Power motivations and sexual risk-taking

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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")

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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 = "Gender2") +
  scale_y_continuous(breaks = seq(0, 160, 10), guide = "prism_offset") +
  theme(legend.position = "none")

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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")
)

Multi model with dopl and pni as predictor variables

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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"
)

::: {#tbl-SRTBDoPLPNI .cell}

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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).

:::

Table 1: Experiment 2 | Bayesian regression of individual DOSPERT domains as response and dominance, prestige, leadership, and pathlogical narcissism as predictors.
	SRTB Likelihood			SRTB Perception			SRTB Benefit			SRTB Frequency
Predictor	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE	Estimate	HDI (95&)	ROPE
Intercept	-0.14	-0.75, 0.46	24%	0.00	-0.61, 0.6	27%	0.01	-0.59, 0.59	28%	0.01	-0.5, 0.54	31%
Dominance	0.49	0.45, 0.53	0%	0.49	0.45, 0.52	0%	0.48	0.44, 0.52	0%	0.47	0.43, 0.51	0%
Prestige	0.08	-0.13, 0.3	54%	-0.07	-0.28, 0.14	57%	0.07	-0.14, 0.28	59%	0.20	0.01, 0.38	13%
Leadership	-0.17	-0.37, 0.03	22%	-0.06	-0.26, 0.14	63%	-0.26	-0.46, -0.07	2%	-0.29	-0.46, -0.12	0%
B-PNI	-0.33	-0.52, -0.14	0%	-0.34	-0.53, -0.15	0%	-0.22	-0.41, -0.03	8%	-0.17	-0.33, 0	19%
Age	0.00	-0.02, 0.02	100%	0.00	-0.02, 0.02	100%	0.00	-0.02, 0.02	100%	-0.01	-0.02, 0.01	100%
Gender	0.20	-0.04, 0.45	20%	0.08	-0.16, 0.33	50%	-0.13	-0.37, 0.11	39%	0.39	0.15, 0.62	0%
Note: ROPE equates to percentage in region of practical equivalence. HDI equates to high density interval of the posterior distribution.

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"
)
## Running MCMC with 4 chains, at most 8 in parallel...
## 
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## 
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## Total execution time: 25.6 seconds.

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    -0.82     0.51
## SRTBRiskz_Intercept                          -0.11      0.29    -0.68     0.46
## SRTBBenefitz_Intercept                       -0.16      0.34    -0.83     0.50
## 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    -0.11     0.40
## SRTBLikelihoodz_DoPL_Prestige_z              -0.12      0.17    -0.45     0.20
## 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    -0.28     0.62
## SRTBLikelihoodz_Gender2:DoPL_Leadership_z    -0.10      0.22    -0.54     0.34
## SRTBLikelihoodz_Gender2:B_PNI_z              -0.42      0.21    -0.82    -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                  -0.31      0.12    -0.55    -0.07
## SRTBRiskz_B_PNI_z                            -0.23      0.13    -0.48     0.03
## 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    -0.35     0.43
## 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      0.16    -0.19     0.45
## SRTBBenefitz_DoPL_Leadership_z               -0.29      0.14    -0.56    -0.02
## SRTBBenefitz_B_PNI_z                         -0.23      0.14    -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    -0.52     0.35
## SRTBBenefitz_Gender2:B_PNI_z                 -0.18      0.20    -0.58     0.23
## SRTBFrequencyz_DoPL_Dominance_z               0.49      0.02     0.45     0.53
## SRTBFrequencyz_Gender2                        0.26      0.13     0.01     0.52
## SRTBFrequencyz_DoPL_Prestige_z               -0.11      0.17    -0.43     0.22
## SRTBFrequencyz_DoPL_Leadership_z             -0.04      0.14    -0.32     0.23
## SRTBFrequencyz_B_PNI_z                       -0.25      0.15    -0.54     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    -0.01     0.88
## SRTBFrequencyz_Gender2:DoPL_Leadership_z     -0.42      0.22    -0.86     0.01
## SRTBFrequencyz_Gender2:B_PNI_z               -0.37      0.21    -0.77     0.04
##                                           Rhat Bulk_ESS Tail_ESS
## SRTBLikelihoodz_Intercept                 1.00    37056    29518
## SRTBRiskz_Intercept                       1.00    56027    28434
## SRTBBenefitz_Intercept                    1.00    36139    28201
## SRTBFrequencyz_Intercept                  1.00    36552    28506
## SRTBLikelihoodz_DoPL_Dominance_z          1.00    63894    26284
## SRTBLikelihoodz_Gender2                   1.00    46628    28825
## SRTBLikelihoodz_DoPL_Prestige_z           1.00    21783    25167
## SRTBLikelihoodz_DoPL_Leadership_z         1.00    24438    26559
## SRTBLikelihoodz_B_PNI_z                   1.00    26250    28283
## SRTBLikelihoodz_Age                       1.00    37534    29296
## SRTBLikelihoodz_DoPL_Dominance_z:Gender2  1.00    63725    26604
## SRTBLikelihoodz_Gender2:DoPL_Prestige_z   1.00    22870    26671
## SRTBLikelihoodz_Gender2:DoPL_Leadership_z 1.00    26467    28330
## SRTBLikelihoodz_Gender2:B_PNI_z           1.00    25940    27073
## SRTBRiskz_DoPL_Dominance_z                1.00    60766    26221
## SRTBRiskz_Gender2                         1.00    57915    27195
## SRTBRiskz_DoPL_Prestige_z                 1.00    35124    29638
## SRTBRiskz_DoPL_Leadership_z               1.00    37248    28974
## SRTBRiskz_B_PNI_z                         1.00    40969    28660
## SRTBRiskz_Age                             1.00    57177    26937
## SRTBRiskz_DoPL_Dominance_z:Gender2        1.00    62465    27011
## SRTBRiskz_Gender2:DoPL_Prestige_z         1.00    36443    30549
## SRTBRiskz_Gender2:DoPL_Leadership_z       1.00    41288    28542
## SRTBRiskz_Gender2:B_PNI_z                 1.00    40680    28322
## SRTBBenefitz_DoPL_Dominance_z             1.00    66813    27803
## SRTBBenefitz_Gender2                      1.00    46608    29275
## SRTBBenefitz_DoPL_Prestige_z              1.00    22616    25403
## SRTBBenefitz_DoPL_Leadership_z            1.00    24132    26836
## SRTBBenefitz_B_PNI_z                      1.00    25791    28168
## SRTBBenefitz_Age                          1.00    36709    28537
## SRTBBenefitz_DoPL_Dominance_z:Gender2     1.00    66494    25793
## SRTBBenefitz_Gender2:DoPL_Prestige_z      1.00    22954    25028
## SRTBBenefitz_Gender2:DoPL_Leadership_z    1.00    26168    27902
## SRTBBenefitz_Gender2:B_PNI_z              1.00    25194    26749
## SRTBFrequencyz_DoPL_Dominance_z           1.00    63824    28152
## SRTBFrequencyz_Gender2                    1.00    47881    28823
## SRTBFrequencyz_DoPL_Prestige_z            1.00    21523    26928
## SRTBFrequencyz_DoPL_Leadership_z          1.00    24100    27984
## SRTBFrequencyz_B_PNI_z                    1.00    25590    27936
## SRTBFrequencyz_Age                        1.00    37118    29498
## SRTBFrequencyz_DoPL_Dominance_z:Gender2   1.00    66395    25091
## SRTBFrequencyz_Gender2:DoPL_Prestige_z    1.00    22053    26053
## SRTBFrequencyz_Gender2:DoPL_Leadership_z  1.00    26115    27479
## SRTBFrequencyz_Gender2:B_PNI_z            1.00    24779    28055
## 
## Family Specific Parameters: 
##                       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_SRTBLikelihoodz     1.30      0.07     1.17     1.45 1.00    36287
## sigma_SRTBRiskz           1.10      0.06     0.99     1.22 1.00    55208
## sigma_SRTBBenefitz        1.28      0.07     1.15     1.43 1.00    36365
## sigma_SRTBFrequencyz      1.30      0.07     1.17     1.44 1.00    36878
##                       Tail_ESS
## sigma_SRTBLikelihoodz    30112
## sigma_SRTBRiskz          27329
## sigma_SRTBBenefitz       29800
## sigma_SRTBFrequencyz     30574
## 
## 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     0.25
## rescor(SRTBBenefitz,SRTBFrequencyz)        0.66      0.04     0.57     0.74
##                                        Rhat Bulk_ESS Tail_ESS
## rescor(SRTBLikelihoodz,SRTBRiskz)      1.00    52845    30572
## rescor(SRTBLikelihoodz,SRTBBenefitz)   1.00    36134    30450
## rescor(SRTBRiskz,SRTBBenefitz)         1.00    50388    29321
## rescor(SRTBLikelihoodz,SRTBFrequencyz) 1.00    34925    29531
## rescor(SRTBRiskz,SRTBFrequencyz)       1.00    50179    29869
## rescor(SRTBBenefitz,SRTBFrequencyz)    1.00    36998    29178
## 
## 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).

Table 2: Experiment 2 | Bayesian regression of individual DOSPERT domains as response and dominance, prestige, leadership, and pathlogical narcissism as predictors.
	SRTB Likelihood			SRTB Perception			SRTB Benefit			SRTB Frequency
Predictor	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE	Estimate	HDI (95%)	ROPE	Estimate	HDI (95&)	ROPE
Intercept	-0.26	-0.93, 0.41	19%	-0.15	-0.82, 0.51	22%	-0.16	-0.83, 0.5	22%	-0.11	-0.68, 0.46	26%
Dominance	0.49	0.45, 0.53	0%	0.49	0.45, 0.53	0%	0.48	0.44, 0.52	0%	0.48	0.44, 0.51	0%
Gender	0.26	0.01, 0.52	8%	0.15	-0.11, 0.4	35%	-0.06	-0.3, 0.2	55%	0.47	0.22, 0.72	0%
Prestige	-0.11	-0.43, 0.22	39%	-0.12	-0.45, 0.2	38%	0.13	-0.19, 0.45	37%	0.21	-0.08, 0.5	21%
Leadership	-0.04	-0.32, 0.23	53%	-0.06	-0.34, 0.21	51%	-0.29	-0.56, -0.02	6%	-0.31	-0.55, -0.07	2%
B-PNI	-0.25	-0.54, 0.04	14%	-0.24	-0.53, 0.05	15%	-0.23	-0.51, 0.05	17%	-0.23	-0.48, 0.03	15%
Age	0.00	-0.02, 0.02	100%	0.00	-0.02, 0.02	100%	0.01	-0.02, 0.03	100%	-0.01	-0.02, 0.01	100%
Dominance : Gender	0.49	0.45, 0.53	0%	0.49	0.46, 0.53	0%	0.49	0.45, 0.53	0%	0.48	0.44, 0.52	0%
Gender : Prestige	0.44	-0.01, 0.88	5%	0.17	-0.28, 0.62	27%	-0.05	-0.5, 0.39	35%	0.04	-0.35, 0.43	39%
Gender : Leadership	-0.42	-0.86, 0.01	5%	-0.10	-0.54, 0.34	33%	-0.08	-0.52, 0.35	35%	-0.05	-0.43, 0.34	40%
Gender : B-PNI	-0.37	-0.77, 0.04	8%	-0.42	-0.82, -0.01	4%	-0.18	-0.58, 0.23	28%	-0.10	-0.45, 0.26	38%
Note: ROPE equates to percentage in region of practical equivalence. HDI equates to high density interval of the posterior distribution.

m1 model comparison

Show the code

m1_comparison <- loo(m1, m1_interaction)
m1_comparison

Show the code

bayes_factor(m1, m1_interaction)
## Iteration: 1
## Iteration: 2
## Iteration: 3
## Iteration: 4
## Iteration: 1
## Iteration: 2
## Iteration: 3
## Iteration: 4
## Iteration: 5
## Estimated Bayes factor in favor of m1 over m1_interaction: 934430191642570573392593813504.00000

Correlation

Show the code

correlation_df <- Experiment_4_DF_Final %>% rename(
"Age" = "Age",
"Dominance" = "DoPL_Dominance_z",
"Leadership" = "DoPL_Leadership_z",
"Prestige" = "DoPL_Prestige_z",
"B-PNI" = "B_PNI_z",
"Grandiosity" = "PNI_Grandiosity_z",
"Vulnerability" = "PNI_Vulnerability_z",
"UMS" = "UMS_z",
"Intimacy" = "UMS_Intimacy_z",
"Affiliation" = "UMS_Affiliation_z",
"SRTB Benefit" = "SRTB_Benefit_z",
"SRTB Risk" = "SRTB_Risk_z",
"SRTB Likelihood" = "SRTB_Likelihood_z",
"SRTB Frequency" = "SRTB_Frequency_z"
)

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

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

Show the code

summary(corr_1)
## # Correlation Matrix (auto-method)
## 
## Parameter       | SRTB Frequency | SRTB Likelihood | SRTB Risk | SRTB Benefit | Affiliation |  Intimacy |     UMS | Vulnerability | Grandiosity |   B-PNI | Prestige | Leadership | Dominance
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## Age             |           0.01 |            0.02 |     -0.10 |         0.01 |       -0.08 |     -0.06 |   -0.09 |       -0.19** |       -0.13 | -0.19** |    -0.09 |   8.33e-03 |     -0.07
## Dominance       |         -0.15* |         -0.19** |      0.13 |      -0.19** |     0.26*** | -4.91e-03 |  0.18** |       0.34*** |     0.40*** | 0.42*** |  0.27*** |    0.41*** |          
## Leadership      |          -0.04 |           -0.02 |     -0.04 |        -0.10 |     0.41*** |   0.26*** | 0.41*** |        0.20** |     0.47*** | 0.35*** |  0.55*** |            |          
## Prestige        |          -0.05 |           -0.13 |      0.08 |        -0.07 |     0.54*** |   0.42*** | 0.60*** |       0.34*** |     0.56*** | 0.49*** |          |            |          
## B-PNI           |         -0.14* |         -0.18** |      0.03 |        -0.07 |     0.32*** |   0.23*** | 0.34*** |       0.91*** |     0.80*** |         |          |            |          
## Grandiosity     |          -0.13 |         -0.17** |     -0.02 |        -0.05 |     0.47*** |   0.40*** | 0.51*** |       0.49*** |             |         |          |            |          
## Vulnerability   |          -0.13 |          -0.15* |      0.05 |        -0.08 |       0.15* |      0.07 |   0.15* |               |             |         |          |            |          
## UMS             |          -0.12 |           -0.10 |      0.08 |        -0.09 |     0.91*** |   0.81*** |         |               |             |         |          |            |          
## Intimacy        |          -0.01 |           -0.06 |      0.03 |        -0.03 |     0.54*** |           |         |               |             |         |          |            |          
## Affiliation     |         -0.16* |           -0.10 |      0.08 |        -0.11 |             |           |         |               |             |         |          |            |          
## SRTB Benefit    |        0.43*** |         0.41*** |  -0.52*** |              |             |           |         |               |             |         |          |            |          
## SRTB Risk       |       -0.30*** |        -0.32*** |           |              |             |           |         |               |             |         |          |            |          
## SRTB Likelihood |        0.40*** |                 |           |              |             |           |         |               |             |         |          |            |