Gender Interaction: DOSPERT
m1_int <- brm(generalRiskPreference ~ dominance_Sum * Gender + leadership_Sum * Gender + prestige_Sum * Gender + Age,
data = Experiment_2_demographics_Gender, warmup = 1000, iter = 10000,
prior = c(
prior(normal(0, 1), class = "Intercept"),
prior(normal(1.04, 4.92), class = "b", coef = "dominanceSum"),
prior(normal(-1.86, 2.04), class = "b", coef = "prestigeSum"),
prior(normal(-3.87, 0.04), class = "b", coef = "leadershipSum"),
prior(normal(-1.93, 1.91), class = "b", coef = "dominance_Sum:Gender"),
prior(normal(-1.85, 1.98), class = "b", coef = "Gender:prestigeSum"),
prior(normal(-1.88, 1.98), class = "b", coef = "Gender:leadershipSum"),
prior(normal(-4.74, 1), class = "b", coef = "Age")
), save_pars = save_pars(all = T)
)
summary(m1_int)
## Family: MV(gaussian, gaussian, gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: ethicalPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age
## financialPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age
## socialPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age
## healthAndSafetyPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age
## recreationalPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 40000; warmup = 1000; thin = 1;
## total post-warmup draws = 156000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 0.15 0.23 -0.30 0.61 1.00 194459 133021
## financialPreferencez_Intercept 0.03 0.26 -0.48 0.53 1.00 201934 125331
## socialPreferencez_Intercept 1.23 0.24 0.77 1.70 1.00 195841 131654
## healthAndSafetyPreferencez_Intercept 0.53 0.25 0.05 1.01 1.00 180945 132435
## recreationalPreferencez_Intercept 0.55 0.25 0.07 1.04 1.00 191576 133042
## ethicalPreferencez_dominance_Sum 0.07 0.19 -0.31 0.45 1.00 106102 112402
## ethicalPreferencez_Gender 0.28 0.11 0.07 0.49 1.00 175818 129147
## ethicalPreferencez_prestige_Sum -0.26 0.17 -0.60 0.08 1.00 112995 115323
## ethicalPreferencez_leadership_Sum -0.18 0.18 -0.54 0.18 1.00 110249 113394
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 202193 134791
## ethicalPreferencez_dominance_Sum:Gender 0.16 0.12 -0.06 0.39 1.00 107999 112689
## ethicalPreferencez_Gender:prestige_Sum 0.16 0.11 -0.05 0.37 1.00 116062 115157
## ethicalPreferencez_Gender:leadership_Sum 0.00 0.11 -0.21 0.22 1.00 111469 115388
## financialPreferencez_dominance_Sum 0.02 0.22 -0.40 0.44 1.00 115275 112622
## financialPreferencez_Gender 0.19 0.12 -0.05 0.42 1.00 191208 126977
## financialPreferencez_prestige_Sum -0.29 0.21 -0.70 0.12 1.00 115007 118113
## financialPreferencez_leadership_Sum -0.03 0.21 -0.43 0.38 1.00 114356 114775
## financialPreferencez_Age -0.01 0.01 -0.02 0.00 1.00 209762 128368
## financialPreferencez_dominance_Sum:Gender 0.04 0.13 -0.21 0.29 1.00 115737 113097
## financialPreferencez_Gender:prestige_Sum 0.17 0.13 -0.08 0.42 1.00 116996 116728
## financialPreferencez_Gender:leadership_Sum 0.08 0.12 -0.17 0.32 1.00 116532 114675
## socialPreferencez_dominance_Sum 0.00 0.20 -0.39 0.40 1.00 117390 117165
## socialPreferencez_Gender -0.45 0.11 -0.67 -0.23 1.00 193249 132341
## socialPreferencez_prestige_Sum 0.10 0.19 -0.29 0.48 1.00 120181 114678
## socialPreferencez_leadership_Sum 0.15 0.20 -0.24 0.55 1.00 115822 116967
## socialPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 206330 131871
## socialPreferencez_dominance_Sum:Gender 0.00 0.12 -0.23 0.23 1.00 117661 117365
## socialPreferencez_Gender:prestige_Sum -0.04 0.12 -0.27 0.19 1.00 122383 112483
## socialPreferencez_Gender:leadership_Sum 0.07 0.12 -0.17 0.31 1.00 116076 117168
## healthAndSafetyPreferencez_dominance_Sum 0.30 0.20 -0.10 0.71 1.00 97150 109277
## healthAndSafetyPreferencez_Gender 0.01 0.12 -0.21 0.24 1.00 164143 127545
## healthAndSafetyPreferencez_prestige_Sum -0.58 0.18 -0.93 -0.23 1.00 110940 116552
## healthAndSafetyPreferencez_leadership_Sum 0.04 0.19 -0.34 0.41 1.00 101058 113423
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 188671 131843
## healthAndSafetyPreferencez_dominance_Sum:Gender 0.00 0.12 -0.24 0.24 1.00 96407 107331
## healthAndSafetyPreferencez_Gender:prestige_Sum 0.24 0.11 0.02 0.46 1.00 112858 117222
## healthAndSafetyPreferencez_Gender:leadership_Sum -0.02 0.11 -0.24 0.20 1.00 100709 113013
## recreationalPreferencez_dominance_Sum 0.49 0.20 0.09 0.88 1.00 109723 116207
## recreationalPreferencez_Gender 0.21 0.12 -0.02 0.44 1.00 177524 130251
## recreationalPreferencez_prestige_Sum -0.50 0.12 -0.73 -0.27 1.00 135431 118997
## recreationalPreferencez_leadership_Sum 0.16 0.17 -0.18 0.49 1.00 113098 114740
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02 1.00 195400 131867
## recreationalPreferencez_dominance_Sum:Gender -0.21 0.12 -0.44 0.03 1.00 110342 116372
## recreationalPreferencez_Gender:prestige_Sum 0.18 0.08 0.02 0.33 1.00 138852 118756
## recreationalPreferencez_Gender:leadership_Sum 0.00 0.11 -0.21 0.21 1.00 113755 115728
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 0.88 0.04 0.81 0.96 1.00 179581 123546
## sigma_financialPreferencez 0.98 0.04 0.90 1.07 1.00 201653 123283
## sigma_socialPreferencez 0.90 0.04 0.83 0.98 1.00 195350 124743
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.87 1.03 1.00 173147 129171
## sigma_recreationalPreferencez 0.94 0.04 0.87 1.03 1.00 182457 126634
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 0.35 0.05 0.25 0.45 1.00 187097 125639
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06 0.02 0.25 1.00 183560 128837
## rescor(financialPreferencez,socialPreferencez) 0.24 0.06 0.12 0.35 1.00 181861 127590
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.50 0.05 0.40 0.58 1.00 163628 128023
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06 0.09 0.32 1.00 168893 126323
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.32 0.05 0.21 0.42 1.00 184028 127125
## rescor(ethicalPreferencez,recreationalPreferencez) 0.21 0.06 0.09 0.32 1.00 168023 128043
## rescor(financialPreferencez,recreationalPreferencez) 0.23 0.06 0.11 0.34 1.00 169702 129008
## rescor(socialPreferencez,recreationalPreferencez) 0.39 0.05 0.28 0.48 1.00 170766 126967
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.46 0.05 0.36 0.55 1.00 179814 129305
##
## 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 <- brm(mvbind(ethicalPreference_z, financialPreference_z, socialPreference_z, healthAndSafetyPreference_z, recreationalPreference_z) ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), backend = "cmdstanr",
prior = prior_test_1, warmup = 1000, iter = 40000, save_pars = save_pars(all = TRUE)
)
summary(m1)
## Family: MV(gaussian, gaussian, gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: ethicalPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## financialPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## socialPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## healthAndSafetyPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## recreationalPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 40000; warmup = 1000; thin = 1;
## total post-warmup draws = 156000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 0.19 0.23 -0.26 0.64 1.00 212551 133987
## financialPreferencez_Intercept 0.01 0.25 -0.48 0.51 1.00 228435 131270
## socialPreferencez_Intercept 1.24 0.23 0.78 1.69 1.00 220488 135807
## healthAndSafetyPreferencez_Intercept 0.52 0.24 0.04 1.00 1.00 207755 137383
## recreationalPreferencez_Intercept 0.51 0.24 0.03 0.99 1.00 219477 135665
## ethicalPreferencez_dominance_Sum 0.33 0.06 0.21 0.45 1.00 184984 130137
## ethicalPreferencez_prestige_Sum -0.05 0.06 -0.17 0.07 1.00 177533 129800
## ethicalPreferencez_leadership_Sum -0.18 0.06 -0.30 -0.06 1.00 179079 133837
## ethicalPreferencez_Gender 0.27 0.11 0.05 0.48 1.00 197403 131456
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 204831 134759
## financialPreferencez_dominance_Sum 0.09 0.06 -0.04 0.21 1.00 197536 129809
## financialPreferencez_prestige_Sum -0.05 0.07 -0.18 0.08 1.00 196888 131918
## financialPreferencez_leadership_Sum 0.09 0.07 -0.04 0.22 1.00 193177 130283
## financialPreferencez_Gender 0.19 0.12 -0.05 0.42 1.00 211525 132426
## financialPreferencez_Age -0.01 0.01 -0.02 0.00 1.00 223451 130857
## socialPreferencez_dominance_Sum 0.01 0.06 -0.11 0.13 1.00 200558 135972
## socialPreferencez_prestige_Sum 0.01 0.06 -0.11 0.13 1.00 192297 130284
## socialPreferencez_leadership_Sum 0.28 0.06 0.15 0.40 1.00 188379 130679
## socialPreferencez_Gender -0.45 0.11 -0.67 -0.24 1.00 204118 132949
## socialPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 209135 136084
## healthAndSafetyPreferencez_dominance_Sum 0.30 0.06 0.18 0.42 1.00 164714 130272
## healthAndSafetyPreferencez_prestige_Sum -0.24 0.06 -0.36 -0.11 1.00 166704 130732
## healthAndSafetyPreferencez_leadership_Sum 0.00 0.06 -0.12 0.13 1.00 163164 133531
## healthAndSafetyPreferencez_Gender 0.01 0.12 -0.21 0.24 1.00 179963 134646
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 193445 132048
## recreationalPreferencez_dominance_Sum 0.16 0.06 0.04 0.28 1.00 183480 134769
## recreationalPreferencez_prestige_Sum -0.27 0.06 -0.39 -0.16 1.00 190445 134418
## recreationalPreferencez_leadership_Sum 0.18 0.06 0.05 0.30 1.00 179093 130894
## recreationalPreferencez_Gender 0.23 0.12 -0.00 0.45 1.00 194446 130133
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02 1.00 203011 133743
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 0.89 0.04 0.82 0.97 1.00 215952 127953
## sigma_financialPreferencez 0.98 0.04 0.90 1.06 1.00 240264 126716
## sigma_socialPreferencez 0.90 0.04 0.83 0.98 1.00 238825 122865
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.87 1.02 1.00 202093 133684
## sigma_recreationalPreferencez 0.94 0.04 0.86 1.02 1.00 223714 129971
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 0.36 0.05 0.25 0.46 1.00 225531 126597
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06 0.01 0.24 1.00 215552 132800
## rescor(financialPreferencez,socialPreferencez) 0.23 0.06 0.12 0.34 1.00 216115 128669
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.50 0.05 0.41 0.58 1.00 193681 135547
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06 0.09 0.32 1.00 206857 132917
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05 0.20 0.42 1.00 212954 127137
## rescor(ethicalPreferencez,recreationalPreferencez) 0.19 0.06 0.07 0.30 1.00 196279 134270
## rescor(financialPreferencez,recreationalPreferencez) 0.21 0.06 0.10 0.32 1.00 203114 131793
## rescor(socialPreferencez,recreationalPreferencez) 0.38 0.05 0.28 0.48 1.00 199639 130658
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05 0.35 0.54 1.00 201961 130657
##
## 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_hdi <- bayestestR::hdi(m1, effects = "fixed", component = "conditional", ci = .95)
kable(m1_hdi[
sign(m1_hdi$CI_low) == sign(m1_hdi$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
], format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>%
kable_styling(full_width = T) %>%
remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_ethicalPreferencez_dominance_Sum
|
0.95
|
0.21
|
0.44
|
|
b_ethicalPreferencez_leadership_Sum
|
0.95
|
-0.30
|
-0.06
|
|
b_ethicalPreferencez_Gender
|
0.95
|
0.05
|
0.48
|
|
b_ethicalPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_socialPreferencez_Intercept
|
0.95
|
0.78
|
1.69
|
|
b_socialPreferencez_leadership_Sum
|
0.95
|
0.15
|
0.40
|
|
b_socialPreferencez_Gender
|
0.95
|
-0.67
|
-0.23
|
|
b_socialPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_healthAndSafetyPreferencez_Intercept
|
0.95
|
0.04
|
1.00
|
|
b_healthAndSafetyPreferencez_dominance_Sum
|
0.95
|
0.18
|
0.43
|
|
b_healthAndSafetyPreferencez_prestige_Sum
|
0.95
|
-0.36
|
-0.11
|
|
b_healthAndSafetyPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_recreationalPreferencez_Intercept
|
0.95
|
0.04
|
0.99
|
|
b_recreationalPreferencez_dominance_Sum
|
0.95
|
0.04
|
0.28
|
|
b_recreationalPreferencez_prestige_Sum
|
0.95
|
-0.39
|
-0.16
|
|
b_recreationalPreferencez_leadership_Sum
|
0.95
|
0.05
|
0.30
|
|
b_recreationalPreferencez_Age
|
0.95
|
-0.04
|
-0.02
|
m1_int <- brm(mvbind(ethicalPreference_z, financialPreference_z, socialPreference_z, healthAndSafetyPreference_z, recreationalPreference_z) ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), backend = "cmdstanr",
prior = prior_gender_int, warmup = 1000, iter = 40000, save_pars = save_pars(all = TRUE)
)
summary(m1)
## Family: MV(gaussian, gaussian, gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: ethicalPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## financialPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## socialPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## healthAndSafetyPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## recreationalPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 40000; warmup = 1000; thin = 1;
## total post-warmup draws = 156000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 0.19 0.23 -0.26 0.64 1.00 212551 133987
## financialPreferencez_Intercept 0.01 0.25 -0.48 0.51 1.00 228435 131270
## socialPreferencez_Intercept 1.24 0.23 0.78 1.69 1.00 220488 135807
## healthAndSafetyPreferencez_Intercept 0.52 0.24 0.04 1.00 1.00 207755 137383
## recreationalPreferencez_Intercept 0.51 0.24 0.03 0.99 1.00 219477 135665
## ethicalPreferencez_dominance_Sum 0.33 0.06 0.21 0.45 1.00 184984 130137
## ethicalPreferencez_prestige_Sum -0.05 0.06 -0.17 0.07 1.00 177533 129800
## ethicalPreferencez_leadership_Sum -0.18 0.06 -0.30 -0.06 1.00 179079 133837
## ethicalPreferencez_Gender 0.27 0.11 0.05 0.48 1.00 197403 131456
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 204831 134759
## financialPreferencez_dominance_Sum 0.09 0.06 -0.04 0.21 1.00 197536 129809
## financialPreferencez_prestige_Sum -0.05 0.07 -0.18 0.08 1.00 196888 131918
## financialPreferencez_leadership_Sum 0.09 0.07 -0.04 0.22 1.00 193177 130283
## financialPreferencez_Gender 0.19 0.12 -0.05 0.42 1.00 211525 132426
## financialPreferencez_Age -0.01 0.01 -0.02 0.00 1.00 223451 130857
## socialPreferencez_dominance_Sum 0.01 0.06 -0.11 0.13 1.00 200558 135972
## socialPreferencez_prestige_Sum 0.01 0.06 -0.11 0.13 1.00 192297 130284
## socialPreferencez_leadership_Sum 0.28 0.06 0.15 0.40 1.00 188379 130679
## socialPreferencez_Gender -0.45 0.11 -0.67 -0.24 1.00 204118 132949
## socialPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 209135 136084
## healthAndSafetyPreferencez_dominance_Sum 0.30 0.06 0.18 0.42 1.00 164714 130272
## healthAndSafetyPreferencez_prestige_Sum -0.24 0.06 -0.36 -0.11 1.00 166704 130732
## healthAndSafetyPreferencez_leadership_Sum 0.00 0.06 -0.12 0.13 1.00 163164 133531
## healthAndSafetyPreferencez_Gender 0.01 0.12 -0.21 0.24 1.00 179963 134646
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 193445 132048
## recreationalPreferencez_dominance_Sum 0.16 0.06 0.04 0.28 1.00 183480 134769
## recreationalPreferencez_prestige_Sum -0.27 0.06 -0.39 -0.16 1.00 190445 134418
## recreationalPreferencez_leadership_Sum 0.18 0.06 0.05 0.30 1.00 179093 130894
## recreationalPreferencez_Gender 0.23 0.12 -0.00 0.45 1.00 194446 130133
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02 1.00 203011 133743
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 0.89 0.04 0.82 0.97 1.00 215952 127953
## sigma_financialPreferencez 0.98 0.04 0.90 1.06 1.00 240264 126716
## sigma_socialPreferencez 0.90 0.04 0.83 0.98 1.00 238825 122865
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.87 1.02 1.00 202093 133684
## sigma_recreationalPreferencez 0.94 0.04 0.86 1.02 1.00 223714 129971
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 0.36 0.05 0.25 0.46 1.00 225531 126597
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06 0.01 0.24 1.00 215552 132800
## rescor(financialPreferencez,socialPreferencez) 0.23 0.06 0.12 0.34 1.00 216115 128669
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.50 0.05 0.41 0.58 1.00 193681 135547
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06 0.09 0.32 1.00 206857 132917
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05 0.20 0.42 1.00 212954 127137
## rescor(ethicalPreferencez,recreationalPreferencez) 0.19 0.06 0.07 0.30 1.00 196279 134270
## rescor(financialPreferencez,recreationalPreferencez) 0.21 0.06 0.10 0.32 1.00 203114 131793
## rescor(socialPreferencez,recreationalPreferencez) 0.38 0.05 0.28 0.48 1.00 199639 130658
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05 0.35 0.54 1.00 201961 130657
##
## 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_int_hdi <- bayestestR::hdi(m1_int, effects = "fixed", component = "conditional", ci = .95)
kable(m1_int_hdi[
sign(m1_int_hdi$CI_low) == sign(m1_int_hdi$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
], format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>%
kable_styling(full_width = T) %>%
remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_ethicalPreferencez_Gender
|
0.95
|
0.06
|
0.49
|
|
b_ethicalPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_socialPreferencez_Intercept
|
0.95
|
0.77
|
1.69
|
|
b_socialPreferencez_Gender
|
0.95
|
-0.67
|
-0.23
|
|
b_socialPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_healthAndSafetyPreferencez_Intercept
|
0.95
|
0.04
|
1.01
|
|
b_healthAndSafetyPreferencez_prestige_Sum
|
0.95
|
-0.94
|
-0.23
|
|
b_healthAndSafetyPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_healthAndSafetyPreferencez_Gender:prestige_Sum
|
0.95
|
0.02
|
0.46
|
|
b_recreationalPreferencez_Intercept
|
0.95
|
0.07
|
1.03
|
|
b_recreationalPreferencez_dominance_Sum
|
0.95
|
0.09
|
0.88
|
|
b_recreationalPreferencez_prestige_Sum
|
0.95
|
-0.73
|
-0.27
|
|
b_recreationalPreferencez_Age
|
0.95
|
-0.04
|
-0.02
|
|
b_recreationalPreferencez_Gender:prestige_Sum
|
0.95
|
0.02
|
0.33
|
mod_pni <- brm(generalRiskPreference ~ dominance_Sum + grandiosity_Sum_z + vulnerability_Sum_z + Age + Gender,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), backend = "cmdstanr",
warmup = 1000, iter = 10000,
prior = prior_int_mod, save_pars = save_pars(all = TRUE)
)
summary(mod_pni)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: generalRiskPreference ~ dominance_Sum + grandiosity_Sum_z + vulnerability_Sum_z + Age + Gender
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## 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 Bulk_ESS Tail_ESS
## Intercept 17.87 2.49 12.98 22.69 1.00 50781 28450
## dominance_Sum 3.05 0.74 1.61 4.49 1.00 48860 28576
## grandiosity_Sum_z 0.14 0.62 -1.07 1.35 1.00 53440 28354
## vulnerability_Sum_z -0.50 0.64 -1.75 0.75 1.00 45932 27990
## Age -0.26 0.07 -0.40 -0.11 1.00 48955 27280
## Gender 1.03 0.82 -0.58 2.63 1.00 50314 26042
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 11.24 0.59 10.17 12.46 1.00 40098 29140
##
## 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).
mod_pni_gen <- brm(generalRiskPreference ~ dominance_Sum * Gender + grandiosity_Sum_z * Gender + vulnerability_Sum_z * Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), backend = "cmdstanr",
warmup = 1000, iter = 10000,
prior = prior_int_gen_mod, save_pars = save_pars(all = TRUE)
)
m3 <- brm(mvbind(dominance_Sum, prestige_Sum, leadership_Sum) ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, prior = prior_m3,
save_pars = save_pars(all = TRUE), backend = "cmdstanr"
)
saveRDS(m3, "m3.rds")
summary(m3)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: dominance_Sum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
## prestige_Sum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
## leadership_Sum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## 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 Bulk_ESS Tail_ESS
## dominanceSum_Intercept 0.06 0.26 -0.43 0.57 1.00 43108 30289
## prestigeSum_Intercept 1.00 0.28 0.47 1.55 1.00 46976 30549
## leadershipSum_Intercept 0.30 0.26 -0.21 0.82 1.00 43748 30563
## dominanceSum_ethicalPreference_z 0.29 0.07 0.16 0.42 1.00 41604 28252
## dominanceSum_financialPreference_z -0.18 0.02 -0.21 -0.15 1.00 49934 25993
## dominanceSum_socialPreference_z -0.06 0.07 -0.19 0.07 1.00 38332 28424
## dominanceSum_healthAndSafetyPreference_z 0.05 0.05 -0.05 0.14 1.00 46005 29959
## dominanceSum_recreationalPreference_z 0.03 0.06 -0.09 0.14 1.00 42718 27766
## dominanceSum_Gender 0.27 0.12 0.03 0.51 1.00 40142 29811
## dominanceSum_Age -0.02 0.01 -0.03 -0.00 1.00 42386 30528
## prestigeSum_ethicalPreference_z 0.02 0.08 -0.13 0.17 1.00 38609 29534
## prestigeSum_financialPreference_z 0.02 0.06 -0.10 0.14 1.00 46280 29297
## prestigeSum_socialPreference_z -0.24 0.01 -0.26 -0.21 1.00 53317 27882
## prestigeSum_healthAndSafetyPreference_z -0.05 0.06 -0.18 0.08 1.00 42732 29870
## prestigeSum_recreationalPreference_z -0.08 0.06 -0.20 0.04 1.00 43773 29034
## prestigeSum_Gender -0.15 0.13 -0.41 0.11 1.00 40388 29559
## prestigeSum_Age -0.03 0.01 -0.04 -0.01 1.00 44319 31444
## leadershipSum_ethicalPreference_z -0.14 0.07 -0.28 0.00 1.00 38352 30119
## leadershipSum_financialPreference_z 0.03 0.05 -0.07 0.14 1.00 46189 29843
## leadershipSum_socialPreference_z 0.03 0.05 -0.08 0.13 1.00 43874 30655
## leadershipSum_healthAndSafetyPreference_z 0.05 0.06 -0.07 0.16 1.00 42668 29996
## leadershipSum_recreationalPreference_z -0.05 0.05 -0.14 0.04 1.00 44470 29437
## leadershipSum_Gender -0.09 0.13 -0.34 0.16 1.00 38622 28561
## leadershipSum_Age -0.01 0.01 -0.02 0.01 1.00 41458 30285
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_dominanceSum 0.96 0.04 0.88 1.05 1.00 43621 29634
## sigma_prestigeSum 1.07 0.05 0.98 1.17 1.00 43522 30237
## sigma_leadershipSum 1.00 0.05 0.92 1.10 1.00 38264 29259
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 0.34 0.06 0.22 0.45 1.00 35973 30300
## rescor(dominanceSum,leadershipSum) 0.38 0.05 0.27 0.48 1.00 34850 29072
## rescor(prestigeSum,leadershipSum) 0.51 0.05 0.41 0.60 1.00 38731 29413
##
## 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).
m7_fixef <- fixef(m7_DoPL_DOSPERT)
m3_int_gender <- brm(mvbind(dominance_Sum, prestige_Sum, leadership_Sum) ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, prior = prior_m3_int_gender,
save_pars = save_pars(all = TRUE), backend = "cmdstanr"
)
saveRDS(m3_int_gender, "m3_int_gender.rds")
summary(m3_int_gender)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: dominance_Sum ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## prestige_Sum ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## leadership_Sum ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## 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 Bulk_ESS Tail_ESS
## dominanceSum_Intercept -0.13 0.26 -0.63 0.38 1.00 60311 28813
## prestigeSum_Intercept 0.54 0.27 0.02 1.06 1.00 55460 30671
## leadershipSum_Intercept 0.04 0.26 -0.47 0.55 1.00 54450 29788
## dominanceSum_ethicalPreference_z 0.02 0.16 -0.30 0.34 1.00 39555 28419
## dominanceSum_Gender 0.34 0.12 0.10 0.58 1.00 60987 29332
## dominanceSum_financialPreference_z -0.19 0.02 -0.22 -0.16 1.00 81451 28314
## dominanceSum_socialPreference_z -0.08 0.15 -0.38 0.21 1.00 38003 27738
## dominanceSum_healthAndSafetyPreference_z -0.04 0.06 -0.16 0.09 1.00 51911 30155
## dominanceSum_recreationalPreference_z -0.05 0.10 -0.25 0.15 1.00 40757 28528
## dominanceSum_Age -0.01 0.01 -0.03 -0.00 1.00 62665 31245
## dominanceSum_ethicalPreference_z:Gender 0.11 0.10 -0.08 0.31 1.00 37779 28381
## dominanceSum_Gender:financialPreference_z 0.10 0.04 0.02 0.18 1.00 64844 27567
## dominanceSum_Gender:socialPreference_z 0.09 0.10 -0.10 0.29 1.00 36694 27615
## dominanceSum_Gender:healthAndSafetyPreference_z 0.09 0.06 -0.03 0.21 1.00 48764 29800
## dominanceSum_Gender:recreationalPreference_z 0.03 0.07 -0.11 0.17 1.00 38005 28942
## prestigeSum_ethicalPreference_z -0.04 0.16 -0.35 0.28 1.00 38355 29070
## prestigeSum_Gender 0.08 0.13 -0.17 0.34 1.00 50359 31078
## prestigeSum_financialPreference_z -0.08 0.13 -0.33 0.17 1.00 39406 28808
## prestigeSum_socialPreference_z -0.25 0.01 -0.28 -0.22 1.00 84103 25983
## prestigeSum_healthAndSafetyPreference_z -0.06 0.10 -0.25 0.14 1.00 42767 31207
## prestigeSum_recreationalPreference_z -0.10 0.10 -0.30 0.10 1.00 42002 28485
## prestigeSum_Age -0.02 0.01 -0.03 -0.01 1.00 59756 30700
## prestigeSum_ethicalPreference_z:Gender 0.04 0.10 -0.15 0.24 1.00 36641 29442
## prestigeSum_Gender:financialPreference_z 0.07 0.08 -0.08 0.23 1.00 37583 27891
## prestigeSum_Gender:socialPreference_z 0.31 0.05 0.22 0.40 1.00 59282 27962
## prestigeSum_Gender:healthAndSafetyPreference_z -0.05 0.08 -0.20 0.10 1.00 38228 29619
## prestigeSum_Gender:recreationalPreference_z -0.02 0.07 -0.16 0.12 1.00 38638 28723
## leadershipSum_ethicalPreference_z -0.08 0.17 -0.42 0.25 1.00 35587 28717
## leadershipSum_Gender 0.04 0.13 -0.21 0.28 1.00 49559 30301
## leadershipSum_financialPreference_z -0.08 0.09 -0.26 0.09 1.00 45054 29233
## leadershipSum_socialPreference_z -0.14 0.07 -0.29 0.01 1.00 45591 30501
## leadershipSum_healthAndSafetyPreference_z 0.06 0.10 -0.13 0.25 1.00 45420 29998
## leadershipSum_recreationalPreference_z -0.15 0.06 -0.27 -0.03 1.00 52884 29553
## leadershipSum_Age -0.00 0.01 -0.01 0.01 1.00 56744 29487
## leadershipSum_ethicalPreference_z:Gender -0.04 0.10 -0.24 0.17 1.00 34003 28880
## leadershipSum_Gender:financialPreference_z 0.11 0.06 -0.02 0.23 1.00 42619 29489
## leadershipSum_Gender:socialPreference_z 0.30 0.06 0.18 0.42 1.00 39614 29267
## leadershipSum_Gender:healthAndSafetyPreference_z -0.06 0.07 -0.21 0.08 1.00 42070 29980
## leadershipSum_Gender:recreationalPreference_z 0.09 0.06 -0.01 0.20 1.00 46407 29432
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_dominanceSum 0.94 0.04 0.87 1.03 1.00 64838 29300
## sigma_prestigeSum 0.99 0.04 0.91 1.08 1.00 60362 28418
## sigma_leadershipSum 0.97 0.04 0.89 1.06 1.00 58382 27522
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 0.31 0.06 0.20 0.42 1.00 47676 30279
## rescor(dominanceSum,leadershipSum) 0.37 0.05 0.26 0.47 1.00 50068 30104
## rescor(prestigeSum,leadershipSum) 0.46 0.05 0.37 0.55 1.00 53187 29608
##
## 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).
m3_int_gender_hdi <- bayestestR::hdi(m3_int_gender, effects = "fixed", component = "conditional", ci = .95)
kable(m3_int_gender_hdi[sign(m3_int_gender_hdi$CI_low) == sign(m3_int_gender_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>%
kable_styling(full_width = T) %>%
remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_dominanceSum_Gender
|
0.95
|
0.11
|
0.59
|
|
b_dominanceSum_financialPreference_z
|
0.95
|
-0.22
|
-0.16
|
|
b_dominanceSum_Age
|
0.95
|
-0.03
|
0.00
|
|
b_dominanceSum_Gender:financialPreference_z
|
0.95
|
0.02
|
0.18
|
|
b_prestigeSum_Intercept
|
0.95
|
0.01
|
1.06
|
|
b_prestigeSum_socialPreference_z
|
0.95
|
-0.28
|
-0.22
|
|
b_prestigeSum_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_prestigeSum_Gender:socialPreference_z
|
0.95
|
0.22
|
0.40
|
|
b_leadershipSum_recreationalPreference_z
|
0.95
|
-0.28
|
-0.04
|
|
b_leadershipSum_Gender:socialPreference_z
|
0.95
|
0.17
|
0.42
|
future::plan("multicore")
bla_mod_1 <- "general_Preference =~ generalRiskPreference + prestige_Sum + leadership_Sum + dominance_Sum
general_2Preference =~ generalRiskPreference + PNI_Sum + dominance_Sum + Gender"
fit <- blavaan(bla_mod_1, data = Experiment_2_demographics_Gender, n.chains = 4, auto.var = TRUE, auto.fix.first = TRUE, auto.cov.lv.x = TRUE, target = "cmdstanr", bcontrol = list(cores = parallel::detectCores()))
fit_1 <- bcfa(bla_mod_1, data = Experiment_2_demographics_Gender, n.chains = 4, auto.var = TRUE, auto.fix.first = TRUE, auto.cov.lv.x = TRUE, target = "cmdstanr", sample = 5000, bcontrol = list(cores = parallel::detectCores()))
fit <- readRDS("./fit.rds")
summary(fit)
plot(fit)
m2 <- brm(mvbind(ethicalPreference_z, financialPreference_z, socialPreference_z, healthAndSafetyPreference_z, recreationalPreference_z) ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age, data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 500, save_pars = save_pars(all = TRUE),backend = "cmdstanr",
prior = prior_m2)
# saveRDS(m2, "m2.rds")
summary(m2)
## Family: MV(gaussian, gaussian, gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: ethicalPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## financialPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## socialPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## healthAndSafetyPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## recreationalPreference_z ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 10000; warmup = 500; thin = 1;
## total post-warmup draws = 38000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 0.15 0.24 -0.32 0.60 1.00 54673 34250
## financialPreferencez_Intercept 0.05 0.26 -0.46 0.56 1.00 60246 32970
## socialPreferencez_Intercept 1.10 0.24 0.64 1.57 1.00 56841 33878
## healthAndSafetyPreferencez_Intercept 0.45 0.25 -0.03 0.94 1.00 46787 32743
## recreationalPreferencez_Intercept 0.48 0.25 -0.01 0.97 1.00 52103 32851
## ethicalPreferencez_dominance_Sum 0.31 0.06 0.19 0.44 1.00 40213 30188
## ethicalPreferencez_prestige_Sum -0.06 0.06 -0.19 0.06 1.00 41426 31689
## ethicalPreferencez_leadership_Sum -0.18 0.06 -0.30 -0.06 1.00 42127 30759
## ethicalPreferencez_PNI_Sum_z 0.05 0.07 -0.08 0.18 1.00 39790 32358
## ethicalPreferencez_Gender 0.27 0.11 0.06 0.49 1.00 43322 32738
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 55860 34714
## financialPreferencez_dominance_Sum 0.10 0.07 -0.04 0.24 1.00 45368 33218
## financialPreferencez_prestige_Sum -0.04 0.07 -0.18 0.10 1.00 43982 29172
## financialPreferencez_leadership_Sum 0.09 0.07 -0.04 0.22 1.00 45624 32746
## financialPreferencez_PNI_Sum_z -0.04 0.07 -0.19 0.11 1.00 43309 30814
## financialPreferencez_Gender 0.18 0.12 -0.06 0.42 1.00 54850 32356
## financialPreferencez_Age -0.01 0.01 -0.02 0.00 1.00 60588 31630
## socialPreferencez_dominance_Sum -0.05 0.06 -0.17 0.08 1.00 45028 31569
## socialPreferencez_prestige_Sum -0.04 0.06 -0.17 0.08 1.00 45758 30651
## socialPreferencez_leadership_Sum 0.27 0.06 0.15 0.39 1.00 47740 31812
## socialPreferencez_PNI_Sum_z 0.17 0.07 0.04 0.30 1.00 45033 28346
## socialPreferencez_Gender -0.44 0.11 -0.65 -0.22 1.00 53622 33107
## socialPreferencez_Age -0.02 0.01 -0.03 -0.00 1.00 56789 33083
## healthAndSafetyPreferencez_dominance_Sum 0.27 0.07 0.14 0.40 1.00 35351 31847
## healthAndSafetyPreferencez_prestige_Sum -0.26 0.07 -0.39 -0.13 1.00 38610 31925
## healthAndSafetyPreferencez_leadership_Sum -0.00 0.06 -0.13 0.12 1.00 38466 31378
## healthAndSafetyPreferencez_PNI_Sum_z 0.08 0.07 -0.05 0.22 1.00 35490 32069
## healthAndSafetyPreferencez_Gender 0.02 0.12 -0.20 0.25 1.00 39999 32548
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 52445 33051
## recreationalPreferencez_dominance_Sum 0.15 0.07 0.02 0.28 1.00 40269 32133
## recreationalPreferencez_prestige_Sum -0.28 0.06 -0.40 -0.16 1.00 43384 32425
## recreationalPreferencez_leadership_Sum 0.17 0.06 0.05 0.30 1.00 42374 31417
## recreationalPreferencez_PNI_Sum_z 0.04 0.07 -0.10 0.18 1.00 37806 32382
## recreationalPreferencez_Gender 0.23 0.12 0.00 0.46 1.00 44778 32154
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02 1.00 57096 33349
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 0.89 0.04 0.82 0.97 1.00 54933 30189
## sigma_financialPreferencez 0.98 0.04 0.90 1.07 1.00 65172 31155
## sigma_socialPreferencez 0.89 0.04 0.82 0.97 1.00 65666 30737
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.87 1.02 1.00 49238 31382
## sigma_recreationalPreferencez 0.94 0.04 0.86 1.02 1.00 53058 29291
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 0.36 0.05 0.26 0.46 1.00 61009 30619
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06 0.01 0.24 1.00 52397 31150
## rescor(financialPreferencez,socialPreferencez) 0.24 0.06 0.13 0.35 1.00 57277 31658
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.50 0.05 0.41 0.58 1.00 45262 32204
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06 0.10 0.32 1.00 53229 31709
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05 0.20 0.41 1.00 51886 31049
## rescor(ethicalPreferencez,recreationalPreferencez) 0.19 0.06 0.07 0.30 1.00 48161 33864
## rescor(financialPreferencez,recreationalPreferencez) 0.21 0.06 0.10 0.33 1.00 50776 31854
## rescor(socialPreferencez,recreationalPreferencez) 0.39 0.05 0.28 0.48 1.00 51372 32830
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05 0.35 0.54 1.00 53175 32239
##
## 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).
m2_hdi <- bayestestR::hdi(m2, effects = "fixed", component = "conditional", ci = .95)
kable(m2_hdi[sign(m2_hdi$CI_low) == sign(m2_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>% kable_styling(full_width = T) %>% remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_ethicalPreferencez_dominance_Sum
|
0.95
|
0.19
|
0.44
|
|
b_ethicalPreferencez_leadership_Sum
|
0.95
|
-0.30
|
-0.06
|
|
b_ethicalPreferencez_Gender
|
0.95
|
0.06
|
0.49
|
|
b_ethicalPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_socialPreferencez_Intercept
|
0.95
|
0.63
|
1.56
|
|
b_socialPreferencez_leadership_Sum
|
0.95
|
0.15
|
0.39
|
|
b_socialPreferencez_PNI_Sum_z
|
0.95
|
0.04
|
0.30
|
|
b_socialPreferencez_Gender
|
0.95
|
-0.65
|
-0.22
|
|
b_socialPreferencez_Age
|
0.95
|
-0.03
|
0.00
|
|
b_healthAndSafetyPreferencez_dominance_Sum
|
0.95
|
0.14
|
0.40
|
|
b_healthAndSafetyPreferencez_prestige_Sum
|
0.95
|
-0.39
|
-0.13
|
|
b_healthAndSafetyPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_recreationalPreferencez_dominance_Sum
|
0.95
|
0.01
|
0.27
|
|
b_recreationalPreferencez_prestige_Sum
|
0.95
|
-0.40
|
-0.16
|
|
b_recreationalPreferencez_leadership_Sum
|
0.95
|
0.05
|
0.30
|
|
b_recreationalPreferencez_Gender
|
0.95
|
0.00
|
0.46
|
|
b_recreationalPreferencez_Age
|
0.95
|
-0.04
|
-0.02
|
# plot(m2, ask = FALSE)
m2_int <- brm(mvbind(ethicalPreference_z, financialPreference_z, socialPreference_z, healthAndSafetyPreference_z, recreationalPreference_z) ~ dominance_Sum*Gender + prestige_Sum*Gender + leadership_Sum*Gender + PNI_Sum_z*Gender + Age, data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 500, save_pars = save_pars(all = TRUE),backend = "cmdstanr",
prior = prior_m2_int_gen)
saveRDS(m2_int, "m2_int.rds")
summary(m2_int)
## Family: MV(gaussian, gaussian, gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: ethicalPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## financialPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## socialPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## healthAndSafetyPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## recreationalPreference_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 10000; warmup = 500; thin = 1;
## total post-warmup draws = 38000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 0.12 0.24 -0.34 0.59 1.00 52492 33108
## financialPreferencez_Intercept 0.08 0.26 -0.44 0.59 1.00 55105 32962
## socialPreferencez_Intercept 1.07 0.24 0.60 1.54 1.00 51318 31206
## healthAndSafetyPreferencez_Intercept 0.40 0.25 -0.09 0.89 1.00 50621 33395
## recreationalPreferencez_Intercept 0.44 0.25 -0.05 0.94 1.00 48983 31303
## ethicalPreferencez_dominance_Sum 0.11 0.21 -0.31 0.53 1.00 23996 27939
## ethicalPreferencez_Gender 0.28 0.11 0.07 0.50 1.00 52356 33081
## ethicalPreferencez_prestige_Sum -0.20 0.18 -0.56 0.16 1.00 27237 28465
## ethicalPreferencez_leadership_Sum -0.18 0.18 -0.54 0.18 1.00 29683 29889
## ethicalPreferencez_PNI_Sum_z -0.14 0.22 -0.57 0.28 1.00 23178 28772
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01 1.00 61087 33624
## ethicalPreferencez_dominance_Sum:Gender 0.12 0.13 -0.12 0.37 1.00 24396 27585
## ethicalPreferencez_Gender:prestige_Sum 0.11 0.11 -0.11 0.33 1.00 27414 27876
## ethicalPreferencez_Gender:leadership_Sum 0.01 0.11 -0.21 0.22 1.00 29752 29847
## ethicalPreferencez_Gender:PNI_Sum_z 0.13 0.13 -0.12 0.38 1.00 24001 28281
## financialPreferencez_dominance_Sum 0.13 0.24 -0.34 0.59 1.00 25342 27434
## financialPreferencez_Gender 0.18 0.12 -0.06 0.42 1.00 56320 30228
## financialPreferencez_prestige_Sum -0.17 0.22 -0.60 0.27 1.00 28562 28234
## financialPreferencez_leadership_Sum -0.02 0.21 -0.43 0.39 1.00 30043 29752
## financialPreferencez_PNI_Sum_z -0.28 0.25 -0.76 0.20 1.00 25462 28029
## financialPreferencez_Age -0.01 0.01 -0.02 0.00 1.00 62719 31456
## financialPreferencez_dominance_Sum:Gender -0.02 0.14 -0.29 0.26 1.00 25368 28052
## financialPreferencez_Gender:prestige_Sum 0.11 0.13 -0.16 0.37 1.00 28983 29150
## financialPreferencez_Gender:leadership_Sum 0.08 0.13 -0.17 0.32 1.00 30287 29104
## financialPreferencez_Gender:PNI_Sum_z 0.15 0.14 -0.13 0.44 1.00 26165 28360
## socialPreferencez_dominance_Sum -0.17 0.22 -0.61 0.26 1.00 26294 28607
## socialPreferencez_Gender -0.43 0.11 -0.65 -0.22 1.00 54933 31329
## socialPreferencez_prestige_Sum 0.02 0.20 -0.38 0.43 1.00 31487 29270
## socialPreferencez_leadership_Sum 0.11 0.20 -0.29 0.51 1.00 31078 28562
## socialPreferencez_PNI_Sum_z 0.35 0.22 -0.08 0.80 1.00 27048 28240
## socialPreferencez_Age -0.02 0.01 -0.03 -0.00 1.00 55851 32738
## socialPreferencez_dominance_Sum:Gender 0.08 0.13 -0.18 0.33 1.00 26386 28865
## socialPreferencez_Gender:prestige_Sum -0.02 0.12 -0.26 0.22 1.00 31165 29394
## socialPreferencez_Gender:leadership_Sum 0.09 0.12 -0.15 0.33 1.00 31305 28655
## socialPreferencez_Gender:PNI_Sum_z -0.12 0.13 -0.38 0.14 1.00 27416 28236
## healthAndSafetyPreferencez_dominance_Sum 0.05 0.23 -0.39 0.50 1.00 22569 27928
## healthAndSafetyPreferencez_Gender 0.03 0.12 -0.20 0.26 1.00 46644 33102
## healthAndSafetyPreferencez_prestige_Sum -0.69 0.19 -1.06 -0.31 1.00 28303 29483
## healthAndSafetyPreferencez_leadership_Sum -0.01 0.19 -0.38 0.36 1.00 28415 29521
## healthAndSafetyPreferencez_PNI_Sum_z 0.53 0.23 0.08 0.97 1.00 21733 28118
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.00 1.00 58853 32114
## healthAndSafetyPreferencez_dominance_Sum:Gender 0.13 0.13 -0.13 0.39 1.00 22517 27795
## healthAndSafetyPreferencez_Gender:prestige_Sum 0.29 0.12 0.06 0.52 1.00 28422 28235
## healthAndSafetyPreferencez_Gender:leadership_Sum -0.00 0.11 -0.22 0.23 1.00 28509 29207
## healthAndSafetyPreferencez_Gender:PNI_Sum_z -0.28 0.13 -0.54 -0.01 1.00 22229 28399
## recreationalPreferencez_dominance_Sum 0.13 0.23 -0.32 0.57 1.00 22680 26939
## recreationalPreferencez_Gender 0.23 0.12 0.00 0.45 1.00 48724 31190
## recreationalPreferencez_prestige_Sum -0.58 0.12 -0.81 -0.35 1.00 37539 31225
## recreationalPreferencez_leadership_Sum 0.08 0.17 -0.26 0.41 1.00 29966 29259
## recreationalPreferencez_PNI_Sum_z 0.76 0.22 0.33 1.20 1.00 23144 28599
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02 1.00 54308 32619
## recreationalPreferencez_dominance_Sum:Gender 0.01 0.13 -0.25 0.27 1.00 22446 26737
## recreationalPreferencez_Gender:prestige_Sum 0.22 0.08 0.07 0.38 1.00 38926 31219
## recreationalPreferencez_Gender:leadership_Sum 0.04 0.11 -0.17 0.25 1.00 30199 28029
## recreationalPreferencez_Gender:PNI_Sum_z -0.47 0.13 -0.72 -0.21 1.00 23733 27154
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 0.88 0.04 0.81 0.96 1.00 56456 30027
## sigma_financialPreferencez 0.98 0.04 0.90 1.07 1.00 63236 29337
## sigma_socialPreferencez 0.89 0.04 0.82 0.97 1.00 64818 29922
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.86 1.02 1.00 52515 32043
## sigma_recreationalPreferencez 0.93 0.04 0.85 1.01 1.00 55925 30355
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 0.35 0.05 0.24 0.45 1.00 53344 30176
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06 0.01 0.24 1.00 53336 31439
## rescor(financialPreferencez,socialPreferencez) 0.25 0.06 0.14 0.36 1.00 52329 32127
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.51 0.05 0.41 0.59 1.00 45629 32340
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.22 0.06 0.10 0.33 1.00 48588 32710
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05 0.20 0.42 1.00 60431 31514
## rescor(ethicalPreferencez,recreationalPreferencez) 0.22 0.06 0.10 0.33 1.00 47563 32921
## rescor(financialPreferencez,recreationalPreferencez) 0.24 0.06 0.13 0.35 1.00 48568 31643
## rescor(socialPreferencez,recreationalPreferencez) 0.39 0.05 0.28 0.49 1.00 52835 31501
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05 0.35 0.54 1.00 54843 30774
##
## 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).
m2_int_hdi <- bayestestR::hdi(m2_int, effects = "fixed", component = "conditional", ci = .95)
kable(m2_int_hdi[sign(m2_int_hdi$CI_low) == sign(m2_int_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>% kable_styling(full_width = T) %>% remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_ethicalPreferencez_Gender
|
0.95
|
0.08
|
0.50
|
|
b_ethicalPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_socialPreferencez_Intercept
|
0.95
|
0.59
|
1.54
|
|
b_socialPreferencez_Gender
|
0.95
|
-0.65
|
-0.22
|
|
b_socialPreferencez_Age
|
0.95
|
-0.03
|
0.00
|
|
b_healthAndSafetyPreferencez_prestige_Sum
|
0.95
|
-1.06
|
-0.32
|
|
b_healthAndSafetyPreferencez_PNI_Sum_z
|
0.95
|
0.10
|
0.99
|
|
b_healthAndSafetyPreferencez_Age
|
0.95
|
-0.03
|
0.00
|
|
b_healthAndSafetyPreferencez_Gender:prestige_Sum
|
0.95
|
0.07
|
0.53
|
|
b_healthAndSafetyPreferencez_Gender:PNI_Sum_z
|
0.95
|
-0.53
|
0.00
|
|
b_recreationalPreferencez_Gender
|
0.95
|
0.00
|
0.45
|
|
b_recreationalPreferencez_prestige_Sum
|
0.95
|
-0.82
|
-0.36
|
|
b_recreationalPreferencez_PNI_Sum_z
|
0.95
|
0.33
|
1.19
|
|
b_recreationalPreferencez_Age
|
0.95
|
-0.04
|
-0.02
|
|
b_recreationalPreferencez_Gender:prestige_Sum
|
0.95
|
0.07
|
0.38
|
|
b_recreationalPreferencez_Gender:PNI_Sum_z
|
0.95
|
-0.72
|
-0.21
|
# plot(m2_int, ask = FALSE)
m4 <- brm(mvbind(dominance_Sum, prestige_Sum, leadership_Sum) ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, prior = prior_m4,
save_pars = save_pars(all = TRUE), backend = "cmdstanr"
)
saveRDS(m4, "m4.rds")
m7_fixef <- fixef(m7_DoPL_DOSPERT)
m4_int_gender <- brm(mvbind(dominance_Sum, prestige_Sum, leadership_Sum) ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + PNI_Sum_z*Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, prior = prior_m4_int_gender,
save_pars = save_pars(all = TRUE), backend = "cmdstanr"
)
saveRDS(m4_int_gender, "m4_int_gender.rds")
summary(m4)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: dominance_Sum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age
## prestige_Sum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age
## leadership_Sum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## 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 Bulk_ESS Tail_ESS
## dominanceSum_Intercept -0.38 0.24 -0.85 0.09 1.00 53318 29544
## prestigeSum_Intercept 0.49 0.25 -0.01 0.99 1.00 53433 29684
## leadershipSum_Intercept -0.04 0.26 -0.54 0.47 1.00 50007 30074
## dominanceSum_ethicalPreference_z 0.25 0.06 0.13 0.37 1.00 53819 29932
## dominanceSum_financialPreference_z -0.18 0.02 -0.21 -0.14 1.00 64165 27283
## dominanceSum_socialPreference_z -0.07 0.06 -0.19 0.05 1.00 47382 30378
## dominanceSum_healthAndSafetyPreference_z 0.04 0.05 -0.06 0.13 1.00 53320 28843
## dominanceSum_recreationalPreference_z 0.05 0.06 -0.06 0.16 1.00 54941 28500
## dominanceSum_PNI_Sum_z 0.44 0.06 0.33 0.56 1.00 51802 29625
## dominanceSum_Gender 0.27 0.11 0.05 0.50 1.00 49194 30708
## dominanceSum_Age -0.00 0.01 -0.01 0.01 1.00 53189 30116
## prestigeSum_ethicalPreference_z -0.03 0.07 -0.17 0.10 1.00 47042 30439
## prestigeSum_financialPreference_z 0.06 0.06 -0.05 0.18 1.00 54571 28624
## prestigeSum_socialPreference_z -0.24 0.01 -0.27 -0.21 1.00 63867 26771
## prestigeSum_healthAndSafetyPreference_z -0.06 0.06 -0.18 0.06 1.00 50923 29456
## prestigeSum_recreationalPreference_z -0.07 0.06 -0.18 0.05 1.00 54568 29650
## prestigeSum_PNI_Sum_z 0.51 0.06 0.39 0.63 1.00 51650 29241
## prestigeSum_Gender -0.14 0.12 -0.38 0.09 1.00 51286 29551
## prestigeSum_Age -0.01 0.01 -0.02 0.00 1.00 50262 31360
## leadershipSum_ethicalPreference_z -0.17 0.07 -0.30 -0.03 1.00 47061 29654
## leadershipSum_financialPreference_z 0.05 0.05 -0.06 0.15 1.00 54406 27849
## leadershipSum_socialPreference_z 0.03 0.05 -0.08 0.13 1.00 51457 28381
## leadershipSum_healthAndSafetyPreference_z 0.04 0.06 -0.07 0.16 1.00 51853 30577
## leadershipSum_recreationalPreference_z -0.04 0.05 -0.13 0.05 1.00 55486 28298
## leadershipSum_PNI_Sum_z 0.33 0.06 0.21 0.45 1.00 47641 29268
## leadershipSum_Gender -0.09 0.12 -0.32 0.15 1.00 48322 29732
## leadershipSum_Age 0.01 0.01 -0.01 0.02 1.00 49673 30120
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_dominanceSum 0.87 0.04 0.80 0.95 1.00 55680 27361
## sigma_prestigeSum 0.95 0.04 0.88 1.04 1.00 51474 28543
## sigma_leadershipSum 0.95 0.04 0.87 1.04 1.00 49241 30098
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 0.17 0.06 0.04 0.29 1.00 44587 30591
## rescor(dominanceSum,leadershipSum) 0.29 0.06 0.17 0.40 1.00 44324 30158
## rescor(prestigeSum,leadershipSum) 0.43 0.05 0.32 0.52 1.00 46035 28957
##
## 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).
summary(m4_int_gender)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: dominance_Sum ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + PNI_Sum_z * Gender + Age
## prestige_Sum ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + PNI_Sum_z * Gender + Age
## leadership_Sum ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + PNI_Sum_z * Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## 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 Bulk_ESS Tail_ESS
## dominanceSum_Intercept -0.48 0.23 -0.94 -0.03 1.00 67805 28486
## prestigeSum_Intercept 0.16 0.24 -0.31 0.64 1.00 59904 27068
## leadershipSum_Intercept -0.22 0.25 -0.71 0.27 1.00 61014 30136
## dominanceSum_ethicalPreference_z 0.05 0.16 -0.26 0.36 1.00 35155 28773
## dominanceSum_Gender 0.30 0.11 0.08 0.52 1.00 57737 28527
## dominanceSum_financialPreference_z -0.19 0.02 -0.22 -0.16 1.00 70210 28124
## dominanceSum_socialPreference_z -0.11 0.14 -0.39 0.18 1.00 32281 28617
## dominanceSum_healthAndSafetyPreference_z -0.05 0.06 -0.17 0.07 1.00 41923 30574
## dominanceSum_recreationalPreference_z -0.10 0.10 -0.30 0.10 1.00 33948 28463
## dominanceSum_PNI_Sum_z 0.73 0.17 0.39 1.06 1.00 28180 27644
## dominanceSum_Age 0.00 0.01 -0.01 0.01 1.00 67383 27434
## dominanceSum_ethicalPreference_z:Gender 0.08 0.10 -0.11 0.27 1.00 34063 27880
## dominanceSum_Gender:financialPreference_z 0.11 0.04 0.04 0.18 1.00 67186 26517
## dominanceSum_Gender:socialPreference_z 0.04 0.10 -0.15 0.23 1.00 31272 28385
## dominanceSum_Gender:healthAndSafetyPreference_z 0.10 0.06 -0.01 0.21 1.00 40919 31285
## dominanceSum_Gender:recreationalPreference_z 0.08 0.07 -0.06 0.21 1.00 33233 28701
## dominanceSum_Gender:PNI_Sum_z -0.19 0.11 -0.40 0.02 1.00 28221 27421
## prestigeSum_ethicalPreference_z -0.04 0.15 -0.34 0.26 1.00 30409 28755
## prestigeSum_Gender 0.04 0.11 -0.19 0.26 1.00 46722 29554
## prestigeSum_financialPreference_z -0.02 0.12 -0.26 0.22 1.00 34113 29108
## prestigeSum_socialPreference_z -0.25 0.01 -0.28 -0.22 1.00 69063 27704
## prestigeSum_healthAndSafetyPreference_z -0.10 0.10 -0.30 0.10 1.00 37200 30174
## prestigeSum_recreationalPreference_z -0.16 0.10 -0.35 0.03 1.00 34624 28837
## prestigeSum_PNI_Sum_z 1.02 0.17 0.68 1.36 1.00 24610 26951
## prestigeSum_Age -0.01 0.01 -0.02 0.00 1.00 67893 28833
## prestigeSum_ethicalPreference_z:Gender 0.04 0.09 -0.15 0.22 1.00 29008 27913
## prestigeSum_Gender:financialPreference_z 0.05 0.08 -0.10 0.21 1.00 33236 28956
## prestigeSum_Gender:socialPreference_z 0.25 0.04 0.17 0.33 1.00 55379 28801
## prestigeSum_Gender:healthAndSafetyPreference_z -0.03 0.07 -0.17 0.12 1.00 36128 29883
## prestigeSum_Gender:recreationalPreference_z 0.02 0.07 -0.11 0.15 1.00 32912 28138
## prestigeSum_Gender:PNI_Sum_z -0.39 0.11 -0.60 -0.17 1.00 24205 27215
## leadershipSum_ethicalPreference_z -0.09 0.17 -0.43 0.24 1.00 32042 28923
## leadershipSum_Gender 0.00 0.12 -0.23 0.24 1.00 50626 29030
## leadershipSum_financialPreference_z -0.08 0.09 -0.25 0.10 1.00 35559 28104
## leadershipSum_socialPreference_z -0.15 0.07 -0.30 -0.01 1.00 38242 29965
## leadershipSum_healthAndSafetyPreference_z 0.04 0.10 -0.14 0.23 1.00 33978 28757
## leadershipSum_recreationalPreference_z -0.16 0.06 -0.28 -0.04 1.00 47799 30766
## leadershipSum_PNI_Sum_z 0.73 0.18 0.37 1.08 1.00 26329 27324
## leadershipSum_Age 0.01 0.01 -0.00 0.02 1.00 61372 29159
## leadershipSum_ethicalPreference_z:Gender -0.04 0.10 -0.24 0.17 1.00 30378 28243
## leadershipSum_Gender:financialPreference_z 0.11 0.06 -0.01 0.23 1.00 34759 28598
## leadershipSum_Gender:socialPreference_z 0.26 0.06 0.14 0.39 1.00 34480 29579
## leadershipSum_Gender:healthAndSafetyPreference_z -0.06 0.07 -0.19 0.08 1.00 32256 29093
## leadershipSum_Gender:recreationalPreference_z 0.10 0.05 -0.00 0.21 1.00 40400 29995
## leadershipSum_Gender:PNI_Sum_z -0.28 0.11 -0.50 -0.06 1.00 26340 27398
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_dominanceSum 0.85 0.04 0.78 0.92 1.00 63714 26427
## sigma_prestigeSum 0.89 0.04 0.81 0.96 1.00 56287 28101
## sigma_leadershipSum 0.92 0.04 0.85 1.00 1.00 60427 27782
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 0.14 0.06 0.03 0.26 1.00 51867 28446
## rescor(dominanceSum,leadershipSum) 0.27 0.06 0.15 0.38 1.00 52577 29982
## rescor(prestigeSum,leadershipSum) 0.37 0.05 0.27 0.47 1.00 57139 29315
##
## 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).
m4_hdi <- bayestestR::hdi(m4, effects = "fixed", component = "conditional", ci = .95)
kable(m4_hdi[sign(m4_hdi$CI_low) == sign(m4_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>% kable_styling(full_width = T) %>% remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_dominanceSum_ethicalPreference_z
|
0.95
|
0.13
|
0.37
|
|
b_dominanceSum_financialPreference_z
|
0.95
|
-0.21
|
-0.14
|
|
b_dominanceSum_PNI_Sum_z
|
0.95
|
0.33
|
0.56
|
|
b_dominanceSum_Gender
|
0.95
|
0.06
|
0.50
|
|
b_prestigeSum_socialPreference_z
|
0.95
|
-0.27
|
-0.21
|
|
b_prestigeSum_PNI_Sum_z
|
0.95
|
0.39
|
0.63
|
|
b_leadershipSum_ethicalPreference_z
|
0.95
|
-0.30
|
-0.03
|
|
b_leadershipSum_PNI_Sum_z
|
0.95
|
0.21
|
0.45
|
m4_int_gender_hdi <- bayestestR::hdi(m4_int_gender, effects = "fixed", component = "conditional", ci = .95)
kable(m4_int_gender_hdi[sign(m4_int_gender_hdi$CI_low) == sign(m4_int_gender_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>% kable_styling(full_width = T) %>% remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_dominanceSum_Intercept
|
0.95
|
-0.95
|
-0.04
|
|
b_dominanceSum_Gender
|
0.95
|
0.08
|
0.51
|
|
b_dominanceSum_financialPreference_z
|
0.95
|
-0.22
|
-0.16
|
|
b_dominanceSum_PNI_Sum_z
|
0.95
|
0.39
|
1.05
|
|
b_dominanceSum_Gender:financialPreference_z
|
0.95
|
0.04
|
0.18
|
|
b_prestigeSum_socialPreference_z
|
0.95
|
-0.28
|
-0.22
|
|
b_prestigeSum_PNI_Sum_z
|
0.95
|
0.68
|
1.36
|
|
b_prestigeSum_Gender:socialPreference_z
|
0.95
|
0.17
|
0.33
|
|
b_prestigeSum_Gender:PNI_Sum_z
|
0.95
|
-0.60
|
-0.17
|
|
b_leadershipSum_socialPreference_z
|
0.95
|
-0.29
|
0.00
|
|
b_leadershipSum_recreationalPreference_z
|
0.95
|
-0.28
|
-0.05
|
|
b_leadershipSum_PNI_Sum_z
|
0.95
|
0.36
|
1.07
|
|
b_leadershipSum_Gender:socialPreference_z
|
0.95
|
0.14
|
0.38
|
|
b_leadershipSum_Gender:PNI_Sum_z
|
0.95
|
-0.50
|
-0.05
|
m1_model <- bf(riskPerceptionSum ~ dominance_Sum + prestige_Sum + leadership_Sum)
m2_model <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum)
m3_model <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum)
mediation_model <- brm(m3_model + m1_model + m2_model + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
saveRDS(mediation_model, "mediation_model.rds")
m1_model.1 <- bf(riskPerceptionSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum)
m2_model.1 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum)
m3_model.1 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum)
mediation_model.1 <- brm(m3_model.1 + m1_model.1 + m2_model.1 + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
saveRDS(mediation_model.1, "mediation_model.1.rds")
m1_model.2 <- bf(riskPerceptionSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum)
m2_model.2 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum)
m3_model.2 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum)
mediation_model.2 <- brm(m3_model.2 + m1_model.2 + m2_model.2 + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
saveRDS(mediation_model.2, "mediation_model.2.rds")
m1_model.3 <- bf(riskPerceptionSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
m2_model.3 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
m3_model.3 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
mediation_model.3 <- brm(m3_model.3 + m1_model.3 + m2_model.3 + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
saveRDS(mediation_model.3, "mediation_model.3.rds")
m1_model.4 <- bf(riskPerceptionSum ~ dominance_Sum * Gender + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
m2_model.4 <- bf(riskBenefitSum ~ dominance_Sum * Gender + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
m3_model.4 <- bf(riskSum ~ dominance_Sum * Gender + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
mediation_model.4 <- brm(m3_model.4 + m1_model.4 + m2_model.4 + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
m1_model.5 <- bf(riskPerceptionSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum + Gender + PNI_Sum + Age)
m2_model.5 <- bf(riskBenefitSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum + Gender + PNI_Sum + Age)
m3_model.5 <- bf(riskSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum + Gender + PNI_Sum + Age)
mediation_model.5 <- brm(m3_model.5 + m1_model.5 + m2_model.5 + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
m1_model.6 <- bf(riskPerceptionSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Gender + PNI_Sum + Age)
m2_model.6 <- bf(riskBenefitSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Gender + PNI_Sum + Age)
m3_model.6 <- bf(riskSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Gender + PNI_Sum + Age)
mediation_model.6 <- brm(m3_model.6 + m1_model.6 + m2_model.6 + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
saveRDS(mediation_model.6, "mediation_model.6.rds")
m1_model.4 <- bf(riskPerceptionSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
m2_model.4 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
m3_model.4 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum + Age)
mediation_model.4 <- brm(m3_model.4 + m1_model.4 + m2_model.4 + set_rescor(FALSE), warmup = 1000, iter = 10000, data = d1, backend = "cmdstanr", save_pars = save_pars(all = TRUE))
saveRDS(mediation_model.5, "mediation_model.5.rds")
mediation_test.2 <- loo(mediation_model.5, mediation_model.6)
mediation_comparison.2 <- bayesfactor_models(mediation_model.5, mediation_model.6, denominator = 2)
d1 <- Experiment_2_demographics_Gender[complete.cases(Experiment_2_demographics_Gender), ]
mediation_test.1 <- loo(mediation_model, mediation_model.1, mediation_model.2, mediation_model.3, mediation_model.4)
mediation_comparison <- bayesfactor_models(mediation_model, mediation_model.1, mediation_model.2, mediation_model.3, mediation_model.4, denominator = 5)
print(mediation_test.1)
## Output of model 'mediation_model':
##
## Computed from 36000 by 279 log-likelihood matrix
##
## Estimate SE
## elpd_loo -3597.0 25.9
## p_loo 14.7 1.2
## looic 7193.9 51.7
## ------
## Monte Carlo SE of elpd_loo is 0.0.
##
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
##
## Output of model 'mediation_model.1':
##
## Computed from 36000 by 279 log-likelihood matrix
##
## Estimate SE
## elpd_loo -3595.2 25.7
## p_loo 17.4 1.3
## looic 7190.5 51.4
## ------
## Monte Carlo SE of elpd_loo is 0.0.
##
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
##
## Output of model 'mediation_model.2':
##
## Computed from 36000 by 279 log-likelihood matrix
##
## Estimate SE
## elpd_loo -3591.2 26.1
## p_loo 20.7 1.6
## looic 7182.4 52.1
## ------
## Monte Carlo SE of elpd_loo is 0.0.
##
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
##
## Output of model 'mediation_model.3':
##
## Computed from 36000 by 279 log-likelihood matrix
##
## Estimate SE
## elpd_loo -3581.0 26.6
## p_loo 23.2 1.7
## looic 7162.1 53.2
## ------
## Monte Carlo SE of elpd_loo is 0.0.
##
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
##
## Output of model 'mediation_model.4':
##
## Computed from 36000 by 279 log-likelihood matrix
##
## Estimate SE
## elpd_loo -3582.9 26.6
## p_loo 26.2 1.9
## looic 7165.9 53.2
## ------
## Monte Carlo SE of elpd_loo is 0.0.
##
## All Pareto k estimates are good (k < 0.5).
## See help('pareto-k-diagnostic') for details.
##
## Model comparisons:
## elpd_diff se_diff
## mediation_model.3 0.0 0.0
## mediation_model.4 -1.9 1.5
## mediation_model.2 -10.1 6.3
## mediation_model.1 -14.2 7.7
## mediation_model -15.9 8.1
print(mediation_comparison)
## Bayes Factors for Model Comparison
##
## Model BF
## [1] 3.45e-12
## [2] 1.23e-12
## [3] 2.55e-07
## [4] 0.002
##
## * Against Denominator: [5]
## * Bayes Factor Type: marginal likelihoods (bridgesampling)
print(mediation_comparison.2)
## Bayes Factors for Model Comparison
##
## Model BF
## [1] 0.002
##
## * Against Denominator: [2]
## * Bayes Factor Type: marginal likelihoods (bridgesampling)
library(blavaan)
model.1 <- "Perception =~ prestige_Sum + leadership_Sum + dominance_Sum
Preference =~ ethicalPreference_z + financialPreference_z + socialPreference_z + recreationalPreference_z + healthAndSafetyPreference_z
Perception ~ Preference"
model.2 <- "PNI_2 =~ exploitativeness_Sum_z + grandiose_fantasy_Sum_z + ssse_Sum_z
PNI_3 =~ cse_Sum_z + devaluing_Sum_z + entitlement_rage_Sum_z + hts_Sum_z
PNI_2 ~ PNI_3"
model.3 <- "Preferences =~ dominance_Sum + PNI_Sum_z + prestige_Sum + leadership_Sum"
model.4 <- "Preference =~ prestige_Sum + leadership_Sum + dominance_Sum
Preference2 =~ exploitativeness_Sum_z + grandiose_fantasy_Sum_z + ssse_Sum_z
Preference ~ Preference2"
fit_bayes <- blavaan(model = model.1, data = Experiment_2_demographics_Gender, target = "cmdstanr", auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE, bcontrol = list(cores = parallel::detectCores()))
fit_bayes.2 <- bsem(model = model.2, data = Experiment_2_demographics_Gender, n.chains = 4, seed = 1234, target = "cmdstanr", auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE, bcontrol = list(cores = parallel::detectCores()))
fit_bayes.3 <- bsem(model.1, data = Experiment_2_demographics_Gender, target = "cmdstanr", auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE, bcontrol = list(cores = parallel::detectCores()))
fit_bayes.4 <- bsem(model.4, data = Experiment_2_demographics_Gender, target = "cmdstanr", auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE, bcontrol = list(cores = parallel::detectCores()))
summary(fit_bayes)
## blavaan (0.4-4.996) results of 1000 samples after 500 adapt/burnin iterations
##
## Number of observations 279
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value NA 0.000
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Perception =~
## prestige_Sum 1.000
## leadership_Sum 1.089 0.272 0.686 1.778 1.026 normal(0,10)
## dominance_Sum 0.717 0.159 0.421 1.052 1.000 normal(0,10)
## Preference =~
## ethiclPrfrnc_z 1.000
## finnclPrfrnc_z 0.674 0.133 0.434 0.958 1.000 normal(0,10)
## socialPrfrnc_z 0.640 0.141 0.388 0.948 1.001 normal(0,10)
## rcrtnlPrfrnc_z 1.044 0.162 0.772 1.410 1.002 normal(0,10)
## hlthAndSftyPr_ 1.387 0.172 1.095 1.774 1.001 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Perception ~
## Preference 0.161 0.105 -0.034 0.369 1.003 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .prestige_Sum 0.000
## .leadership_Sum 0.000
## .dominance_Sum 0.000
## .ethiclPrfrnc_z 0.000
## .finnclPrfrnc_z 0.000
## .socialPrfrnc_z 0.000
## .rcrtnlPrfrnc_z 0.000
## .hlthAndSftyPr_ 0.000
## .Perception 0.000
## Preference 0.000
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .prestige_Sum 0.588 0.111 0.351 0.798 1.007 gamma(1,.5)[sd]
## .leadership_Sum 0.524 0.130 0.180 0.728 1.025 gamma(1,.5)[sd]
## .dominance_Sum 0.810 0.088 0.649 0.997 1.006 gamma(1,.5)[sd]
## .ethiclPrfrnc_z 0.605 0.066 0.483 0.746 1.001 gamma(1,.5)[sd]
## .finnclPrfrnc_z 0.819 0.077 0.682 0.980 1.000 gamma(1,.5)[sd]
## .socialPrfrnc_z 0.834 0.078 0.694 0.997 1.000 gamma(1,.5)[sd]
## .rcrtnlPrfrnc_z 0.624 0.068 0.500 0.762 1.001 gamma(1,.5)[sd]
## .hlthAndSftyPr_ 0.327 0.079 0.172 0.483 0.999 gamma(1,.5)[sd]
## .Perception 0.430 0.121 0.228 0.707 1.004 gamma(1,.5)[sd]
## Preference 0.359 0.078 0.221 0.526 1.001 gamma(1,.5)[sd]
summary(fit_bayes.2)
## blavaan (0.4-4.996) results of 1000 samples after 500 adapt/burnin iterations
##
## Number of observations 279
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value NA 0.259
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PNI_2 =~
## explttvnss_Sm_ 1.000
## grnds_fntsy_S_ 1.786 0.445 1.155 2.835 1.005 normal(0,10)
## ssse_Sum_z 2.002 0.489 1.305 3.130 1.007 normal(0,10)
## PNI_3 =~
## cse_Sum_z 1.000
## devaluing_Sm_z 1.047 0.090 0.880 1.235 1.000 normal(0,10)
## enttlmnt_rg_S_ 1.059 0.090 0.891 1.250 1.000 normal(0,10)
## hts_Sum_z 0.966 0.089 0.804 1.149 1.001 normal(0,10)
##
## Covariances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## PNI_2 ~~
## PNI_3 0.195 0.047 0.111 0.295 1.003 lkj_corr(1)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .explttvnss_Sm_ 0.000
## .grnds_fntsy_S_ 0.000
## .ssse_Sum_z 0.000
## .cse_Sum_z 0.000
## .devaluing_Sm_z 0.000
## .enttlmnt_rg_S_ 0.000
## .hts_Sum_z 0.000
## PNI_2 0.000
## PNI_3 0.000
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .explttvnss_Sm_ 0.861 0.075 0.720 1.015 0.999 gamma(1,.5)[sd]
## .grnds_fntsy_S_ 0.610 0.074 0.475 0.760 0.999 gamma(1,.5)[sd]
## .ssse_Sum_z 0.486 0.074 0.346 0.645 1.001 gamma(1,.5)[sd]
## .cse_Sum_z 0.451 0.051 0.356 0.555 1.001 gamma(1,.5)[sd]
## .devaluing_Sm_z 0.409 0.047 0.324 0.510 0.999 gamma(1,.5)[sd]
## .enttlmnt_rg_S_ 0.395 0.047 0.310 0.494 1.000 gamma(1,.5)[sd]
## .hts_Sum_z 0.492 0.052 0.398 0.598 1.000 gamma(1,.5)[sd]
## PNI_2 0.139 0.054 0.052 0.264 1.001 gamma(1,.5)[sd]
## PNI_3 0.563 0.084 0.412 0.743 1.001 gamma(1,.5)[sd]
summary(fit_bayes.3)
## blavaan (0.4-4.996) results of 1000 samples after 500 adapt/burnin iterations
##
## Number of observations 279
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value NA 0.000
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Perception =~
## prestige_Sum 1.000
## leadership_Sum 1.080 0.220 0.716 1.566 1.002 normal(0,10)
## dominance_Sum 0.723 0.155 0.453 1.054 1.001 normal(0,10)
## Preference =~
## ethiclPrfrnc_z 1.000
## finnclPrfrnc_z 0.668 0.130 0.428 0.948 1.000 normal(0,10)
## socialPrfrnc_z 0.635 0.134 0.399 0.924 1.001 normal(0,10)
## rcrtnlPrfrnc_z 1.038 0.153 0.774 1.388 1.002 normal(0,10)
## hlthAndSftyPr_ 1.385 0.169 1.095 1.766 1.000 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Perception ~
## Preference 0.160 0.104 -0.041 0.372 1.000 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .prestige_Sum -0.000 0.062 -0.118 0.121 1.000 normal(0,32)
## .leadership_Sum 0.001 0.061 -0.118 0.117 1.000 normal(0,32)
## .dominance_Sum 0.002 0.061 -0.116 0.127 1.000 normal(0,32)
## .ethiclPrfrnc_z -0.019 0.058 -0.134 0.095 1.001 normal(0,32)
## .finnclPrfrnc_z -0.004 0.059 -0.119 0.110 1.000 normal(0,32)
## .socialPrfrnc_z -0.037 0.060 -0.152 0.078 1.000 normal(0,32)
## .rcrtnlPrfrnc_z -0.013 0.060 -0.131 0.105 1.000 normal(0,32)
## .hlthAndSftyPr_ -0.019 0.060 -0.137 0.098 0.999 normal(0,32)
## .Perception 0.000
## Preference 0.000
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .prestige_Sum 0.593 0.100 0.383 0.778 1.002 gamma(1,.5)[sd]
## .leadership_Sum 0.531 0.110 0.293 0.736 1.000 gamma(1,.5)[sd]
## .dominance_Sum 0.811 0.085 0.658 0.985 1.001 gamma(1,.5)[sd]
## .ethiclPrfrnc_z 0.604 0.067 0.488 0.742 1.001 gamma(1,.5)[sd]
## .finnclPrfrnc_z 0.823 0.076 0.685 0.981 1.000 gamma(1,.5)[sd]
## .socialPrfrnc_z 0.836 0.078 0.697 0.990 0.999 gamma(1,.5)[sd]
## .rcrtnlPrfrnc_z 0.625 0.068 0.494 0.762 1.002 gamma(1,.5)[sd]
## .hlthAndSftyPr_ 0.326 0.077 0.172 0.474 1.002 gamma(1,.5)[sd]
## .Perception 0.428 0.111 0.243 0.670 1.003 gamma(1,.5)[sd]
## Preference 0.361 0.075 0.228 0.514 1.002 gamma(1,.5)[sd]
summary(fit_bayes.4)
## blavaan (0.4-4.996) results of 1000 samples after 500 adapt/burnin iterations
##
## Number of observations 279
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value NA 0.000
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Preference =~
## prestige_Sum 1.000
## leadership_Sum 0.963 0.121 0.749 1.219 0.999 normal(0,10)
## dominance_Sum 0.703 0.117 0.485 0.945 1.000 normal(0,10)
## Preference2 =~
## explttvnss_Sm_ 1.000
## grnds_fntsy_S_ 0.828 0.153 0.562 1.162 1.001 normal(0,10)
## ssse_Sum_z 0.925 0.160 0.646 1.284 1.002 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Preference ~
## Preference2 1.126 0.170 0.845 1.509 1.000 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .prestige_Sum 0.002 0.061 -0.121 0.120 0.999 normal(0,32)
## .leadership_Sum 0.002 0.060 -0.115 0.116 0.999 normal(0,32)
## .dominance_Sum 0.005 0.061 -0.110 0.129 0.999 normal(0,32)
## .explttvnss_Sm_ 0.009 0.059 -0.106 0.126 1.000 normal(0,32)
## .grnds_fntsy_S_ -0.011 0.061 -0.127 0.109 1.000 normal(0,32)
## .ssse_Sum_z 0.010 0.059 -0.103 0.126 0.999 normal(0,32)
## .Preference 0.000
## Preference2 0.000
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .prestige_Sum 0.538 0.069 0.411 0.681 1.001 gamma(1,.5)[sd]
## .leadership_Sum 0.571 0.066 0.447 0.708 1.000 gamma(1,.5)[sd]
## .dominance_Sum 0.792 0.077 0.653 0.952 0.999 gamma(1,.5)[sd]
## .explttvnss_Sm_ 0.618 0.071 0.483 0.771 1.001 gamma(1,.5)[sd]
## .grnds_fntsy_S_ 0.756 0.077 0.613 0.912 1.000 gamma(1,.5)[sd]
## .ssse_Sum_z 0.666 0.070 0.537 0.814 0.999 gamma(1,.5)[sd]
## .Preference 0.020 0.026 0.000 0.093 1.001 gamma(1,.5)[sd]
## Preference2 0.384 0.086 0.231 0.566 1.000 gamma(1,.5)[sd]
graph_sem(fit_bayes)
graph_sem(fit_bayes.2)
graph_sem(fit_bayes.3)
graph_sem(fit_bayes.4)
Experiment_2_demographics_Gender$Gender <- as.numeric(Experiment_2_demographics_Gender$Gender)
model.5 <- "Risk_Benefit =~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
Risk_Sum =~ dominance_Sum*Gender + prestige_Sum*Gender + leadership_Sum*Gender + Age
Risk_Benefit ~ Risk_Sum"
fit_bayes.5 <- bsem(model.5, data = Experiment_2_demographics_Gender, n.chains = 4, seed = 1234, target = "cmdstanr", auto.var=TRUE, auto.fix.first=TRUE,
auto.cov.lv.x=TRUE, bcontrol = list(cores = parallel::detectCores()))
summary(fit_bayes.5)
## ** WARNING ** blavaan (0.4-4.996) did NOT converge after 500 adapt+burnin iterations
## ** WARNING ** Proceed with caution
##
## Number of observations 279
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value NA 0.588
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Risk_Benefit =~
## dmnnc_S 1.000
## prstg_S 2.239 0.661 1.263 3.577 1.174 normal(0,10)
## ldrsh_S 1.763 0.424 1.086 2.527 1.213 normal(0,10)
## Gender 0.149 0.425 -0.513 0.899 2.000 normal(0,10)
## Age -4.660 2.536 -10.108 -0.228 1.086 normal(0,10)
## Risk_Sum =~
## Gender (dm_S) 1.000
## Age 2.521 8.196 -15.747 18.447 1.013 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Risk_Benefit ~
## Risk_Sum 0.196 5.332 -11.198 12.991 1.006 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .dominance_Sum 0.008 0.051 -0.101 0.116 1.001 normal(0,32)
## .prestige_Sum -0.023 0.071 -0.126 0.110 1.354 normal(0,32)
## .leadership_Sum -0.000 0.056 -0.111 0.123 1.021 normal(0,32)
## .Gender 1.562 0.029 1.499 1.613 1.083 normal(0,32)
## .Age 29.533 0.528 28.402 30.572 1.018 normal(0,32)
## .Risk_Benefit 0.000
## Risk_Sum 0.000
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .dominance_Sum 0.903 0.123 0.701 1.099 1.622 gamma(1,.5)[sd]
## .prestige_Sum 0.408 0.140 0.078 0.668 1.076 gamma(1,.5)[sd]
## .leadership_Sum 0.663 0.113 0.418 0.826 1.184 gamma(1,.5)[sd]
## .Gender 0.220 0.045 0.144 0.287 1.839 gamma(1,.5)[sd]
## .Age 93.033 7.439 78.710 109.827 1.002 gamma(1,.5)[sd]
## .Risk_Benefit 0.085 0.065 0.001 0.241 1.069 gamma(1,.5)[sd]
## Risk_Sum 0.052 0.071 0.000 0.206 2.391 gamma(1,.5)[sd]
graph_sem(fit_bayes.5)
m5 <- brm(mvbind(riskSum, riskPerceptionSum, riskBenefitSum) ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age, data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 500, save_pars = save_pars(all = TRUE), backend = "cmdstanr",
prior = prior_m5)
summary(m5)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: riskSum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age
## riskPerceptionSum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age
## riskBenefitSum ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + PNI_Sum_z + Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 10000; warmup = 500; thin = 1;
## total post-warmup draws = 38000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## riskSum_Intercept 9.88 1.26 7.44 12.32 1.00 74984 30877
## riskPerceptionSum_Intercept 13.84 1.24 11.42 16.25 1.00 75705 29503
## riskBenefitSum_Intercept 7.60 0.97 5.70 9.50 1.00 61831 29383
## riskSum_ethicalPreference_z -0.07 0.23 -0.52 0.39 1.00 87586 26762
## riskSum_financialPreference_z -0.13 0.07 -0.26 0.00 1.00 93732 26016
## riskSum_socialPreference_z -0.05 0.10 -0.24 0.13 1.00 90972 25986
## riskSum_healthAndSafetyPreference_z -0.09 0.19 -0.48 0.29 1.00 90521 26870
## riskSum_recreationalPreference_z -0.15 0.14 -0.41 0.12 1.00 93795 27292
## riskSum_PNI_Sum_z 1.27 0.72 -0.16 2.70 1.00 69156 32057
## riskSum_Gender 0.29 0.50 -0.69 1.27 1.00 88152 27080
## riskSum_Age -0.03 0.01 -0.04 -0.01 1.00 99502 26403
## riskPerceptionSum_ethicalPreference_z -0.46 0.20 -0.85 -0.07 1.00 85921 27376
## riskPerceptionSum_financialPreference_z -0.39 0.25 -0.89 0.11 1.00 92890 26681
## riskPerceptionSum_socialPreference_z -0.03 0.10 -0.23 0.16 1.00 98535 27406
## riskPerceptionSum_healthAndSafetyPreference_z -0.22 0.02 -0.26 -0.19 1.00 92754 25328
## riskPerceptionSum_recreationalPreference_z -6.41 0.84 -8.04 -4.76 1.00 86262 29362
## riskPerceptionSum_PNI_Sum_z -0.76 0.83 -2.39 0.88 1.00 77605 31833
## riskPerceptionSum_Gender -0.63 0.34 -1.30 0.04 1.00 92810 26161
## riskPerceptionSum_Age -0.01 0.02 -0.05 0.02 1.00 94691 26082
## riskBenefitSum_ethicalPreference_z 0.33 0.12 0.09 0.56 1.00 94342 27225
## riskBenefitSum_financialPreference_z 0.34 0.12 0.10 0.58 1.00 98037 27324
## riskBenefitSum_socialPreference_z 0.10 0.06 -0.02 0.22 1.00 95641 26451
## riskBenefitSum_healthAndSafetyPreference_z 0.18 0.09 0.01 0.35 1.00 84259 25464
## riskBenefitSum_recreationalPreference_z 0.14 0.08 -0.02 0.29 1.00 88677 26790
## riskBenefitSum_PNI_Sum_z 0.82 0.65 -0.44 2.09 1.00 65544 32683
## riskBenefitSum_Gender -0.05 0.05 -0.13 0.04 1.00 97032 26230
## riskBenefitSum_Age -0.00 0.00 -0.01 0.00 1.00 38768 25210
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_riskSum 29.57 0.40 28.79 30.36 1.00 65122 30811
## sigma_riskPerceptionSum 38.23 0.45 37.35 39.10 1.00 62131 30184
## sigma_riskBenefitSum 25.74 0.37 25.01 26.48 1.00 57536 27683
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 0.79 0.01 0.76 0.81 1.00 70236 29641
## rescor(riskSum,riskBenefitSum) 0.88 0.01 0.86 0.89 1.00 70664 31330
## rescor(riskPerceptionSum,riskBenefitSum) 0.82 0.01 0.80 0.84 1.00 70508 31384
##
## 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).
m5_hdi <- bayestestR::hdi(m5, effects = "fixed", component = "conditional", ci = .95)
kable(m5_hdi[sign(m5_hdi$CI_low) == sign(m5_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>% kable_styling(full_width = T) %>% remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_riskSum_Intercept
|
0.95
|
7.44
|
12.32
|
|
b_riskSum_Age
|
0.95
|
-0.04
|
-0.01
|
|
b_riskPerceptionSum_Intercept
|
0.95
|
11.38
|
16.21
|
|
b_riskPerceptionSum_ethicalPreference_z
|
0.95
|
-0.84
|
-0.07
|
|
b_riskPerceptionSum_healthAndSafetyPreference_z
|
0.95
|
-0.26
|
-0.19
|
|
b_riskPerceptionSum_recreationalPreference_z
|
0.95
|
-8.06
|
-4.78
|
|
b_riskBenefitSum_Intercept
|
0.95
|
5.64
|
9.44
|
|
b_riskBenefitSum_ethicalPreference_z
|
0.95
|
0.10
|
0.57
|
|
b_riskBenefitSum_financialPreference_z
|
0.95
|
0.10
|
0.58
|
|
b_riskBenefitSum_healthAndSafetyPreference_z
|
0.95
|
0.01
|
0.35
|
m6 <- brm(mvbind(riskSum, riskPerceptionSum, riskBenefitSum) ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z +Gender + Age, data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 500, backend = "cmdstanr", save_pars = save_pars(all = TRUE), prior = prior_m6)
summary(m6)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## riskPerceptionSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z + Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 10000; warmup = 500; thin = 1;
## total post-warmup draws = 38000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## riskSum_Intercept 9.37 1.40 6.62 12.12 1.00 65537 28803
## riskPerceptionSum_Intercept 14.68 1.03 12.62 16.71 1.00 64628 29442
## riskBenefitSum_Intercept 9.07 1.56 6.01 12.14 1.00 70273 28908
## riskSum_dominance_Sum 1.04 0.51 0.03 2.05 1.00 74404 28973
## riskSum_prestige_Sum 0.16 0.34 -0.49 0.82 1.00 90257 28481
## riskSum_leadership_Sum -0.21 0.17 -0.56 0.13 1.00 84193 26475
## riskSum_PNI_Sum_z 4.22 1.80 0.69 7.76 1.00 35309 30198
## riskSum_Gender 0.37 0.52 -0.65 1.38 1.00 87426 27218
## riskSum_Age -0.02 0.02 -0.06 0.02 1.00 83454 27070
## riskPerceptionSum_dominance_Sum -3.09 0.85 -4.76 -1.41 1.00 72761 29025
## riskPerceptionSum_prestige_Sum 0.06 0.40 -0.73 0.85 1.00 76901 26090
## riskPerceptionSum_leadership_Sum -0.16 0.23 -0.61 0.30 1.00 85140 26885
## riskPerceptionSum_PNI_Sum_z 2.09 2.35 -2.55 6.67 1.00 36111 31818
## riskPerceptionSum_Gender -0.64 0.20 -1.04 -0.24 1.00 73186 26642
## riskPerceptionSum_Age -0.04 0.00 -0.05 -0.03 1.00 47711 24191
## riskBenefitSum_dominance_Sum 0.56 0.41 -0.24 1.35 1.00 71856 28518
## riskBenefitSum_prestige_Sum -0.30 0.32 -0.93 0.32 1.00 73789 28329
## riskBenefitSum_leadership_Sum -0.20 0.17 -0.54 0.14 1.00 76745 27710
## riskBenefitSum_PNI_Sum_z 3.50 1.58 0.39 6.61 1.00 35672 29118
## riskBenefitSum_Gender 0.17 0.62 -1.05 1.39 1.00 83654 26228
## riskBenefitSum_Age -0.06 0.03 -0.11 -0.00 1.00 82590 27147
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_riskSum 29.56 0.40 28.79 30.35 1.00 55553 30977
## sigma_riskPerceptionSum 38.41 0.45 37.55 39.29 1.00 59805 30672
## sigma_riskBenefitSum 26.00 0.38 25.26 26.74 1.00 51632 30961
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 0.78 0.01 0.76 0.80 1.00 64266 29703
## rescor(riskSum,riskBenefitSum) 0.87 0.01 0.85 0.89 1.00 63802 29862
## rescor(riskPerceptionSum,riskBenefitSum) 0.78 0.01 0.76 0.81 1.00 79716 27954
##
## 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).
m6_hdi <- bayestestR::hdi(m6, effects = "fixed", component = "conditional", ci = .95)
kable(m6_hdi[sign(m6_hdi$CI_low) == sign(m6_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>% kable_styling(full_width = T) %>% remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_riskSum_Intercept
|
0.95
|
6.64
|
12.13
|
|
b_riskSum_dominance_Sum
|
0.95
|
0.05
|
2.06
|
|
b_riskSum_PNI_Sum_z
|
0.95
|
0.62
|
7.67
|
|
b_riskPerceptionSum_Intercept
|
0.95
|
12.69
|
16.77
|
|
b_riskPerceptionSum_dominance_Sum
|
0.95
|
-4.78
|
-1.44
|
|
b_riskPerceptionSum_Gender
|
0.95
|
-1.05
|
-0.24
|
|
b_riskPerceptionSum_Age
|
0.95
|
-0.05
|
-0.03
|
|
b_riskBenefitSum_Intercept
|
0.95
|
6.11
|
12.24
|
|
b_riskBenefitSum_PNI_Sum_z
|
0.95
|
0.39
|
6.60
|
|
b_riskBenefitSum_Age
|
0.95
|
-0.11
|
0.00
|
m6_int <- brm(mvbind(riskSum, riskPerceptionSum, riskBenefitSum) ~ dominance_Sum*Gender + prestige_Sum*Gender + leadership_Sum*Gender + PNI_Sum_z*Gender + Age, data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 500, backend = "cmdstanr", save_pars = save_pars(all = TRUE), prior = prior_m6_int)
summary(m6_int)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: riskSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## riskPerceptionSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## riskBenefitSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 10000; warmup = 500; thin = 1;
## total post-warmup draws = 38000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## riskSum_Intercept 9.27 1.40 6.51 12.02 1.00 79816 30114
## riskPerceptionSum_Intercept 14.86 1.03 12.83 16.88 1.00 79813 29854
## riskBenefitSum_Intercept 9.21 1.58 6.11 12.30 1.00 88599 28996
## riskSum_dominance_Sum 0.56 0.48 -0.39 1.50 1.00 81841 28610
## riskSum_Gender 0.33 0.52 -0.69 1.35 1.00 88437 28307
## riskSum_prestige_Sum 0.22 0.41 -0.60 1.03 1.00 86514 29156
## riskSum_leadership_Sum -0.31 0.19 -0.68 0.06 1.00 96633 27059
## riskSum_PNI_Sum_z 0.39 0.87 -1.32 2.08 1.00 71814 26064
## riskSum_Age -0.01 0.02 -0.05 0.03 1.00 87545 27136
## riskSum_dominance_Sum:Gender 1.00 0.50 0.03 1.96 1.00 70073 31385
## riskSum_Gender:prestige_Sum -0.30 0.28 -0.85 0.25 1.00 84409 28314
## riskSum_Gender:leadership_Sum -0.26 0.39 -1.02 0.51 1.00 71810 31232
## riskSum_Gender:PNI_Sum_z 0.78 0.70 -0.59 2.13 1.00 67140 28759
## riskPerceptionSum_dominance_Sum -2.22 0.89 -3.97 -0.47 1.00 81966 28053
## riskPerceptionSum_Gender -0.63 0.20 -1.02 -0.24 1.00 89928 25040
## riskPerceptionSum_prestige_Sum -0.17 0.38 -0.91 0.57 1.00 82112 27342
## riskPerceptionSum_leadership_Sum -0.03 0.54 -1.11 1.03 1.00 87639 27716
## riskPerceptionSum_PNI_Sum_z -0.19 0.92 -1.98 1.61 1.00 80066 27422
## riskPerceptionSum_Age -0.04 0.00 -0.05 -0.03 1.00 37900 24531
## riskPerceptionSum_dominance_Sum:Gender -1.39 0.54 -2.44 -0.33 1.00 82463 29611
## riskPerceptionSum_Gender:prestige_Sum -0.10 0.52 -1.12 0.93 1.00 83997 27502
## riskPerceptionSum_Gender:leadership_Sum 0.29 0.71 -1.11 1.68 1.00 70132 31425
## riskPerceptionSum_Gender:PNI_Sum_z 0.25 0.79 -1.30 1.80 1.00 70717 28931
## riskBenefitSum_dominance_Sum 0.46 0.46 -0.44 1.37 1.00 79179 28525
## riskBenefitSum_Gender 0.12 0.62 -1.10 1.34 1.00 93262 26738
## riskBenefitSum_prestige_Sum -0.26 0.45 -1.13 0.62 1.00 78864 29496
## riskBenefitSum_leadership_Sum -0.34 0.18 -0.69 0.02 1.00 90813 27569
## riskBenefitSum_PNI_Sum_z 0.78 0.86 -0.90 2.46 1.00 72058 29749
## riskBenefitSum_Age -0.06 0.03 -0.11 -0.01 1.00 94536 26612
## riskBenefitSum_dominance_Sum:Gender 0.64 0.40 -0.14 1.43 1.00 70500 29744
## riskBenefitSum_Gender:prestige_Sum -0.66 0.20 -1.05 -0.28 1.00 90314 28650
## riskBenefitSum_Gender:leadership_Sum 0.07 0.40 -0.70 0.85 1.00 72447 32062
## riskBenefitSum_Gender:PNI_Sum_z 0.27 0.66 -1.04 1.57 1.00 64901 30122
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_riskSum 29.54 0.40 28.77 30.33 1.00 70524 30397
## sigma_riskPerceptionSum 38.39 0.45 37.53 39.26 1.00 73529 30289
## sigma_riskBenefitSum 25.98 0.37 25.25 26.71 1.00 68381 30790
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 0.78 0.01 0.76 0.80 1.00 73700 31465
## rescor(riskSum,riskBenefitSum) 0.87 0.01 0.85 0.89 1.00 76110 30993
## rescor(riskPerceptionSum,riskBenefitSum) 0.79 0.01 0.77 0.81 1.00 72276 29419
##
## 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).
m6_int_hdi <- bayestestR::hdi(m6_int, effects = "fixed", component = "conditional", ci = .95)
kable(m6_int_hdi[sign(m6_int_hdi$CI_low) == sign(m6_int_hdi$CI_high),
c('Parameter', 'CI','CI_low', 'CI_high')] , format = "html", booktabs = T, escape = F, longtable = F, digits = 2) %>% kable_styling(full_width = T) %>% remove_column(1)
|
Parameter
|
CI
|
CI_low
|
CI_high
|
|
b_riskSum_Intercept
|
0.95
|
6.49
|
11.99
|
|
b_riskSum_dominance_Sum:Gender
|
0.95
|
0.03
|
1.96
|
|
b_riskPerceptionSum_Intercept
|
0.95
|
12.83
|
16.88
|
|
b_riskPerceptionSum_dominance_Sum
|
0.95
|
-3.94
|
-0.45
|
|
b_riskPerceptionSum_Gender
|
0.95
|
-1.01
|
-0.23
|
|
b_riskPerceptionSum_Age
|
0.95
|
-0.05
|
-0.03
|
|
b_riskPerceptionSum_dominance_Sum:Gender
|
0.95
|
-2.44
|
-0.34
|
|
b_riskBenefitSum_Intercept
|
0.95
|
6.11
|
12.30
|
|
b_riskBenefitSum_Age
|
0.95
|
-0.11
|
-0.01
|
|
b_riskBenefitSum_Gender:prestige_Sum
|
0.95
|
-1.04
|
-0.27
|
