Main Analyses

Library

library(formr)
library(effects)

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library(sjstats)

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library(lmerTest)

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library(dplyr)

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library(ggplot2)
library(RColorBrewer)
library(tidyverse)

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library(mgcv)         

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library(stringr)     
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library(lmtest) 

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library(minqa)

Data

Load selected data based on 03_codebook

data_included_documented = read.csv(file = "data_included_documented.csv")[,-1]

Political, Ethnic, and Religious Similarity

H1a Preference for Similarity in Political Beliefs

H1a(1) There is no linear link between right-wing political orientation and women’s preferences for partner’s similar political beliefs and values. H1a(2) There is a positive quadratic link between right-wing political orientation and women’s preferences for partner’s similar political beliefs and values. Outcome: Preference ratings for partner’s similar political beliefs and values. Predictor: Political Orientation. Random intercept and random slope for country.

H1a(1) Linear Effect

Models

model_pref_politicalsim_lin = lmer(pref_politicalsim ~ political_orientation +
                       (1+political_orientation|country),
                               data = data_included_documented, 
                       control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_pref_politicalsim_lin)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_politicalsim ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 52555.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4262 -0.6742  0.1455  0.7343  2.1944 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr 
##  country  (Intercept)           0.7454   0.8634        
##           political_orientation 0.0329   0.1814   -0.84
##  Residual                       3.3402   1.8276        
## Number of obs: 12949, groups:  country, 144
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            3.40380    0.12195 69.80926  27.910   <2e-16 ***
## political_orientation -0.08334    0.03177 47.56677  -2.624   0.0117 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.838

confint(model_pref_politicalsim_lin, level = 0.997)

## Computing profile confidence intervals ...

##                           0.15 %     99.85 %
## .sig01                 0.5752608  1.24915249
## .sig02                -0.9536264 -0.49107070
## .sig03                 0.1024774  0.29138678
## .sigma                 1.7942564  1.86205701
## (Intercept)            3.0209153  3.76952829
## political_orientation -0.1784924  0.02029572

Standardized Coefficients

standardize_parameters(model_pref_politicalsim_lin, method = "refit", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                   -0.0173 0.997 -0.126 0.0912 
## 2 political_orientation         -0.0593 0.997 -0.126 0.00779

Plot

lmer(pref_politicalsim ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

H1a(2) Quadratic Effect

Here, we are examining the quadratic effect of right-wing political orientation on preferred political similarity in a partner using the Two Lines Approach (Simonsohn, 2018). We are using the Robin Hood Algorithm in order to set the breaking point. Then, we are calculating two multilevel regressions on either side of the breaking point. Outcome: Preference ratings for partner’s similar political beliefs and values. Predictors: Political Orientation & Age. Random intercept and random slope for country.

Algorithm: Find breaking point

See 11_twolines_analyses_multilevel

Regression 1 (x <= breaking_point)

model_pref_politicalsim_1 = lmer(pref_politicalsim ~ political_orientation +
                                 (1+political_orientation|country),
                               data = data_included_documented %>%
                                 dplyr::filter(political_orientation <= 3), control =lmerControl(optimizer = "bobyqa"))

summary(model_pref_politicalsim_1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_politicalsim ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented %>% dplyr::filter(political_orientation <=  
##     3)
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 42671.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5773 -0.7032  0.1458  0.7426  2.2334 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr 
##  country  (Intercept)           0.65527  0.8095        
##           political_orientation 0.05036  0.2244   -0.75
##  Residual                       3.18024  1.7833        
## Number of obs: 10636, groups:  country, 138
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            3.82477    0.12460 45.48408   30.70  < 2e-16 ***
## political_orientation -0.31340    0.04212 30.17206   -7.44 2.63e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.815

confint(model_pref_politicalsim_1, level = 0.997)

## Computing profile confidence intervals ...

##                            0.15 %    99.85 %
## .sig01                 0.48282467  1.2567244
## .sig02                -0.92918790 -0.1896777
## .sig03                 0.09638487  0.4008507
## .sigma                 1.74741488  1.8206102
## (Intercept)            3.39772953  4.2064773
## political_orientation -0.44132932 -0.1574817

standardize_parameters(model_pref_politicalsim_1, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                     0     0.997  0       0    
## 2 political_orientation          -0.170 0.997 -0.237  -0.102

plot(allEffects(model_pref_politicalsim_1))

Regression 2 (x >= breaking_point)

model_pref_politicalsim_2 = lmer(pref_politicalsim ~ political_orientation +
                                 (1+political_orientation|country),
                               data = data_included_documented %>%
                                 dplyr::filter(political_orientation >= 3), control =lmerControl(optimizer = "bobyqa"))

summary(model_pref_politicalsim_2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_politicalsim ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented %>% dplyr::filter(political_orientation >=  
##     3)
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 29821.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8134 -0.7937  0.1168  0.6892  2.1899 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr 
##  country  (Intercept)           0.7483   0.8650        
##           political_orientation 0.0314   0.1772   -0.81
##  Residual                       3.3136   1.8203        
## Number of obs: 7357, groups:  country, 129
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            1.62534    0.17798 25.66396   9.132 1.53e-09 ***
## political_orientation  0.40821    0.04567 22.90045   8.939 6.31e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.911

confint(model_pref_politicalsim_2, level = 0.997)

## Computing profile confidence intervals ...

##                            0.15 %   99.85 %
## .sig01                 0.32893449 1.6393578
## .sig02                -0.97195114 0.4328139
## .sig03                 0.04045946 0.3781595
## .sigma                 1.77636620 1.8662207
## (Intercept)            1.08787002 2.3016652
## political_orientation  0.23113547 0.5549943

standardize_parameters(model_pref_politicalsim_2, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                     0     0.997  0       0    
## 2 political_orientation           0.175 0.997  0.117   0.233

plot(allEffects(model_pref_politicalsim_2))

H1b Preference for Similarity in Ethnicity/Race

H1b There is a positive linear link between right-wing political orientation and women’s preferences for partner’s similar ethnicity/race. Outcome: Preference ratings for partner’s similar ethnicity/race. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_pref_ethnicalsim <- lmer(pref_ethnicalsim ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_pref_ethnicalsim)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_ethnicalsim ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 30431.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2873 -0.4577 -0.0320  0.5952  2.4045 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr 
##  country  (Intercept)           0.083581 0.28910       
##           political_orientation 0.006438 0.08024  -0.40
##  Residual                       1.955963 1.39856       
## Number of obs: 8643, groups:  country, 127
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            2.93636    0.06571 24.05651  44.689  < 2e-16 ***
## political_orientation  0.14098    0.02047 25.38692   6.889 2.95e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.728

confint(model_pref_ethnicalsim, level = 0.997)

## Computing profile confidence intervals ...

##                            0.15 %   99.85 %
## .sig01                 0.11069563 0.5700297
## .sig02                -0.87971422 0.7945738
## .sig03                 0.03014798 0.1653320
## .sigma                 1.36741251 1.4309525
## (Intercept)            2.73858107 3.1632222
## political_orientation  0.07038154 0.2025605

Standardized Coefficients

standardize_parameters(model_pref_ethnicalsim, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                     0     0.997 0        0    
## 2 political_orientation           0.128 0.997 0.0731   0.184

Plot

lmer(pref_ethnicalsim ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

H1c Preference for Similarity in Religion

H1c There is a positive linear link between right-wing political orientation and women’s preferences for partner’s similar religious beliefs.
Outcome: Preference ratings for partner’s similar religious beliefs. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_pref_religioussim <- lmer(pref_religioussim ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_pref_religioussim)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_religioussim ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 56523.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.1900 -1.0116  0.0650  0.8719  1.9319 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr 
##  country  (Intercept)           0.389003 0.62370       
##           political_orientation 0.004839 0.06956  -0.29
##  Residual                       4.571227 2.13804       
## Number of obs: 12937, groups:  country, 144
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            2.95120    0.09640 31.33217  30.614  < 2e-16 ***
## political_orientation  0.17408    0.02132 23.38888   8.166 2.66e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.575

confint(model_pref_religioussim, level = 0.997)

## Computing profile confidence intervals ...

##                             0.15 %   99.85 %
## .sig01                 0.309523959 1.0217923
## .sig02                -0.907182380 0.9735600
## .sig03                 0.004622921 0.1587600
## .sigma                 2.099015572 2.1783470
## (Intercept)            2.637243786 3.3410490
## political_orientation  0.073780282 0.2632057

Standardized Coefficients

standardize_parameters(model_pref_religioussim, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                     0     0.997 0        0    
## 2 political_orientation           0.108 0.997 0.0686   0.147

Plot

lmer(pref_religioussim ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

Ideal Partner Preferences

H2a Preference for the Level of Financial Security- Successfulness

H2a There is a positive linear link between right-wing political orientation and women’s preferences for the level of financial security and successfulness. Outcome: Level ratings for partner’s financial security-successfulness. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_pref_level_financially_secure_successful_ambitious <-
  lmer(
    pref_level_financially_secure_successful_ambitious ~ political_orientation + (1 +
                                                                                    political_orientation |
                                                                                    country),
    data = data_included_documented,
    control = lmerControl(optimizer = "bobyqa")
  )

## boundary (singular) fit: see help('isSingular')

Summary

summary(model_pref_level_financially_secure_successful_ambitious)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_level_financially_secure_successful_ambitious ~ political_orientation +  
##     (1 + political_orientation | country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 31715.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8198 -0.7049  0.0118  0.7074  2.5653 
## 
## Random effects:
##  Groups   Name                  Variance  Std.Dev. Corr 
##  country  (Intercept)           0.1340500 0.36613       
##           political_orientation 0.0009219 0.03036  -1.00
##  Residual                       0.7234663 0.85057       
## Number of obs: 12551, groups:  country, 143
## 
## Fixed effects:
##                        Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)            4.360909   0.047409 75.507204   91.99   <2e-16 ***
## political_orientation  0.069690   0.006831 99.425822   10.20   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.771
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

confint(model_pref_level_financially_secure_successful_ambitious, level = 0.997)

## Computing profile confidence intervals ...

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## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig01

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig02

## Warning in zeta(shiftpar, start = opt[seqpar1][-w]): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig03

## Warning in zeta(shiftpar, start = opt[seqpar1][-w]): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in zetafun(np, ns): NAs detected in profiling

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sigma

## Warning in min(diff(obj1[, 2]) < (-non.mono.tol), na.rm = TRUE): kein
## nicht-fehlendes Argument für min; gebe Inf zurück

## Warning in confint.thpr(pp, level = level, zeta = zeta): non-monotonic profile
## for .sig01

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig02: falling back to linear interpolation

## Warning in min(diff(obj1[, 2]) < (-non.mono.tol), na.rm = TRUE): kein
## nicht-fehlendes Argument für min; gebe Inf zurück

## Warning in confint.thpr(pp, level = level, zeta = zeta): non-monotonic profile
## for .sig03

## Warning in min(diff(obj1[, 2]) < (-non.mono.tol), na.rm = TRUE): kein
## nicht-fehlendes Argument für min; gebe Inf zurück

## Warning in confint.thpr(pp, level = level, zeta = zeta): non-monotonic profile
## for .sigma

##                            0.15 %    99.85 %
## .sig01                         NA         NA
## .sig02                -1.00639456 1.00000000
## .sig03                         NA         NA
## .sigma                         NA         NA
## (Intercept)            4.21955176 4.51794888
## political_orientation  0.04395714 0.09167436

Standardized Coefficients

standardize_parameters(model_pref_level_financially_secure_successful_ambitious, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                     0     0.997 0        0    
## 2 political_orientation           0.105 0.997 0.0747   0.136

Plot

lmer(pref_level_financially_secure_successful_ambitious ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

## boundary (singular) fit: see help('isSingular')

H2b Preference for the Level of Confidence-Assertiveness

H2b There is a positive linear link between right-wing political orientation and women’s preferences for the level of confidence and assertiveness.
Outcome: Level ratings for partner’s confidence-assertiveness. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_pref_level_confident_assertive <- lmer(pref_level_confident_assertive ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_pref_level_confident_assertive)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pref_level_confident_assertive ~ political_orientation + (1 +  
##     political_orientation | country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 28928.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.4786 -0.6798 -0.0354  0.6526  3.0002 
## 
## Random effects:
##  Groups   Name                  Variance  Std.Dev. Corr 
##  country  (Intercept)           0.1518032 0.38962       
##           political_orientation 0.0007535 0.02745  -0.52
##  Residual                       0.5614583 0.74931       
## Number of obs: 12697, groups:  country, 144
## 
## Fixed effects:
##                        Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)           4.432e+00  4.713e-02 1.022e+02  94.044  < 2e-16 ***
## political_orientation 2.946e-02  7.797e-03 2.185e+01   3.778  0.00104 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.544

confint(model_pref_level_confident_assertive, level = 0.997)

## Computing profile confidence intervals ...

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
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## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig02

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig02: falling back to linear interpolation

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## Reduktion auf einmalige 'x' Werte

##                             0.15 %    99.85 %
## .sig01                 0.288700584 0.52594291
## .sig02                -0.967689483 0.23965114
## .sig03                 0.003602748 0.05946370
## .sigma                 0.735494367 0.76355617
## (Intercept)            4.291019618 4.57603976
## political_orientation  0.003430270 0.05344236

Standardized Coefficients

standardize_parameters(model_pref_level_confident_assertive, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                    0      0.997 0       0     
## 2 political_orientation          0.0476 0.997 0.0102  0.0850

Plot

lmer(pref_level_confident_assertive ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

H2c Preference for the Level of Education-Intelligence

H2c There is no link between right-wing political orientation and women’s preferences for the level of education and intelligence.
Outcome: Level ratings for partner’s education-intelligence. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_pref_level_intelligence_educated <- lmer(pref_level_intelligence_educated ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_pref_level_intelligence_educated)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pref_level_intelligence_educated ~ political_orientation + (1 +  
##     political_orientation | country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 30133.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.2463 -0.6840  0.0410  0.6903  2.1702 
## 
## Random effects:
##  Groups   Name                  Variance  Std.Dev. Corr 
##  country  (Intercept)           0.0627259 0.25045       
##           political_orientation 0.0009513 0.03084  -0.22
##  Residual                       0.6171382 0.78558       
## Number of obs: 12723, groups:  country, 141
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            4.80150    0.03776 71.33658 127.162   <2e-16 ***
## political_orientation  0.02120    0.00859 28.58180   2.468   0.0198 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.556

confint(model_pref_level_intelligence_educated, level = 0.997)

## Computing profile confidence intervals ...

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## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): Last two rows have
## identical or NA .zeta values: using minstep

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig02

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig02: falling back to linear interpolation

## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## Reduktion auf einmalige 'x' Werte

##                             0.15 %    99.85 %
## .sig01                 0.167528212 0.36538714
## .sig02                -0.796184160 0.59712860
## .sig03                 0.009011258 0.06387362
## .sigma                 0.771123896 0.80048216
## (Intercept)            4.687218265 4.92338841
## political_orientation -0.006088975 0.05130632

Standardized Coefficients

standardize_parameters(model_pref_level_intelligence_educated, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI   CI_low CI_high
##   <chr>                           <dbl> <dbl>    <dbl>   <dbl>
## 1 (Intercept)                    0      0.997  0        0     
## 2 political_orientation          0.0351 0.997 -0.00712  0.0774

Plot

lmer(pref_level_intelligence_educated ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00200785 (tol = 0.002, component 1)

H2d Preference for the Level of Kindness-Supportiveness

H2d There is no link between right-wing political orientation and women’s preferences for the level of kindness and supportiveness.
Outcome: Level ratings for partner’s kindness-supportiveness. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_pref_level_kind_supportive <- lmer(pref_level_kind_supportive ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_pref_level_kind_supportive)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_level_kind_supportive ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 26550.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.7907 -0.6652  0.0687  0.8000  1.7293 
## 
## Random effects:
##  Groups   Name                  Variance  Std.Dev. Corr
##  country  (Intercept)           1.657e-02 0.128718     
##           political_orientation 5.642e-05 0.007511 0.56
##  Residual                       4.674e-01 0.683662     
## Number of obs: 12729, groups:  country, 143
## 
## Fixed effects:
##                        Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)            5.163681   0.022796 44.855531 226.521   <2e-16 ***
## political_orientation  0.011053   0.004833 14.568403   2.287   0.0376 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.426

confint(model_pref_level_kind_supportive, level = 0.997)

## Computing profile confidence intervals ...

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig01

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig02

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig01: falling back to linear interpolation

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig02: falling back to linear interpolation

##                             0.15 %    99.85 %
## .sig01                 0.085731705 0.20670347
## .sig02                -1.000000000 1.00000000
## .sig03                 0.000000000 0.03294017
## .sigma                 0.671097562 0.69661278
## (Intercept)            5.095444447 5.23771219
## political_orientation -0.005103504 0.02736140

Standardized Coefficients

standardize_parameters(model_pref_level_kind_supportive, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI   CI_low CI_high
##   <chr>                           <dbl> <dbl>    <dbl>   <dbl>
## 1 (Intercept)                    0      0.997  0        0     
## 2 political_orientation          0.0215 0.997 -0.00640  0.0494

Plot

lmer(pref_level_kind_supportive ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00451923 (tol = 0.002, component 1)

H2e Preference for the Level of Attractiveness

H2e There is no link between right-wing political orientation and women’s preferences for the level of attractiveness.
Outcome: Level ratings for partner’s attractiveness. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_pref_level_attractiveness <- lmer(pref_level_attractiveness ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_pref_level_attractiveness)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## pref_level_attractiveness ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 32918.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.6503 -0.6477 -0.0244  0.6476  2.5185 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr
##  country  (Intercept)           0.01093  0.10452      
##           political_orientation 0.00027  0.01643  0.28
##  Residual                       0.80567  0.89759      
## Number of obs: 12526, groups:  country, 142
## 
## Fixed effects:
##                        Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)            3.927775   0.025446 33.185806 154.356  < 2e-16 ***
## political_orientation  0.039238   0.007106 12.674681   5.522 0.000108 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.559

confint(model_pref_level_attractiveness, level = 0.997)

## Computing profile confidence intervals ...

##                            0.15 %    99.85 %
## .sig01                 0.06770729 0.18832187
## .sig02                -0.75850240 0.91589442
## .sig03                 0.00000000 0.05867958
## .sigma                 0.88097657 0.91472753
## (Intercept)            3.84943833 4.00914319
## political_orientation  0.01356438 0.06154413

Standardized Coefficients

standardize_parameters(model_pref_level_attractiveness, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                    0      0.997 0       0     
## 2 political_orientation          0.0583 0.997 0.0270  0.0897

Plot

lmer(pref_level_attractiveness ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.002634 (tol = 0.002, component 1)

Ideal Age and Height

H3a(1) Importance Ratings for Partner’s Age

H3a(1) There is a positive linear link between right-wing political orientation and women’s importance ratings for partner’s age. Outcome: Importance ratings for partner’s age. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_imp_age <- lmer(imp_age ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_imp_age)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: imp_age ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 46141.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0525 -0.5057  0.1715  0.6392  2.1583 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr 
##  country  (Intercept)           0.08563  0.2926        
##           political_orientation 0.01025  0.1012   -0.76
##  Residual                       1.95847  1.3995        
## Number of obs: 13113, groups:  country, 143
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            3.50977    0.05990 21.71636  58.591  < 2e-16 ***
## political_orientation  0.08199    0.02058 15.91106   3.985  0.00108 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.832

confint(model_imp_age, level= 0.997)

## Computing profile confidence intervals ...

##                             0.15 %   99.85 %
## .sig01                 0.108667718 0.5631709
## .sig02                -0.960382731 0.7688915
## .sig03                 0.009474955 0.2058263
## .sigma                 1.374118745 1.4257678
## (Intercept)            3.320129690 3.7190380
## political_orientation  0.013398755 0.1510101

Standardized Coefficients

standardize_parameters(model_imp_age, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                    0      0.997 0        0    
## 2 political_orientation          0.0787 0.997 0.0201   0.137

Plot

lmer(imp_age ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

H3a(2) Level Ratings for Partner’s Age

H3a(2) There is a positive linear link between right-wing political orientation and the relative age discrepancy between ideal partner’s age and women’s age. (discrepancy calculated as ideal partner’s age – women’s age) Outcome: Discrepancy between level ratings for ideal partner’s age and women’s age (ideal_age_rel) Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_ideal_age_rel <- lmer(ideal_age_rel ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

Summary

summary(model_ideal_age_rel)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ideal_age_rel ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 61688.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -11.6404  -0.4618  -0.1175   0.4395  25.7025 
## 
## Random effects:
##  Groups   Name                  Variance Std.Dev. Corr 
##  country  (Intercept)           0.8873   0.9420        
##           political_orientation 0.4705   0.6859   -0.94
##  Residual                       9.5961   3.0978        
## Number of obs: 12057, groups:  country, 140
## 
## Fixed effects:
##                       Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)            2.39574    0.14881 14.23277  16.099 1.56e-10 ***
## political_orientation  0.15044    0.08541 35.89473   1.761   0.0867 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.871

confint(model_ideal_age_rel, level = 0.997)

## Computing profile confidence intervals ...

##                           0.15 %     99.85 %
## .sig01                 0.2473102 1.931245124
## .sig02                -0.9925466 0.009045051
## .sig03                 0.4001037 1.029585987
## .sigma                 3.0391523 3.158354644
## (Intercept)            1.8588095 2.891544423
## political_orientation -0.1136682 0.420746805

Standardized Coefficients

standardize_parameters(model_ideal_age_rel, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI  CI_low CI_high
##   <chr>                           <dbl> <dbl>   <dbl>   <dbl>
## 1 (Intercept)                    0      0.997  0        0    
## 2 political_orientation          0.0652 0.997 -0.0447   0.175

Plot

lmer(ideal_age_rel ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

## boundary (singular) fit: see help('isSingular')

## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+03

H3b(1) Importance Ratings for Partner’s Height

H3b(1) There is a positive linear link between right-wing political orientation and women’s importance ratings for partner’s height. Outcome: Importance ratings for partner’s height. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_imp_height <- lmer(imp_height ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

## boundary (singular) fit: see help('isSingular')

Summary

summary(model_imp_height)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: imp_height ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 46915.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2633 -0.5505  0.1130  0.7243  2.0751 
## 
## Random effects:
##  Groups   Name                  Variance  Std.Dev. Corr 
##  country  (Intercept)           8.972e-02 0.29953       
##           political_orientation 8.986e-05 0.00948  -1.00
##  Residual                       2.130e+00 1.45961       
## Number of obs: 13026, groups:  country, 143
## 
## Fixed effects:
##                        Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)           3.779e+00  5.174e-02 5.157e+01   73.03   <2e-16 ***
## political_orientation 1.185e-01  9.667e-03 5.975e+02   12.26   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.597
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

confint(model_imp_height, level = 0.997)

## Computing profile confidence intervals ...

## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep
## Warning in nextpar(mat, cc, i, delta, lowcut, upcut): unexpected decrease in
## profile: using minstep

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig01

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig02

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig01: falling back to linear interpolation

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig02: falling back to linear interpolation

##                            0.15 %    99.85 %
## .sig01                 0.18912373 0.39522206
## .sig02                -1.00000000 1.00000000
## .sig03                 0.00000000 0.07675487
## .sigma                 1.43715819 1.48624262
## (Intercept)            3.62468680 3.95869154
## political_orientation  0.08311549 0.15306504

Standardized Coefficients

standardize_parameters(model_imp_height, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI CI_low CI_high
##   <chr>                           <dbl> <dbl>  <dbl>   <dbl>
## 1 (Intercept)                     0     0.997 0        0    
## 2 political_orientation           0.108 0.997 0.0821   0.135

Plot

lmer(imp_height ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

## boundary (singular) fit: see help('isSingular')

H3b(2) Level Ratings for Partner’s Height

H3b(2) There is a positive linear link between right-wing political orientation and ideal partner’s height. Outcome: Level ratings for ideal partner’s height. Predictor: Political Orientation. Random intercept and random slope for country.

Models

model_ideal_height <- lmer(ideal_height ~ political_orientation + (1+political_orientation|country), data = data_included_documented, control =lmerControl(optimizer = "bobyqa"))

## boundary (singular) fit: see help('isSingular')

Summary

summary(model_ideal_height)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ideal_height ~ political_orientation + (1 + political_orientation |  
##     country)
##    Data: data_included_documented
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 14686.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.0913 -0.1224 -0.0640 -0.0069  2.5128 
## 
## Random effects:
##  Groups   Name                  Variance  Std.Dev. Corr
##  country  (Intercept)           1.611e-03 0.040138     
##           political_orientation 1.072e-05 0.003274 1.00
##  Residual                       1.882e-01 0.433773     
## Number of obs: 12524, groups:  country, 142
## 
## Fixed effects:
##                        Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)           1.029e+00  1.091e-02 3.506e+01  94.284   <2e-16 ***
## political_orientation 3.100e-03  2.942e-03 3.668e+02   1.054    0.293    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## pltcl_rnttn -0.556
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

confint(model_ideal_height, level = 0.997)

## Computing profile confidence intervals ...

## Warning in FUN(X[[i]], ...): non-monotonic profile for .sig02

## Warning in confint.thpr(pp, level = level, zeta = zeta): bad spline fit for
## .sig02: falling back to linear interpolation

##                             0.15 %    99.85 %
## .sig01                 0.010943539 0.08395169
## .sig02                -1.000000000 1.00000000
## .sig03                 0.000000000 0.01532100
## .sigma                 0.429230881 0.43981211
## (Intercept)            0.996323686 1.06969808
## political_orientation -0.007896281 0.01258552

Standardized Coefficients

standardize_parameters(model_ideal_height, method = "basic", ci = 0.997)

## # A tibble: 2 × 5
##   Parameter             Std_Coefficient    CI  CI_low CI_high
##   <chr>                           <dbl> <dbl>   <dbl>   <dbl>
## 1 (Intercept)                   0       0.997  0       0     
## 2 political_orientation         0.00966 0.997 -0.0176  0.0369

Plot

lmer(ideal_height ~ political_orientation + (1+political_orientation|country),
       data = data_included_documented) %>%
    allEffects() %>%
    plot()

## boundary (singular) fit: see help('isSingular')