Power motivations and sexual risk-taking

Show the code

/* APA Style */

.table>thead>tr>th {
 border-color: black;
}


/** Remove borders within the table body **/
.table>tbody>tr>td {
 border: none;
}


/** Add a top border to the table header row **/
.table thead tr:first-child {
 border-top: 2px solid black;
}


/** Add a bottom border to the table body **/
.table tbody tr:last-child {
 border-bottom: 2px solid black;
}


/** Make the table header row a normal weight; not bold **/
.table th {
 font-weight: normal;
}


/** Make the caption italic and black **/
.table caption {
 font-style: italic;
 color: black;
}

Show the code

library(papaja)
library(xtable)
library(tidyr)
library(purrr)
library(ggplot2)
library(brms)
library(bayestestR)
library(rstan)
library(readxl)
library(sjPlot)
library(cmdstanr)
library(plotly)
library(corrplot)
library(htmlwidgets)
library(bayestestR)
library(formatR)
library(kableExtra)
library(tidybayes)
library(blavaan)
library(rmarkdown)
library(tidySEM)
library(ggcorrplot)
library(ggprism)
library(htmlTable)
library(table1)
library(data.table)
library(semPlot)
library(correlation)
library(dplyr)
library(lavaan)
library(dplyr)
library(tibble)
library(stringi)
library(tidyr)
library(kableExtra)
library(rrtable)
library(sjPlot)
library(purrr)
library(stringi)
library(ggplot2)
library(tidyverse)
setwd("/Users/andrew/Documents/1_UoE/Research/PhD/Experiments/DoPL/Experiments/Experiment_2_Study_Past_Sexual_Experiences")
Experiment_4_DF_Final <- read.csv("./Analysis/Experiment_4_DF_Final.csv")
source("./Analysis/Question_index.R")
Experiment_4_DF_Final$Gender <- as.factor(Experiment_4_DF_Final$Gender)
load("Experiment_4_Analysis.RData")

Results

Gender

Show the code

Experiment_4_DF_Final$Gender <- as.factor(Experiment_4_DF_Final$Gender)
Experiment_4_Analysis_DF <- Experiment_4_DF_Final[!grepl(5, Experiment_4_DF_Final$Gender), ]
ggplot(Experiment_4_Analysis_DF, aes(x = Gender, fill = Gender)) +
  geom_histogram(stat = "count") +
  labs(x = "Gender") +
 scale_x_discrete(labels = c("Male", "Female"), guide = "prism_offset") +
  scale_y_continuous(breaks = seq(0, 100, 10), guide = "prism_offset") +
  theme_apa()

Analysis Priors

Show the code

m1_prior <- c(
# SRTB Risk
prior(normal(0, 1), coef = "Age", resp = "SRTBRiskz"), 
prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBRiskz"), 
prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBRiskz"), 
prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBRiskz"), 
prior(normal(.5, .2), coef = "Gender2", resp = "SRTBRiskz"), 
prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBRiskz"), 
prior(normal(0, 1), class = "Intercept", resp = "SRTBRiskz"), 
prior(normal(0, 1), class = "sigma", resp = "SRTBRiskz"), 

# SRTB Benefit
prior(normal(0, 1), coef = "Age", resp = "SRTBBenefitz"), 
prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBBenefitz"), 
prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBBenefitz"), 
prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBBenefitz"), 
prior(normal(.5, .2), coef = "Gender2", resp = "SRTBBenefitz"), 
prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBBenefitz"), 
prior(normal(0, 1), class = "Intercept", resp = "SRTBBenefitz"), 
prior(normal(0, 1), class = "sigma", resp = "SRTBBenefitz"), 

# SRTB Frequency
prior(normal(0, 1), coef = "Age", resp = "SRTBFrequencyz"), 
prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBFrequencyz"), 
prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBFrequencyz"), 
prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBFrequencyz"), 
prior(normal(.5, .2), coef = "Gender2", resp = "SRTBFrequencyz"), 
prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBFrequencyz"), 
prior(normal(0, 1), class = "Intercept", resp = "SRTBFrequencyz"), 
prior(normal(0, 1), class = "sigma", resp = "SRTBFrequencyz"), 

# SRTB Likelihood
prior(normal(0, 1), coef = "Age", resp = "SRTBLikelihoodz"), 
prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBLikelihoodz"), 
prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBLikelihoodz"), 
prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBLikelihoodz"), 
prior(normal(.5, .2), coef = "Gender2", resp = "SRTBLikelihoodz"), 
prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBLikelihoodz"), 
prior(normal(0, 1), class = "Intercept", resp = "SRTBLikelihoodz"), 
prior(normal(0, 1), class = "sigma", resp = "SRTBLikelihoodz")
)

m1_interaction_prior <- c(
 # SRTB Risk
 prior(normal(0, 1), coef = "Age", resp = "SRTBRiskz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBRiskz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBRiskz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBRiskz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z:Gender2", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Leadership_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Prestige_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "Gender2:B_PNI_z", resp = "SRTBRiskz"),

 # SRTB Benefit
 prior(normal(0, 1), coef = "Age", resp = "SRTBBenefitz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBBenefitz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBBenefitz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBBenefitz"),
prior(normal(.5, .02), coef = "DoPL_Dominance_z:Gender2", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Leadership_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Prestige_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "Gender2:B_PNI_z", resp = "SRTBBenefitz"),
 # SRTB Frequency
 prior(normal(0, 1), coef = "Age", resp = "SRTBFrequencyz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBFrequencyz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBFrequencyz"),
prior(normal(.5, .02), coef = "DoPL_Dominance_z:Gender2", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Leadership_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Prestige_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "Gender2:B_PNI_z", resp = "SRTBFrequencyz"),
 # SRTB Likelihood
 prior(normal(0, 1), coef = "Age", resp = "SRTBLikelihoodz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBLikelihoodz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "B_PNI_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBLikelihoodz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z:Gender2", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Leadership_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "Gender2:DoPL_Prestige_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "Gender2:B_PNI_z", resp = "SRTBLikelihoodz")
)

m2_prior <- c(
 # SRTB Risk
 prior(normal(0, 1), coef = "Age", resp = "SRTBRiskz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBRiskz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "PNI_Vulnerability_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), coef = "PNI_Grandiosity_z", resp = "SRTBRiskz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBRiskz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBRiskz"),

 # SRTB Benefit
 prior(normal(0, 1), coef = "Age", resp = "SRTBBenefitz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBBenefitz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "PNI_Vulnerability_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), coef = "PNI_Grandiosity_z", resp = "SRTBBenefitz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBBenefitz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBBenefitz"),

 # SRTB Frequency
 prior(normal(0, 1), coef = "Age", resp = "SRTBFrequencyz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBFrequencyz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), coef = "PNI_Vulnerability_z", resp = "SRTBFrequencyz"),
  prior(normal(0, 1), coef = "PNI_Grandiosity_z", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBFrequencyz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBFrequencyz"),

 # SRTB Likelihood
 prior(normal(0, 1), coef = "Age", resp = "SRTBLikelihoodz"),
 prior(normal(.5, .02), coef = "DoPL_Dominance_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "DoPL_Leadership_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "DoPL_Prestige_z", resp = "SRTBLikelihoodz"),
 prior(normal(.5, .2), coef = "Gender2", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "PNI_Vulnerability_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), coef = "PNI_Grandiosity_z", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), class = "Intercept", resp = "SRTBLikelihoodz"),
 prior(normal(0, 1), class = "sigma", resp = "SRTBLikelihoodz")
)

Models

Multi model with dopl and pni as predictor variables

Show the code

Experiment_4_Analysis_DF <- Experiment_4_DF_Final[!grepl(5,Experiment_4_DF_Final$Gender), ]
m1 <- brm(mvbind(SRTB_Likelihood_z, SRTB_Risk_z, SRTB_Benefit_z, SRTB_Frequency_z) ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + B_PNI_z + Age + Gender,
 data = Experiment_4_Analysis_DF,
 prior = m1_prior,
 iter = 10000,
 warmup = 1000,
 chains = 4,
 cores = parallel::detectCores(),
 save_pars = save_pars(all = TRUE), 
 backend =  "cmdstanr"
)

Summary of m1

Show the code

summary(m1)
##  Family: MV(gaussian, gaussian, gaussian, gaussian) 
##   Links: mu = identity; sigma = identity
##          mu = identity; sigma = identity
##          mu = identity; sigma = identity
##          mu = identity; sigma = identity 
## Formula: SRTB_Likelihood_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + B_PNI_z + Age + Gender 
##          SRTB_Risk_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + B_PNI_z + Age + Gender 
##          SRTB_Benefit_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + B_PNI_z + Age + Gender 
##          SRTB_Frequency_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + B_PNI_z + Age + Gender 
##    Data: Experiment_4_Analysis_DF (Number of observations: 194) 
##   Draws: 4 chains, each with iter = 10000; warmup = 1000; thin = 1;
##          total post-warmup draws = 36000
## 
## Population-Level Effects: 
##                                   Estimate Est.Error l-95% CI u-95% CI Rhat
## SRTBLikelihoodz_Intercept            -0.00      0.31    -0.61     0.60 1.00
## SRTBRiskz_Intercept                   0.02      0.27    -0.50     0.54 1.00
## SRTBBenefitz_Intercept                0.01      0.30    -0.59     0.59 1.00
## SRTBFrequencyz_Intercept             -0.14      0.31    -0.75     0.46 1.00
## SRTBLikelihoodz_DoPL_Dominance_z      0.49      0.02     0.45     0.52 1.00
## SRTBLikelihoodz_DoPL_Prestige_z      -0.07      0.11    -0.28     0.14 1.00
## SRTBLikelihoodz_DoPL_Leadership_z    -0.06      0.10    -0.26     0.14 1.00
## SRTBLikelihoodz_B_PNI_z              -0.34      0.10    -0.53    -0.15 1.00
## SRTBLikelihoodz_Age                  -0.00      0.01    -0.02     0.02 1.00
## SRTBLikelihoodz_Gender2               0.08      0.13    -0.16     0.33 1.00
## SRTBRiskz_DoPL_Dominance_z            0.47      0.02     0.43     0.51 1.00
## SRTBRiskz_DoPL_Prestige_z             0.20      0.09     0.01     0.38 1.00
## SRTBRiskz_DoPL_Leadership_z          -0.29      0.09    -0.46    -0.12 1.00
## SRTBRiskz_B_PNI_z                    -0.17      0.08    -0.33    -0.00 1.00
## SRTBRiskz_Age                        -0.01      0.01    -0.02     0.01 1.00
## SRTBRiskz_Gender2                     0.39      0.12     0.15     0.62 1.00
## SRTBBenefitz_DoPL_Dominance_z         0.48      0.02     0.44     0.52 1.00
## SRTBBenefitz_DoPL_Prestige_z          0.07      0.11    -0.14     0.28 1.00
## SRTBBenefitz_DoPL_Leadership_z       -0.26      0.10    -0.46    -0.07 1.00
## SRTBBenefitz_B_PNI_z                 -0.22      0.10    -0.41    -0.03 1.00
## SRTBBenefitz_Age                      0.00      0.01    -0.02     0.02 1.00
## SRTBBenefitz_Gender2                 -0.13      0.12    -0.37     0.11 1.00
## SRTBFrequencyz_DoPL_Dominance_z       0.49      0.02     0.45     0.53 1.00
## SRTBFrequencyz_DoPL_Prestige_z        0.08      0.11    -0.13     0.30 1.00
## SRTBFrequencyz_DoPL_Leadership_z     -0.17      0.10    -0.37     0.03 1.00
## SRTBFrequencyz_B_PNI_z               -0.33      0.10    -0.52    -0.14 1.00
## SRTBFrequencyz_Age                    0.00      0.01    -0.02     0.02 1.00
## SRTBFrequencyz_Gender2                0.20      0.12    -0.04     0.45 1.00
##                                   Bulk_ESS Tail_ESS
## SRTBLikelihoodz_Intercept            42331    29462
## SRTBRiskz_Intercept                  66606    28566
## SRTBBenefitz_Intercept               42848    32135
## SRTBFrequencyz_Intercept             43427    30280
## SRTBLikelihoodz_DoPL_Dominance_z     73786    27192
## SRTBLikelihoodz_DoPL_Prestige_z      32533    30989
## SRTBLikelihoodz_DoPL_Leadership_z    34029    30925
## SRTBLikelihoodz_B_PNI_z              38906    31802
## SRTBLikelihoodz_Age                  43242    29594
## SRTBLikelihoodz_Gender2              51666    30080
## SRTBRiskz_DoPL_Dominance_z           73922    25928
## SRTBRiskz_DoPL_Prestige_z            47084    30173
## SRTBRiskz_DoPL_Leadership_z          51221    28736
## SRTBRiskz_B_PNI_z                    53730    29357
## SRTBRiskz_Age                        68173    27448
## SRTBRiskz_Gender2                    63875    27057
## SRTBBenefitz_DoPL_Dominance_z        75645    25691
## SRTBBenefitz_DoPL_Prestige_z         29299    29542
## SRTBBenefitz_DoPL_Leadership_z       32190    30266
## SRTBBenefitz_B_PNI_z                 36779    31256
## SRTBBenefitz_Age                     43227    32075
## SRTBBenefitz_Gender2                 49978    30259
## SRTBFrequencyz_DoPL_Dominance_z      76104    25847
## SRTBFrequencyz_DoPL_Prestige_z       31512    29722
## SRTBFrequencyz_DoPL_Leadership_z     34837    29294
## SRTBFrequencyz_B_PNI_z               38559    31293
## SRTBFrequencyz_Age                   43778    31217
## SRTBFrequencyz_Gender2               51699    29651
## 
## Family Specific Parameters: 
##                       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_SRTBLikelihoodz     1.15      0.06     1.04     1.28 1.00    39575
## sigma_SRTBRiskz           0.99      0.05     0.90     1.10 1.00    64274
## sigma_SRTBBenefitz        1.13      0.06     1.01     1.25 1.00    41526
## sigma_SRTBFrequencyz      1.15      0.06     1.04     1.28 1.00    44299
##                       Tail_ESS
## sigma_SRTBLikelihoodz    30089
## sigma_SRTBRiskz          27450
## sigma_SRTBBenefitz       29966
## sigma_SRTBFrequencyz     28809
## 
## Residual Correlations: 
##                                        Estimate Est.Error l-95% CI u-95% CI
## rescor(SRTBLikelihoodz,SRTBRiskz)         -0.08      0.07    -0.22     0.07
## rescor(SRTBLikelihoodz,SRTBBenefitz)       0.55      0.05     0.45     0.65
## rescor(SRTBRiskz,SRTBBenefitz)            -0.27      0.07    -0.40    -0.13
## rescor(SRTBLikelihoodz,SRTBFrequencyz)     0.53      0.05     0.43     0.63
## rescor(SRTBRiskz,SRTBFrequencyz)          -0.11      0.07    -0.26     0.03
## rescor(SRTBBenefitz,SRTBFrequencyz)        0.56      0.05     0.45     0.65
##                                        Rhat Bulk_ESS Tail_ESS
## rescor(SRTBLikelihoodz,SRTBRiskz)      1.00    56789    29764
## rescor(SRTBLikelihoodz,SRTBBenefitz)   1.00    36579    29763
## rescor(SRTBRiskz,SRTBBenefitz)         1.00    51169    30480
## rescor(SRTBLikelihoodz,SRTBFrequencyz) 1.00    39399    30072
## rescor(SRTBRiskz,SRTBFrequencyz)       1.00    52156    30155
## rescor(SRTBBenefitz,SRTBFrequencyz)    1.00    43394    31123
## 
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Multi model with dopl and pni as predictor variables and interaction terms

Show the code

Experiment_4_Analysis_DF <- Experiment_4_DF_Final[!grepl(5, Experiment_4_DF_Final$Gender), ]
m1_interaction <- brm(mvbind(SRTB_Likelihood_z, SRTB_Risk_z, SRTB_Benefit_z, SRTB_Frequency_z) ~ DoPL_Dominance_z * Gender + DoPL_Prestige_z * Gender  + DoPL_Leadership_z * Gender  + B_PNI_z * Gender  + Age,
 data = Experiment_4_Analysis_DF,
 prior = m1_interaction_prior,
 iter = 10000,
 warmup = 1000,
 chains = 4,
 cores = parallel::detectCores(),
 save_pars = save_pars(all = TRUE),
 backend = "cmdstanr"
)

Summary of the m1_interaction model

Show the code

summary(m1_interaction)
##  Family: MV(gaussian, gaussian, gaussian, gaussian) 
##   Links: mu = identity; sigma = identity
##          mu = identity; sigma = identity
##          mu = identity; sigma = identity
##          mu = identity; sigma = identity 
## Formula: SRTB_Likelihood_z ~ DoPL_Dominance_z * Gender + DoPL_Prestige_z * Gender + DoPL_Leadership_z * Gender + B_PNI_z * Gender + Age 
##          SRTB_Risk_z ~ DoPL_Dominance_z * Gender + DoPL_Prestige_z * Gender + DoPL_Leadership_z * Gender + B_PNI_z * Gender + Age 
##          SRTB_Benefit_z ~ DoPL_Dominance_z * Gender + DoPL_Prestige_z * Gender + DoPL_Leadership_z * Gender + B_PNI_z * Gender + Age 
##          SRTB_Frequency_z ~ DoPL_Dominance_z * Gender + DoPL_Prestige_z * Gender + DoPL_Leadership_z * Gender + B_PNI_z * Gender + Age 
##    Data: Experiment_4_Analysis_DF (Number of observations: 194) 
##   Draws: 4 chains, each with iter = 10000; warmup = 1000; thin = 1;
##          total post-warmup draws = 36000
## 
## Population-Level Effects: 
##                                           Estimate Est.Error l-95% CI u-95% CI
## SRTBLikelihoodz_Intercept                    -0.15      0.34    -0.83     0.51
## SRTBRiskz_Intercept                          -0.11      0.29    -0.69     0.46
## SRTBBenefitz_Intercept                       -0.16      0.34    -0.83     0.49
## SRTBFrequencyz_Intercept                     -0.26      0.34    -0.93     0.41
## SRTBLikelihoodz_DoPL_Dominance_z              0.49      0.02     0.45     0.53
## SRTBLikelihoodz_Gender2                       0.15      0.13    -0.10     0.40
## SRTBLikelihoodz_DoPL_Prestige_z              -0.12      0.17    -0.45     0.21
## SRTBLikelihoodz_DoPL_Leadership_z            -0.06      0.14    -0.34     0.21
## SRTBLikelihoodz_B_PNI_z                      -0.24      0.15    -0.53     0.05
## SRTBLikelihoodz_Age                           0.00      0.01    -0.02     0.02
## SRTBLikelihoodz_DoPL_Dominance_z:Gender2      0.49      0.02     0.46     0.53
## SRTBLikelihoodz_Gender2:DoPL_Prestige_z       0.17      0.23    -0.29     0.62
## SRTBLikelihoodz_Gender2:DoPL_Leadership_z    -0.10      0.23    -0.54     0.35
## SRTBLikelihoodz_Gender2:B_PNI_z              -0.42      0.21    -0.83    -0.01
## SRTBRiskz_DoPL_Dominance_z                    0.48      0.02     0.44     0.51
## SRTBRiskz_Gender2                             0.47      0.13     0.22     0.72
## SRTBRiskz_DoPL_Prestige_z                     0.21      0.15    -0.08     0.50
## SRTBRiskz_DoPL_Leadership_z                  -0.32      0.12    -0.56    -0.07
## SRTBRiskz_B_PNI_z                            -0.23      0.13    -0.48     0.02
## SRTBRiskz_Age                                -0.01      0.01    -0.02     0.01
## SRTBRiskz_DoPL_Dominance_z:Gender2            0.48      0.02     0.44     0.52
## SRTBRiskz_Gender2:DoPL_Prestige_z             0.04      0.20    -0.36     0.44
## SRTBRiskz_Gender2:DoPL_Leadership_z          -0.05      0.20    -0.43     0.34
## SRTBRiskz_Gender2:B_PNI_z                    -0.10      0.18    -0.45     0.26
## SRTBBenefitz_DoPL_Dominance_z                 0.48      0.02     0.44     0.52
## SRTBBenefitz_Gender2                         -0.05      0.13    -0.30     0.20
## SRTBBenefitz_DoPL_Prestige_z                  0.13      0.17    -0.19     0.45
## SRTBBenefitz_DoPL_Leadership_z               -0.29      0.14    -0.57    -0.02
## SRTBBenefitz_B_PNI_z                         -0.23      0.15    -0.51     0.05
## SRTBBenefitz_Age                              0.01      0.01    -0.02     0.03
## SRTBBenefitz_DoPL_Dominance_z:Gender2         0.49      0.02     0.45     0.53
## SRTBBenefitz_Gender2:DoPL_Prestige_z         -0.05      0.23    -0.50     0.39
## SRTBBenefitz_Gender2:DoPL_Leadership_z       -0.08      0.22    -0.51     0.35
## SRTBBenefitz_Gender2:B_PNI_z                 -0.18      0.21    -0.58     0.22
## SRTBFrequencyz_DoPL_Dominance_z               0.49      0.02     0.45     0.53
## SRTBFrequencyz_Gender2                        0.26      0.13     0.02     0.52
## SRTBFrequencyz_DoPL_Prestige_z               -0.11      0.17    -0.44     0.22
## SRTBFrequencyz_DoPL_Leadership_z             -0.04      0.14    -0.32     0.23
## SRTBFrequencyz_B_PNI_z                       -0.25      0.15    -0.53     0.04
## SRTBFrequencyz_Age                            0.00      0.01    -0.02     0.02
## SRTBFrequencyz_DoPL_Dominance_z:Gender2       0.49      0.02     0.45     0.53
## SRTBFrequencyz_Gender2:DoPL_Prestige_z        0.44      0.23    -0.02     0.89
## SRTBFrequencyz_Gender2:DoPL_Leadership_z     -0.42      0.22    -0.86     0.02
## SRTBFrequencyz_Gender2:B_PNI_z               -0.37      0.21    -0.78     0.04
##                                           Rhat Bulk_ESS Tail_ESS
## SRTBLikelihoodz_Intercept                 1.00    37709    29342
## SRTBRiskz_Intercept                       1.00    56107    28235
## SRTBBenefitz_Intercept                    1.00    37708    29701
## SRTBFrequencyz_Intercept                  1.00    37182    29930
## SRTBLikelihoodz_DoPL_Dominance_z          1.00    62611    27179
## SRTBLikelihoodz_Gender2                   1.00    47570    31142
## SRTBLikelihoodz_DoPL_Prestige_z           1.00    21274    24852
## SRTBLikelihoodz_DoPL_Leadership_z         1.00    22856    25721
## SRTBLikelihoodz_B_PNI_z                   1.00    26324    27840
## SRTBLikelihoodz_Age                       1.00    37853    28660
## SRTBLikelihoodz_DoPL_Dominance_z:Gender2  1.00    60437    26294
## SRTBLikelihoodz_Gender2:DoPL_Prestige_z   1.00    21773    25634
## SRTBLikelihoodz_Gender2:DoPL_Leadership_z 1.00    25000    27763
## SRTBLikelihoodz_Gender2:B_PNI_z           1.00    25580    26992
## SRTBRiskz_DoPL_Dominance_z                1.00    59666    28034
## SRTBRiskz_Gender2                         1.00    55711    26306
## SRTBRiskz_DoPL_Prestige_z                 1.00    32985    29978
## SRTBRiskz_DoPL_Leadership_z               1.00    35688    29033
## SRTBRiskz_B_PNI_z                         1.00    39899    28940
## SRTBRiskz_Age                             1.00    56314    27491
## SRTBRiskz_DoPL_Dominance_z:Gender2        1.00    57808    24253
## SRTBRiskz_Gender2:DoPL_Prestige_z         1.00    34042    28898
## SRTBRiskz_Gender2:DoPL_Leadership_z       1.00    39637    29335
## SRTBRiskz_Gender2:B_PNI_z                 1.00    38936    29638
## SRTBBenefitz_DoPL_Dominance_z             1.00    61478    26825
## SRTBBenefitz_Gender2                      1.00    46629    30189
## SRTBBenefitz_DoPL_Prestige_z              1.00    19932    25903
## SRTBBenefitz_DoPL_Leadership_z            1.00    22249    24308
## SRTBBenefitz_B_PNI_z                      1.00    26020    28545
## SRTBBenefitz_Age                          1.00    36984    28735
## SRTBBenefitz_DoPL_Dominance_z:Gender2     1.00    60705    27167
## SRTBBenefitz_Gender2:DoPL_Prestige_z      1.00    20429    26346
## SRTBBenefitz_Gender2:DoPL_Leadership_z    1.00    25119    27358
## SRTBBenefitz_Gender2:B_PNI_z              1.00    25514    26903
## SRTBFrequencyz_DoPL_Dominance_z           1.00    58251    26075
## SRTBFrequencyz_Gender2                    1.00    47203    29954
## SRTBFrequencyz_DoPL_Prestige_z            1.00    19928    25522
## SRTBFrequencyz_DoPL_Leadership_z          1.00    22778    27412
## SRTBFrequencyz_B_PNI_z                    1.00    26804    28205
## SRTBFrequencyz_Age                        1.00    37212    29265
## SRTBFrequencyz_DoPL_Dominance_z:Gender2   1.00    64418    26894
## SRTBFrequencyz_Gender2:DoPL_Prestige_z    1.00    20135    25548
## SRTBFrequencyz_Gender2:DoPL_Leadership_z  1.00    24676    27440
## SRTBFrequencyz_Gender2:B_PNI_z            1.00    25412    27420
## 
## Family Specific Parameters: 
##                       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_SRTBLikelihoodz     1.30      0.07     1.17     1.44 1.00    35837
## sigma_SRTBRiskz           1.10      0.06     0.99     1.22 1.00    51290
## sigma_SRTBBenefitz        1.28      0.07     1.16     1.43 1.00    34620
## sigma_SRTBFrequencyz      1.30      0.07     1.17     1.44 1.00    36713
##                       Tail_ESS
## sigma_SRTBLikelihoodz    29453
## sigma_SRTBRiskz          26771
## sigma_SRTBBenefitz       29040
## sigma_SRTBFrequencyz     29379
## 
## Residual Correlations: 
##                                        Estimate Est.Error l-95% CI u-95% CI
## rescor(SRTBLikelihoodz,SRTBRiskz)          0.13      0.07    -0.01     0.27
## rescor(SRTBLikelihoodz,SRTBBenefitz)       0.65      0.04     0.56     0.73
## rescor(SRTBRiskz,SRTBBenefitz)            -0.02      0.07    -0.16     0.13
## rescor(SRTBLikelihoodz,SRTBFrequencyz)     0.63      0.04     0.54     0.72
## rescor(SRTBRiskz,SRTBFrequencyz)           0.11      0.07    -0.03     0.26
## rescor(SRTBBenefitz,SRTBFrequencyz)        0.66      0.04     0.58     0.74
##                                        Rhat Bulk_ESS Tail_ESS
## rescor(SRTBLikelihoodz,SRTBRiskz)      1.00    52418    28988
## rescor(SRTBLikelihoodz,SRTBBenefitz)   1.00    32970    29447
## rescor(SRTBRiskz,SRTBBenefitz)         1.00    47624    30312
## rescor(SRTBLikelihoodz,SRTBFrequencyz) 1.00    32491    30092
## rescor(SRTBRiskz,SRTBFrequencyz)       1.00    47962    30164
## rescor(SRTBBenefitz,SRTBFrequencyz)    1.00    37477    29763
## 
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

m1 model comparison

loo comparison

Show the code

m1_comparison <- loo(m1, m1_interaction)

Show the code

m1_comparison

Output of model 'm1':

Computed from 36000 by 194 log-likelihood matrix

         Estimate   SE
elpd_loo  -1102.6 29.4
p_loo        36.1  4.6
looic      2205.2 58.8
------
Monte Carlo SE of elpd_loo is 0.1.

Pareto k diagnostic values:
                         Count Pct.    Min. n_eff
(-Inf, 0.5]   (good)     193   99.5%   2145      
 (0.5, 0.7]   (ok)         1    0.5%   741       
   (0.7, 1]   (bad)        0    0.0%   <NA>      
   (1, Inf)   (very bad)   0    0.0%   <NA>      

All Pareto k estimates are ok (k < 0.7).
See help('pareto-k-diagnostic') for details.

Output of model 'm1_interaction':

Computed from 36000 by 194 log-likelihood matrix

         Estimate   SE
elpd_loo  -1164.5 29.2
p_loo        47.3  5.5
looic      2328.9 58.4
------
Monte Carlo SE of elpd_loo is 0.1.

Pareto k diagnostic values:
                         Count Pct.    Min. n_eff
(-Inf, 0.5]   (good)     191   98.5%   4706      
 (0.5, 0.7]   (ok)         3    1.5%   580       
   (0.7, 1]   (bad)        0    0.0%   <NA>      
   (1, Inf)   (very bad)   0    0.0%   <NA>      

All Pareto k estimates are ok (k < 0.7).
See help('pareto-k-diagnostic') for details.

Model comparisons:
               elpd_diff se_diff
m1               0.0       0.0  
m1_interaction -61.8       6.2  

bayes factor comparison

Show the code

comparison <- bayesfactor_models(m1, m1_interaction)

Show the code

comparison

	Model	log_BF
m1		0.00000
m1_interaction		-69.01682

Show the code

m2 <- brm(mvbind(SRTB_Likelihood_z, SRTB_Risk_z, SRTB_Benefit_z, SRTB_Frequency_z) ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + PNI_Grandiosity_z + PNI_Vulnerability_z + Age + Gender,
 data = Experiment_4_Analysis_DF,
 prior = m2_prior,
 iter = 10000,
 warmup = 1000,
 chains = 4,
 cores = parallel::detectCores(),
 save_pars = save_pars(all = TRUE),
 backend = "cmdstanr"
)

Show the code

summary(m2)
##  Family: MV(gaussian, gaussian, gaussian, gaussian) 
##   Links: mu = identity; sigma = identity
##          mu = identity; sigma = identity
##          mu = identity; sigma = identity
##          mu = identity; sigma = identity 
## Formula: SRTB_Likelihood_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + PNI_Grandiosity_z + PNI_Vulnerability_z + Age + Gender 
##          SRTB_Risk_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + PNI_Grandiosity_z + PNI_Vulnerability_z + Age + Gender 
##          SRTB_Benefit_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + PNI_Grandiosity_z + PNI_Vulnerability_z + Age + Gender 
##          SRTB_Frequency_z ~ DoPL_Dominance_z + DoPL_Prestige_z + DoPL_Leadership_z + PNI_Grandiosity_z + PNI_Vulnerability_z + Age + Gender 
##    Data: Experiment_4_Analysis_DF (Number of observations: 194) 
##   Draws: 4 chains, each with iter = 10000; warmup = 1000; thin = 1;
##          total post-warmup draws = 36000
## 
## Population-Level Effects: 
##                                     Estimate Est.Error l-95% CI u-95% CI Rhat
## SRTBLikelihoodz_Intercept              -0.02      0.31    -0.62     0.58 1.00
## SRTBRiskz_Intercept                    -0.01      0.26    -0.53     0.51 1.00
## SRTBBenefitz_Intercept                  0.02      0.30    -0.58     0.60 1.00
## SRTBFrequencyz_Intercept               -0.16      0.31    -0.76     0.44 1.00
## SRTBLikelihoodz_DoPL_Dominance_z        0.49      0.02     0.45     0.52 1.00
## SRTBLikelihoodz_DoPL_Prestige_z        -0.05      0.11    -0.27     0.17 1.00
## SRTBLikelihoodz_DoPL_Leadership_z      -0.04      0.10    -0.24     0.16 1.00
## SRTBLikelihoodz_PNI_Grandiosity_z      -0.25      0.11    -0.47    -0.02 1.00
## SRTBLikelihoodz_PNI_Vulnerability_z    -0.17      0.10    -0.36     0.02 1.00
## SRTBLikelihoodz_Age                    -0.00      0.01    -0.02     0.02 1.00
## SRTBLikelihoodz_Gender2                 0.10      0.13    -0.15     0.35 1.00
## SRTBRiskz_DoPL_Dominance_z              0.47      0.02     0.43     0.51 1.00
## SRTBRiskz_DoPL_Prestige_z               0.23      0.10     0.05     0.42 1.00
## SRTBRiskz_DoPL_Leadership_z            -0.26      0.09    -0.43    -0.08 1.00
## SRTBRiskz_PNI_Grandiosity_z            -0.23      0.10    -0.42    -0.03 1.00
## SRTBRiskz_PNI_Vulnerability_z          -0.02      0.08    -0.18     0.15 1.00
## SRTBRiskz_Age                          -0.01      0.01    -0.02     0.01 1.00
## SRTBRiskz_Gender2                       0.41      0.12     0.17     0.65 1.00
## SRTBBenefitz_DoPL_Dominance_z           0.48      0.02     0.44     0.52 1.00
## SRTBBenefitz_DoPL_Prestige_z            0.06      0.11    -0.16     0.27 1.00
## SRTBBenefitz_DoPL_Leadership_z         -0.27      0.10    -0.47    -0.07 1.00
## SRTBBenefitz_PNI_Grandiosity_z         -0.06      0.11    -0.28     0.16 1.00
## SRTBBenefitz_PNI_Vulnerability_z       -0.18      0.09    -0.37     0.01 1.00
## SRTBBenefitz_Age                        0.00      0.01    -0.02     0.02 1.00
## SRTBBenefitz_Gender2                   -0.14      0.12    -0.38     0.11 1.00
## SRTBFrequencyz_DoPL_Dominance_z         0.49      0.02     0.45     0.53 1.00
## SRTBFrequencyz_DoPL_Prestige_z          0.10      0.11    -0.12     0.32 1.00
## SRTBFrequencyz_DoPL_Leadership_z       -0.16      0.10    -0.36     0.05 1.00
## SRTBFrequencyz_PNI_Grandiosity_z       -0.23      0.11    -0.46    -0.01 1.00
## SRTBFrequencyz_PNI_Vulnerability_z     -0.18      0.10    -0.37     0.02 1.00
## SRTBFrequencyz_Age                      0.00      0.01    -0.02     0.02 1.00
## SRTBFrequencyz_Gender2                  0.21      0.12    -0.03     0.46 1.00
##                                     Bulk_ESS Tail_ESS
## SRTBLikelihoodz_Intercept              38012    30029
## SRTBRiskz_Intercept                    61350    27856
## SRTBBenefitz_Intercept                 35872    29620
## SRTBFrequencyz_Intercept               38132    31074
## SRTBLikelihoodz_DoPL_Dominance_z       67477    26000
## SRTBLikelihoodz_DoPL_Prestige_z        30499    29111
## SRTBLikelihoodz_DoPL_Leadership_z      31661    30943
## SRTBLikelihoodz_PNI_Grandiosity_z      29276    29049
## SRTBLikelihoodz_PNI_Vulnerability_z    32771    30078
## SRTBLikelihoodz_Age                    38422    29742
## SRTBLikelihoodz_Gender2                49035    28882
## SRTBRiskz_DoPL_Dominance_z             70532    27696
## SRTBRiskz_DoPL_Prestige_z              46884    29761
## SRTBRiskz_DoPL_Leadership_z            51054    29821
## SRTBRiskz_PNI_Grandiosity_z            43590    28767
## SRTBRiskz_PNI_Vulnerability_z          48074    29052
## SRTBRiskz_Age                          64107    28678
## SRTBRiskz_Gender2                      58847    28382
## SRTBBenefitz_DoPL_Dominance_z          72989    25291
## SRTBBenefitz_DoPL_Prestige_z           29079    28998
## SRTBBenefitz_DoPL_Leadership_z         30848    29577
## SRTBBenefitz_PNI_Grandiosity_z         27292    27852
## SRTBBenefitz_PNI_Vulnerability_z       31066    29605
## SRTBBenefitz_Age                       36802    30003
## SRTBBenefitz_Gender2                   45447    29035
## SRTBFrequencyz_DoPL_Dominance_z        79313    27155
## SRTBFrequencyz_DoPL_Prestige_z         29606    29034
## SRTBFrequencyz_DoPL_Leadership_z       32538    29982
## SRTBFrequencyz_PNI_Grandiosity_z       29144    29612
## SRTBFrequencyz_PNI_Vulnerability_z     33280    29155
## SRTBFrequencyz_Age                     38714    31030
## SRTBFrequencyz_Gender2                 49533    30042
## 
## Family Specific Parameters: 
##                       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_SRTBLikelihoodz     1.16      0.06     1.04     1.29 1.00    38858
## sigma_SRTBRiskz           0.99      0.05     0.90     1.10 1.00    63110
## sigma_SRTBBenefitz        1.13      0.06     1.02     1.25 1.00    37323
## sigma_SRTBFrequencyz      1.15      0.06     1.04     1.28 1.00    41342
##                       Tail_ESS
## sigma_SRTBLikelihoodz    29959
## sigma_SRTBRiskz          26532
## sigma_SRTBBenefitz       30111
## sigma_SRTBFrequencyz     30566
## 
## Residual Correlations: 
##                                        Estimate Est.Error l-95% CI u-95% CI
## rescor(SRTBLikelihoodz,SRTBRiskz)         -0.08      0.07    -0.23     0.06
## rescor(SRTBLikelihoodz,SRTBBenefitz)       0.56      0.05     0.45     0.65
## rescor(SRTBRiskz,SRTBBenefitz)            -0.26      0.07    -0.40    -0.12
## rescor(SRTBLikelihoodz,SRTBFrequencyz)     0.53      0.05     0.43     0.63
## rescor(SRTBRiskz,SRTBFrequencyz)          -0.12      0.07    -0.26     0.03
## rescor(SRTBBenefitz,SRTBFrequencyz)        0.56      0.05     0.46     0.66
##                                        Rhat Bulk_ESS Tail_ESS
## rescor(SRTBLikelihoodz,SRTBRiskz)      1.00    52834    29434
## rescor(SRTBLikelihoodz,SRTBBenefitz)   1.00    36038    29408
## rescor(SRTBRiskz,SRTBBenefitz)         1.00    47908    29655
## rescor(SRTBLikelihoodz,SRTBFrequencyz) 1.00    36802    30882
## rescor(SRTBRiskz,SRTBFrequencyz)       1.00    48526    29034
## rescor(SRTBBenefitz,SRTBFrequencyz)    1.00    39916    30028
## 
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).

Correlation

Show the code

correlation_df <- MutateColumns::column_mutation(Experiment_4_Analysis_DF)

correlation_df <- subset(correlation_df, select = c(
"Age",
"Dominance",
"Leadership",
"Prestige",
"B PNI",
"PNI Grandiosity",
"PNI Vulnerability",
"UMS",
"UMS Intimacy",
"UMS Affiliation",
"SRTB Risk Benefit",
"SRTB Risk Perception",
"SRTB Risk Likelihood",
"SRTB Risk Frequency"
))

correlation_df$Age <- scale(correlation_df$Age)
corr_1 <- correlation(correlation_df, bayesian = TRUE, method = "auto")
saveRDS(corr_1, "corr_1.rds")

Correlation Summary

Show the code

knitr::include_graphics("../test-1.png")