
## Output of model 'm_hc_atrr':
## 
## Computed from 4000 by 710 log-likelihood matrix
## 
##          Estimate   SE
## elpd_loo   -781.8 20.5
## p_loo         3.1  0.3
## looic      1563.7 40.9
## ------
## Monte Carlo SE of elpd_loo is 0.0.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## Output of model 'm_congruency_atrr':
## 
## Computed from 4000 by 710 log-likelihood matrix
## 
##          Estimate   SE
## elpd_loo   -782.2 20.7
## p_loo         5.2  0.4
## looic      1564.3 41.3
## ------
## Monte Carlo SE of elpd_loo is 0.0.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## Model comparisons:
##                   elpd_diff se_diff
## m_hc_atrr          0.0       0.0   
## m_congruency_atrr -0.3       2.0

Relationship satisfaction

compare_models = loo(m_hc_relsat, m_congruency_relsat)

LooIC first model: 795.66

LooIC second model: 792.98

Model Comparisons: The difference between models is -1.34 compared to a standard error of 2.63

compare_models
## Output of model 'm_hc_relsat':
## 
## Computed from 4000 by 710 log-likelihood matrix
## 
##          Estimate   SE
## elpd_loo   -397.8 25.0
## p_loo         3.7  0.5
## looic       795.7 50.1
## ------
## Monte Carlo SE of elpd_loo is 0.0.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## Output of model 'm_congruency_relsat':
## 
## Computed from 4000 by 710 log-likelihood matrix
## 
##          Estimate   SE
## elpd_loo   -396.5 24.7
## p_loo         5.5  0.6
## looic       793.0 49.3
## ------
## Monte Carlo SE of elpd_loo is 0.0.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## Model comparisons:
##                     elpd_diff se_diff
## m_congruency_relsat  0.0       0.0   
## m_hc_relsat         -1.3       2.6

Sexual Satisfaction

m_hc_sexsat$data$satisfaction_sexual_intercourse =
  as.numeric(m_hc_sexsat$data$satisfaction_sexual_intercourse)
m_congruency_sexsat$data$satisfaction_sexual_intercourse =
  as.numeric(m_congruency_sexsat$data$satisfaction_sexual_intercourse)

compare_models = loo(m_hc_sexsat, m_congruency_sexsat)

LooIC first model: 2070.32

LooIC second model: 2072.21

Model Comparisons: The difference between models is -0.94 compared to a standard error of 1.57

compare_models
## Output of model 'm_hc_sexsat':
## 
## Computed from 4000 by 710 log-likelihood matrix
## 
##          Estimate   SE
## elpd_loo  -1035.2 19.9
## p_loo         3.2  0.3
## looic      2070.3 39.8
## ------
## Monte Carlo SE of elpd_loo is 0.0.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## Output of model 'm_congruency_sexsat':
## 
## Computed from 4000 by 710 log-likelihood matrix
## 
##          Estimate   SE
## elpd_loo  -1036.1 20.0
## p_loo         5.1  0.4
## looic      2072.2 39.9
## ------
## Monte Carlo SE of elpd_loo is 0.0.
## 
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
## 
## Model comparisons:
##                     elpd_diff se_diff
## m_hc_sexsat          0.0       0.0   
## m_congruency_sexsat -0.9       1.6
---
title: "Robustness Analyses Effects of Contraception"
output:
  html_document:
    toc: true
    toc_depth: 4
    toc_float: true
    code_folding: 'hide'
    self_contained: false
editor_options: 
  chunk_output_type: console
---
  
  
## Data 
```{r results='hide',message=F,warning=F}
source("0_helpers.R")

load("data/cleaned_selected_wrangled.rdata")

data = data %>%
  filter(!(contraception_method %contains% "hormonal_other"))
```


## Effects of Hormonal Contraceptives {.tabset}
### Attractiveness of Partner {.tabset}
#### Model
```{r}
m_hc_atrr = brm(attractiveness_partner ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_atrr_robust")
```

#### Summary
```{r}
summary(m_hc_atrr, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_atrr, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_atrr %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)
```

### Relationship Satisfaction {.tabset}
#### Model
```{r}
m_hc_relsat = brm(relationship_satisfaction ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_relsat_robust")
```

#### Summary
```{r}
summary(m_hc_relsat, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_relsat, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_relsat %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)
```


### Sexual Satisfaction {.tabset}
#### Model
```{r}
m_hc_sexsat = brm(satisfaction_sexual_intercourse ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_sexsat_robust")
```

#### Summary
```{r}
summary(m_hc_sexsat, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_sexsat, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_sexsat %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)
```

### Libido {.tabset}

#### Model
```{r}
m_hc_libido = brm(diary_libido_mean ~ contraception_hormonal,
                data = data, family = gaussian(),
                file = "m_hc_libido_robust")
```

#### Summary
```{r}
summary(m_hc_libido, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_libido, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_libido %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)
```

### Sexual Frequency (Penetrative Intercourse) {.tabset}
#### Model
```{r}
m_hc_sexfreqpen = brm(diary_sex_active_sex_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal,
                data = data, family = poisson(),
                file = "m_hc_sexfreqpen_robust")
```

#### Summary
```{r}
summary(m_hc_sexfreqpen, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_sexfreqpen, range = c(-0.05, 0.05)), ci = 0.90,
                      parameters = "contraception")
equivalence_test(m_hc_sexfreqpen, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_sexfreqpen,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_sexfreqpen %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Masturbation Frequency {.tabset}
#### Model
```{r}
m_hc_masfreq = brm(diary_masturbation_sum ~ offset(log(number_of_days)) +
                     contraception_hormonal,
                data = data, family = poisson(),
                file = "m_hc_masfreq_robust")
```

#### Summary
```{r}
summary(m_hc_masfreq, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_masfreq,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_masfreq %>%
  spread_draws(b_contraception_hormonalyes) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", NA),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", NA)) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

## Controlled Models: Effects of Hormonal Contraceptives {.tabset}
### Attractiveness of Partner {.tabset}
#### Model
```{r}
m_hc_atrr_controlled = brm(attractiveness_partner ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_atrr_controlled_robust")
```

#### Summary
```{r}
summary(m_hc_atrr_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_atrr, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_atrr_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Relationship Satisfaction {.tabset}
#### Model
```{r}
m_hc_relsat_controlled = brm(relationship_satisfaction ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_relsat_controlled_robust")
```

#### Summary
```{r}
summary(m_hc_relsat_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))

equivalence_test(m_hc_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_relsat_controlled, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_relsat_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```


### Sexual Satisfaction {.tabset}
#### Model
```{r}
m_hc_sexsat_controlled = brm(satisfaction_sexual_intercourse ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_sexsat_controlled_robust")
```

#### Summary
```{r}
summary(m_hc_sexsat_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_sexsat_controlled, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_sexsat_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Libido {.tabset}

#### Model
```{r}
m_hc_libido_controlled = brm(diary_libido_mean ~ contraception_hormonal +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_hc_libido_controlled_robust")
```

#### Summary
```{r}
summary(m_hc_libido_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_libido_controlled, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_libido_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Sexual Frequency (Penetrative Intercourse) {.tabset}
#### Model
```{r}
m_hc_sexfreqpen_controlled = brm(diary_sex_active_sex_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_hc_sexfreqpen_controlled_robust")
```

#### Summary
```{r}
summary(m_hc_sexfreqpen_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_sexfreqpen_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_sexfreqpen_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_sexfreqpen_controlled,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_sexfreqpen_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Masturabation Frequency {.tabset}
#### Model
```{r}
m_hc_masfreq_controlled = brm(diary_masturbation_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_hc_masfreq_controlled_robust")
```

#### Summary
```{r}
summary(m_hc_masfreq_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_hc_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_hc_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_hc_masfreq_controlled,
                    effects = "contraception_hormonal",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_hc_masfreq_controlled %>%
  spread_draws(b_contraception_hormonalyes,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Contraception", group),
         condition = ifelse(condition %contains% "b_contraception_hormonalyes",
                        "Hormonal Contraception", condition),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception", "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

## Effects of (In)Congruenct HC Use {.tabset}
### Attractiveness of Partner  {.tabset}
#### Model
```{r}
m_congruency_atrr = brm(attractiveness_partner ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_atrr_robust")
```

#### Summary
```{r}
summary(m_congruency_atrr, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_atrr, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_atrr, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_atrr %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)


  
```

### Relationship Satisfaction {.tabset}
#### Model
```{r}
m_congruency_relsat = brm(relationship_satisfaction ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_relsat_robust")
```

#### Summary
```{r}
summary(m_congruency_relsat, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_relsat, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_relsat, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_relsat %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)

```

### Sexual Satisfaction {.tabset}
#### Model
```{r}
m_congruency_sexsat = brm(satisfaction_sexual_intercourse ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_sexsat_robust")
```

#### Summary
```{r}
summary(m_congruency_sexsat, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_sexsat, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_sexsat, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_sexsat %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)
```

### Libido {.tabset}
#### Model
```{r}
m_congruency_libido = brm(diary_libido_mean ~
                          contraception_hormonal * congruent_contraception,
                data = data, family = gaussian(),
                file = "m_congruency_libido_robust")

```

#### Summary
```{r}
summary(m_congruency_libido, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_libido, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_libido, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_libido %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors") +
  xlim (-0.6, 0.6)
```


### Sexual Frequency (Penetrative Intercourse) {.tabset}
#### Model
```{r}
m_congruency_sexfreqpen = brm(diary_sex_active_sex_sum ~ offset(log(number_of_days)) +
                     contraception_hormonal * congruent_contraception,
                data = data, family = poisson(),
                file = "m_congruency_sexfreqpen_robust")
```

#### Summary
```{r}
summary(m_congruency_sexfreqpen, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_sexfreqpen, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_sexfreqpen, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_sexfreqpen,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_sexfreqpen %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.051), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Masturbation Frequency {.tabset}
#### Model
```{r}
m_congruency_masfreq = brm(diary_masturbation_sum ~ offset(log(number_of_days)) +
                             contraception_hormonal * congruent_contraception,
                data = data, family = poisson(),
                file = "m_congruency_masfreq_robust")
```

#### Summary
```{r}
summary(m_congruency_masfreq, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_masfreq, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_masfreq,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_masfreq %>%
spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", NA),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                            "Congruent Contraception",
                                            "Interaction Hormonal Contracpetion and Congruent Contraception")))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```



## Controlled Models: Effects of (In)Congruenct HC Use {.tabset}
### Attractiveness of Partner  {.tabset}
#### Model
```{r}
m_congruency_atrr_controlled = brm(attractiveness_partner ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_atrr_controlled_robust")
```

#### Summary
```{r}
summary(m_congruency_atrr_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_atrr_controlled, range = c(-0.07, 0.07), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_atrr_controlled, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_atrr_controlled %>%
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.07))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.07, 0.07), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Relationship Satisfaction {.tabset}
#### Model
```{r}
m_congruency_relsat_controlled = brm(relationship_satisfaction ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_relsat_controlled_robust")
```

#### Summary
```{r}
summary(m_congruency_relsat_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_relsat_controlled, range = c(-0.04, 0.04), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_relsat_controlled, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_relsat_controlled %>%
  
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.04))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.04, 0.04), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Sexual Satisfaction {.tabset}
#### Model
```{r}
m_congruency_sexsat_controlled = brm(satisfaction_sexual_intercourse ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_sexsat_controlled_robust")
```

#### Summary
```{r}
summary(m_congruency_sexsat_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_sexsat_controlled, range = c(-0.11, 0.11), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_sexsat_controlled, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_sexsat_controlled %>%
  
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.11))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.11, 0.11), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Libido {.tabset}
#### Model
```{r}
m_congruency_libido_controlled = brm(diary_libido_mean ~
                          contraception_hormonal * congruent_contraception +
                             age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = gaussian(),
                file = "m_congruency_libido_controlled_robust")

```

#### Summary
```{r}
summary(m_congruency_libido_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_libido_controlled, range = c(-0.06, 0.06), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_libido_controlled, ask = FALSE)
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_libido_controlled %>%
  
  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.06))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.06, 0.06), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```


### Sexual Frequency (Penetrative Intercourse) {.tabset}
#### Model
```{r}
m_congruency_sexfreqpen_controlled = brm(diary_sex_active_sex_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal * congruent_contraception +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_congruency_sexfreqpen_controlled_robust")
```

#### Summary
```{r}
summary(m_congruency_sexfreqpen_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_sexfreqpen_controlled, range = c(-0.05, 0.05),
                      ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_sexfreqpen_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_sexfreqpen_controlled,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_sexfreqpen_controlled %>%

  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Masturbation Frequency {.tabset}
#### Model
```{r}
m_congruency_masfreq_controlled = brm(diary_masturbation_sum ~
                        offset(log(number_of_days)) +
                        contraception_hormonal * congruent_contraception +
                        age + net_income + relationship_duration_factor +
                              education_years +
                              bfi_extra + bfi_neuro + bfi_agree + bfi_consc + bfi_open +
                              religiosity,
                data = data, family = poisson(),
                file = "m_congruency_masfreq_controlled_robust")
```

#### Summary
```{r}
summary(m_congruency_masfreq_controlled, conf.int = T, prob = 0.90)
```

#### Comparison with ROPE
```{r}
plot(equivalence_test(m_congruency_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception"))
equivalence_test(m_congruency_masfreq_controlled, range = c(-0.05, 0.05), ci = 0.90,
                      parameters = "contraception")
```

#### Plots
```{r warning = FALSE}
conditional_effects(m_congruency_masfreq_controlled,
                    effects = "contraception_hormonal:congruent_contraception",
                    conditions = data.frame(number_of_days = 1))
```

#### Forest Plot for Effect Sizes {.active}
```{r}
m_congruency_masfreq_controlled %>%

  spread_draws(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
               b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity) %>%
  pivot_longer(cols = c(b_contraception_hormonalyes, b_congruent_contraception1,
               `b_contraception_hormonalyes:congruent_contraception1`,
                        b_age,
               b_net_incomeeuro_500_1000, b_net_incomeeuro_1000_2000, 
               b_net_incomeeuro_2000_3000, b_net_incomeeuro_gt_3000, b_net_incomedont_tell,
               b_relationship_duration_factorPartnered_upto28months,
               b_relationship_duration_factorPartnered_upto52months,
               b_relationship_duration_factorPartnered_morethan52months,
               b_education_years,
               b_bfi_extra, b_bfi_neuro, b_bfi_agree, b_bfi_consc, b_bfi_open, 
               b_religiosity),
               names_to = "condition",
               values_to = "r_condition") %>%
  mutate(condition_mean = r_condition,
         group = ifelse(condition %contains% "b_relationship_duration_factor",
                        "Relationship Duration",
                        ifelse(condition %contains% "b_net_income",
                               "Income",
                               NA)),
         group = ifelse(condition %contains% "ontraception",
                        "Contraception", group),
         condition = ifelse(condition == "b_contraception_hormonalyes",
                        "Hormonal Contraception",
                     ifelse(condition == "b_congruent_contraception1",
                            "Congruent Contraception",
                     ifelse(condition == "b_contraception_hormonalyes:congruent_contraception1",
                            "Interaction Hormonal Contracpetion and Congruent Contraception",
                            condition))),
         condition = ifelse(condition == "b_age", "Age",
                ifelse(condition == "b_net_incomeeuro_500_1000", "500-1000 Euro",
                ifelse(condition == "b_net_incomeeuro_1000_2000", "1000-2000 Euro",
                ifelse(condition == "b_net_incomeeuro_2000_3000", "2000-3000 Euro",
                ifelse(condition == "b_net_incomeeuro_gt_3000", ">3000 Euro",
                ifelse(condition == "b_net_incomedont_tell", "do not tell",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto28months",
                       "13-28 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_upto52months",
                       "29-52 months",
                ifelse(condition == "b_relationship_duration_factorPartnered_morethan52months",
                       ">52 months",
                ifelse(condition == "b_education_years", "Years of Education",
                ifelse(condition == "b_bfi_extra", "Extraversion",
                ifelse(condition == "b_bfi_neuro", "Neuroticism",
                ifelse(condition == "b_bfi_agree", "Agreeableness",
                ifelse(condition == "b_bfi_consc", "Conscientiousness",
                ifelse(condition == "b_bfi_open", "Openness",
                ifelse(condition == "b_religiosity", "Religiosity",
                       condition)))))))))))))))),
         group = ifelse(is.na(group), condition, group),
         condition = factor(condition, levels = rev(c("Hormonal Contraception",
                                        "Congruent Contraception",
                                        "Interaction Hormonal Contracpetion and Congruent Contraception",
                                        "Age",
                                        "500-1000 Euro", "1000-2000 Euro",
                                        "2000-3000 Euro", ">3000 Euro", "do not tell",
                                        "13-28 months", "29-52 months",
                                        ">52 months",
                                        "Years of Education",
                                        "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))),
         group = factor(group, levels = c("Contraception", "Age", "Income",
                                          "Relationship Duration","Years of Education",
                                          "Extraversion", "Neuroticism", "Agreeableness",
                                         "Conscientiousness","Openness","Religiosity"))) %>%
  ggplot(aes(y = condition,
             x = condition_mean,
             fill = stat(abs(x) < 0.05))) +
  stat_halfeye() +
  geom_vline(xintercept = c(-0.05, 0.05), linetype = "dotted") +
  apatheme +
  theme(legend.position = "none") +
  scale_fill_manual(values = c("gray80", "skyblue")) +
  labs(x = "Effect Size Estimates", y = "Predictors")
```

### Model Comparison
#### Attractiveness of partner
```{r}
compare_models = loo(m_hc_atrr, m_congruency_atrr)
```
LooIC first model: `r round(compare_models$loos$m_hc_atrr$estimates[3,1], 2)`

LooIC second model: `r round(compare_models$loos$m_congruency_atrr$estimates[3,1], 2)`

Model Comparisons: The difference between models is `r round(compare_models$diffs[2,1], 2)` compared to a standard error of `r round(compare_models$diffs[2,2], 2)`
```{r}
compare_models
```

#### Relationship satisfaction
```{r}
compare_models = loo(m_hc_relsat, m_congruency_relsat)
```
LooIC first model: `r round(compare_models$loos$m_hc_relsat$estimates[3,1], 2)`

LooIC second model: `r round(compare_models$loos$m_congruency_relsat$estimates[3,1], 2)`

Model Comparisons: The difference between models is `r round(compare_models$diffs[2,1], 2)` compared to a standard error of `r round(compare_models$diffs[2,2], 2)`
```{r}
compare_models
```

#### Sexual Satisfaction
```{r}
m_hc_sexsat$data$satisfaction_sexual_intercourse =
  as.numeric(m_hc_sexsat$data$satisfaction_sexual_intercourse)
m_congruency_sexsat$data$satisfaction_sexual_intercourse =
  as.numeric(m_congruency_sexsat$data$satisfaction_sexual_intercourse)

compare_models = loo(m_hc_sexsat, m_congruency_sexsat)
```
LooIC first model: `r round(compare_models$loos$m_hc_sexsat$estimates[3,1], 2)`

LooIC second model: `r round(compare_models$loos$m_congruency_sexsat$estimates[3,1], 2)`

Model Comparisons: The difference between models is `r round(compare_models$diffs[2,1], 2)` compared to a standard error of `r round(compare_models$diffs[2,2], 2)`
```{r}
compare_models
```


