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, backend = "cmdstanr",
prior = c(
prior(normal(0, 1), class = "Intercept"),
prior(normal(1.04, 4.92), class = "b", coef = "dominance_Sum"),
prior(normal(-1.86, 2.04), class = "b", coef = "prestige_Sum"),
prior(normal(-3.87, 0.04), class = "b", coef = "leadership_Sum"),
prior(normal(-1.93, 1.91), class = "b", coef = "dominance_Sum:Gender2"),
prior(normal(-1.85, 1.98), class = "b", coef = "Gender2:prestige_Sum"),
prior(normal(-1.88, 1.98), class = "b", coef = "Gender2:leadership_Sum"),
prior(normal(-4.74, 1), class = "b", coef = "Age")
), save_pars = save_pars(all = T)
)
summary(m1_int)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: generalRiskPreference ~ dominance_Sum * Gender + leadership_Sum * Gender + prestige_Sum * 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
## Intercept 18.80 2.35 14.13 23.35 1.00 47266
## dominance_Sum 3.81 1.02 1.82 5.80 1.00 28559
## Gender2 2.36 1.46 -0.52 5.22 1.00 43123
## leadership_Sum -3.86 0.04 -3.94 -3.79 1.00 49052
## prestige_Sum 0.48 0.87 -1.24 2.20 1.00 30005
## Age -0.28 0.07 -0.43 -0.14 1.00 44484
## dominance_Sum:Gender2 -0.63 1.18 -2.96 1.70 1.00 28801
## Gender2:leadership_Sum 2.84 0.93 1.01 4.65 1.00 41985
## Gender2:prestige_Sum -1.30 1.14 -3.54 0.94 1.00 32773
## Tail_ESS
## Intercept 27942
## dominance_Sum 27922
## Gender2 27833
## leadership_Sum 28005
## prestige_Sum 26850
## Age 27664
## dominance_Sum:Gender2 28769
## Gender2:leadership_Sum 28878
## Gender2:prestige_Sum 29251
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 11.77 0.62 10.63 13.05 1.00 32830 28713
##
## 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_Intercept
|
0.95
|
14.19
|
23.40
|
|
b_dominance_Sum
|
0.95
|
1.82
|
5.80
|
|
b_leadership_Sum
|
0.95
|
-3.94
|
-3.78
|
|
b_Age
|
0.95
|
-0.43
|
-0.14
|
|
b_Gender2:leadership_Sum
|
0.95
|
0.99
|
4.63
|
m_gen <- brm(mvbind(dominance_Sum, prestige_Sum, leadership_Sum) ~ Gender + Age)
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
## ethicalPreferencez_Intercept 0.19 0.23 -0.26 0.64
## financialPreferencez_Intercept 0.01 0.25 -0.48 0.51
## socialPreferencez_Intercept 1.24 0.23 0.78 1.69
## healthAndSafetyPreferencez_Intercept 0.52 0.24 0.04 1.00
## recreationalPreferencez_Intercept 0.51 0.24 0.03 0.99
## ethicalPreferencez_dominance_Sum 0.33 0.06 0.21 0.45
## ethicalPreferencez_prestige_Sum -0.05 0.06 -0.17 0.07
## ethicalPreferencez_leadership_Sum -0.18 0.06 -0.30 -0.06
## ethicalPreferencez_Gender 0.27 0.11 0.05 0.48
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01
## financialPreferencez_dominance_Sum 0.09 0.06 -0.04 0.21
## financialPreferencez_prestige_Sum -0.05 0.07 -0.18 0.08
## financialPreferencez_leadership_Sum 0.09 0.07 -0.04 0.22
## financialPreferencez_Gender 0.19 0.12 -0.05 0.42
## financialPreferencez_Age -0.01 0.01 -0.02 0.00
## socialPreferencez_dominance_Sum 0.01 0.06 -0.11 0.13
## socialPreferencez_prestige_Sum 0.01 0.06 -0.11 0.13
## socialPreferencez_leadership_Sum 0.28 0.06 0.15 0.40
## socialPreferencez_Gender -0.45 0.11 -0.67 -0.24
## socialPreferencez_Age -0.02 0.01 -0.03 -0.01
## healthAndSafetyPreferencez_dominance_Sum 0.30 0.06 0.18 0.42
## healthAndSafetyPreferencez_prestige_Sum -0.24 0.06 -0.36 -0.11
## healthAndSafetyPreferencez_leadership_Sum 0.00 0.06 -0.12 0.13
## healthAndSafetyPreferencez_Gender 0.01 0.12 -0.21 0.24
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.01
## recreationalPreferencez_dominance_Sum 0.16 0.06 0.04 0.28
## recreationalPreferencez_prestige_Sum -0.27 0.06 -0.39 -0.16
## recreationalPreferencez_leadership_Sum 0.18 0.06 0.05 0.30
## recreationalPreferencez_Gender 0.23 0.12 -0.00 0.45
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02
## Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 1.00 212551 133987
## financialPreferencez_Intercept 1.00 228435 131270
## socialPreferencez_Intercept 1.00 220488 135807
## healthAndSafetyPreferencez_Intercept 1.00 207755 137383
## recreationalPreferencez_Intercept 1.00 219477 135665
## ethicalPreferencez_dominance_Sum 1.00 184984 130137
## ethicalPreferencez_prestige_Sum 1.00 177533 129800
## ethicalPreferencez_leadership_Sum 1.00 179079 133837
## ethicalPreferencez_Gender 1.00 197403 131456
## ethicalPreferencez_Age 1.00 204831 134759
## financialPreferencez_dominance_Sum 1.00 197536 129809
## financialPreferencez_prestige_Sum 1.00 196888 131918
## financialPreferencez_leadership_Sum 1.00 193177 130283
## financialPreferencez_Gender 1.00 211525 132426
## financialPreferencez_Age 1.00 223451 130857
## socialPreferencez_dominance_Sum 1.00 200558 135972
## socialPreferencez_prestige_Sum 1.00 192297 130284
## socialPreferencez_leadership_Sum 1.00 188379 130679
## socialPreferencez_Gender 1.00 204118 132949
## socialPreferencez_Age 1.00 209135 136084
## healthAndSafetyPreferencez_dominance_Sum 1.00 164714 130272
## healthAndSafetyPreferencez_prestige_Sum 1.00 166704 130732
## healthAndSafetyPreferencez_leadership_Sum 1.00 163164 133531
## healthAndSafetyPreferencez_Gender 1.00 179963 134646
## healthAndSafetyPreferencez_Age 1.00 193445 132048
## recreationalPreferencez_dominance_Sum 1.00 183480 134769
## recreationalPreferencez_prestige_Sum 1.00 190445 134418
## recreationalPreferencez_leadership_Sum 1.00 179093 130894
## recreationalPreferencez_Gender 1.00 194446 130133
## recreationalPreferencez_Age 1.00 203011 133743
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sigma_ethicalPreferencez 0.89 0.04 0.82 0.97 1.00
## sigma_financialPreferencez 0.98 0.04 0.90 1.06 1.00
## sigma_socialPreferencez 0.90 0.04 0.83 0.98 1.00
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.87 1.02 1.00
## sigma_recreationalPreferencez 0.94 0.04 0.86 1.02 1.00
## Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 215952 127953
## sigma_financialPreferencez 240264 126716
## sigma_socialPreferencez 238825 122865
## sigma_healthAndSafetyPreferencez 202093 133684
## sigma_recreationalPreferencez 223714 129971
##
## Residual Correlations:
## Estimate Est.Error
## rescor(ethicalPreferencez,financialPreferencez) 0.36 0.05
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06
## rescor(financialPreferencez,socialPreferencez) 0.23 0.06
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.50 0.05
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05
## rescor(ethicalPreferencez,recreationalPreferencez) 0.19 0.06
## rescor(financialPreferencez,recreationalPreferencez) 0.21 0.06
## rescor(socialPreferencez,recreationalPreferencez) 0.38 0.05
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05
## l-95% CI u-95% CI
## rescor(ethicalPreferencez,financialPreferencez) 0.25 0.46
## rescor(ethicalPreferencez,socialPreferencez) 0.01 0.24
## rescor(financialPreferencez,socialPreferencez) 0.12 0.34
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.41 0.58
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.09 0.32
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.20 0.42
## rescor(ethicalPreferencez,recreationalPreferencez) 0.07 0.30
## rescor(financialPreferencez,recreationalPreferencez) 0.10 0.32
## rescor(socialPreferencez,recreationalPreferencez) 0.28 0.48
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.35 0.54
## Rhat Bulk_ESS
## rescor(ethicalPreferencez,financialPreferencez) 1.00 225531
## rescor(ethicalPreferencez,socialPreferencez) 1.00 215552
## rescor(financialPreferencez,socialPreferencez) 1.00 216115
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 1.00 193681
## rescor(financialPreferencez,healthAndSafetyPreferencez) 1.00 206857
## rescor(socialPreferencez,healthAndSafetyPreferencez) 1.00 212954
## rescor(ethicalPreferencez,recreationalPreferencez) 1.00 196279
## rescor(financialPreferencez,recreationalPreferencez) 1.00 203114
## rescor(socialPreferencez,recreationalPreferencez) 1.00 199639
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 1.00 201961
## Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 126597
## rescor(ethicalPreferencez,socialPreferencez) 132800
## rescor(financialPreferencez,socialPreferencez) 128669
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 135547
## rescor(financialPreferencez,healthAndSafetyPreferencez) 132917
## rescor(socialPreferencez,healthAndSafetyPreferencez) 127137
## rescor(ethicalPreferencez,recreationalPreferencez) 134270
## rescor(financialPreferencez,recreationalPreferencez) 131793
## rescor(socialPreferencez,recreationalPreferencez) 130658
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 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 = 10000, 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
## ethicalPreferencez_Intercept 0.19 0.23 -0.26 0.64
## financialPreferencez_Intercept 0.01 0.25 -0.48 0.51
## socialPreferencez_Intercept 1.24 0.23 0.78 1.69
## healthAndSafetyPreferencez_Intercept 0.52 0.24 0.04 1.00
## recreationalPreferencez_Intercept 0.51 0.24 0.03 0.99
## ethicalPreferencez_dominance_Sum 0.33 0.06 0.21 0.45
## ethicalPreferencez_prestige_Sum -0.05 0.06 -0.17 0.07
## ethicalPreferencez_leadership_Sum -0.18 0.06 -0.30 -0.06
## ethicalPreferencez_Gender 0.27 0.11 0.05 0.48
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01
## financialPreferencez_dominance_Sum 0.09 0.06 -0.04 0.21
## financialPreferencez_prestige_Sum -0.05 0.07 -0.18 0.08
## financialPreferencez_leadership_Sum 0.09 0.07 -0.04 0.22
## financialPreferencez_Gender 0.19 0.12 -0.05 0.42
## financialPreferencez_Age -0.01 0.01 -0.02 0.00
## socialPreferencez_dominance_Sum 0.01 0.06 -0.11 0.13
## socialPreferencez_prestige_Sum 0.01 0.06 -0.11 0.13
## socialPreferencez_leadership_Sum 0.28 0.06 0.15 0.40
## socialPreferencez_Gender -0.45 0.11 -0.67 -0.24
## socialPreferencez_Age -0.02 0.01 -0.03 -0.01
## healthAndSafetyPreferencez_dominance_Sum 0.30 0.06 0.18 0.42
## healthAndSafetyPreferencez_prestige_Sum -0.24 0.06 -0.36 -0.11
## healthAndSafetyPreferencez_leadership_Sum 0.00 0.06 -0.12 0.13
## healthAndSafetyPreferencez_Gender 0.01 0.12 -0.21 0.24
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.01
## recreationalPreferencez_dominance_Sum 0.16 0.06 0.04 0.28
## recreationalPreferencez_prestige_Sum -0.27 0.06 -0.39 -0.16
## recreationalPreferencez_leadership_Sum 0.18 0.06 0.05 0.30
## recreationalPreferencez_Gender 0.23 0.12 -0.00 0.45
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02
## Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 1.00 212551 133987
## financialPreferencez_Intercept 1.00 228435 131270
## socialPreferencez_Intercept 1.00 220488 135807
## healthAndSafetyPreferencez_Intercept 1.00 207755 137383
## recreationalPreferencez_Intercept 1.00 219477 135665
## ethicalPreferencez_dominance_Sum 1.00 184984 130137
## ethicalPreferencez_prestige_Sum 1.00 177533 129800
## ethicalPreferencez_leadership_Sum 1.00 179079 133837
## ethicalPreferencez_Gender 1.00 197403 131456
## ethicalPreferencez_Age 1.00 204831 134759
## financialPreferencez_dominance_Sum 1.00 197536 129809
## financialPreferencez_prestige_Sum 1.00 196888 131918
## financialPreferencez_leadership_Sum 1.00 193177 130283
## financialPreferencez_Gender 1.00 211525 132426
## financialPreferencez_Age 1.00 223451 130857
## socialPreferencez_dominance_Sum 1.00 200558 135972
## socialPreferencez_prestige_Sum 1.00 192297 130284
## socialPreferencez_leadership_Sum 1.00 188379 130679
## socialPreferencez_Gender 1.00 204118 132949
## socialPreferencez_Age 1.00 209135 136084
## healthAndSafetyPreferencez_dominance_Sum 1.00 164714 130272
## healthAndSafetyPreferencez_prestige_Sum 1.00 166704 130732
## healthAndSafetyPreferencez_leadership_Sum 1.00 163164 133531
## healthAndSafetyPreferencez_Gender 1.00 179963 134646
## healthAndSafetyPreferencez_Age 1.00 193445 132048
## recreationalPreferencez_dominance_Sum 1.00 183480 134769
## recreationalPreferencez_prestige_Sum 1.00 190445 134418
## recreationalPreferencez_leadership_Sum 1.00 179093 130894
## recreationalPreferencez_Gender 1.00 194446 130133
## recreationalPreferencez_Age 1.00 203011 133743
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sigma_ethicalPreferencez 0.89 0.04 0.82 0.97 1.00
## sigma_financialPreferencez 0.98 0.04 0.90 1.06 1.00
## sigma_socialPreferencez 0.90 0.04 0.83 0.98 1.00
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.87 1.02 1.00
## sigma_recreationalPreferencez 0.94 0.04 0.86 1.02 1.00
## Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 215952 127953
## sigma_financialPreferencez 240264 126716
## sigma_socialPreferencez 238825 122865
## sigma_healthAndSafetyPreferencez 202093 133684
## sigma_recreationalPreferencez 223714 129971
##
## Residual Correlations:
## Estimate Est.Error
## rescor(ethicalPreferencez,financialPreferencez) 0.36 0.05
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06
## rescor(financialPreferencez,socialPreferencez) 0.23 0.06
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.50 0.05
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05
## rescor(ethicalPreferencez,recreationalPreferencez) 0.19 0.06
## rescor(financialPreferencez,recreationalPreferencez) 0.21 0.06
## rescor(socialPreferencez,recreationalPreferencez) 0.38 0.05
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05
## l-95% CI u-95% CI
## rescor(ethicalPreferencez,financialPreferencez) 0.25 0.46
## rescor(ethicalPreferencez,socialPreferencez) 0.01 0.24
## rescor(financialPreferencez,socialPreferencez) 0.12 0.34
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.41 0.58
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.09 0.32
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.20 0.42
## rescor(ethicalPreferencez,recreationalPreferencez) 0.07 0.30
## rescor(financialPreferencez,recreationalPreferencez) 0.10 0.32
## rescor(socialPreferencez,recreationalPreferencez) 0.28 0.48
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.35 0.54
## Rhat Bulk_ESS
## rescor(ethicalPreferencez,financialPreferencez) 1.00 225531
## rescor(ethicalPreferencez,socialPreferencez) 1.00 215552
## rescor(financialPreferencez,socialPreferencez) 1.00 216115
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 1.00 193681
## rescor(financialPreferencez,healthAndSafetyPreferencez) 1.00 206857
## rescor(socialPreferencez,healthAndSafetyPreferencez) 1.00 212954
## rescor(ethicalPreferencez,recreationalPreferencez) 1.00 196279
## rescor(financialPreferencez,recreationalPreferencez) 1.00 203114
## rescor(socialPreferencez,recreationalPreferencez) 1.00 199639
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 1.00 201961
## Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 126597
## rescor(ethicalPreferencez,socialPreferencez) 132800
## rescor(financialPreferencez,socialPreferencez) 128669
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 135547
## rescor(financialPreferencez,healthAndSafetyPreferencez) 132917
## rescor(socialPreferencez,healthAndSafetyPreferencez) 127137
## rescor(ethicalPreferencez,recreationalPreferencez) 134270
## rescor(financialPreferencez,recreationalPreferencez) 131793
## rescor(socialPreferencez,recreationalPreferencez) 130658
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 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_Intercept
|
0.95
|
14.19
|
23.40
|
|
b_dominance_Sum
|
0.95
|
1.82
|
5.80
|
|
b_leadership_Sum
|
0.95
|
-3.94
|
-3.78
|
|
b_Age
|
0.95
|
-0.43
|
-0.14
|
|
b_Gender2:leadership_Sum
|
0.95
|
0.99
|
4.63
|
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 20.82 2.30 16.32 25.33 1.00 50211 29054
## dominance_Sum 3.42 0.76 1.93 4.91 1.00 48863 28430
## grandiosity_Sum_z 0.15 0.63 -1.09 1.37 1.00 49593 27430
## vulnerability_Sum_z -0.70 0.65 -1.97 0.58 1.00 46426 28813
## Age -0.27 0.07 -0.41 -0.12 1.00 50055 27877
## Gender2 -2.34 0.84 -4.00 -0.70 1.00 50374 28745
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 11.62 0.62 10.47 12.91 1.00 38564 29013
##
## 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_hdi <- bayestestR::hdi(mod_pni, effects = "fixed", component = "conditional", ci = .95)
kable(mod_pni_hdi[
sign(mod_pni_hdi$CI_low) == sign(mod_pni_hdi$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_Intercept
|
0.95
|
16.28
|
25.27
|
|
b_dominance_Sum
|
0.95
|
1.94
|
4.92
|
|
b_Age
|
0.95
|
-0.41
|
-0.13
|
|
b_Gender2
|
0.95
|
-4.01
|
-0.71
|
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)
)
summary(mod_pni_gen)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: generalRiskPreference ~ dominance_Sum * Gender + grandiosity_Sum_z * Gender + vulnerability_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
## Intercept 18.93 2.26 14.47 23.31 1.00 51105
## dominance_Sum 1.62 0.67 0.32 2.93 1.00 43740
## Gender2 1.16 0.82 -0.45 2.76 1.00 59917
## grandiosity_Sum_z 0.38 0.66 -0.91 1.66 1.00 45491
## vulnerability_Sum_z -0.18 0.69 -1.52 1.18 1.00 43823
## Age -0.26 0.07 -0.40 -0.12 1.00 50372
## dominance_Sum:Gender2 0.99 0.77 -0.52 2.50 1.00 45531
## Gender2:grandiosity_Sum_z -0.13 0.79 -1.65 1.41 1.00 49076
## Gender2:vulnerability_Sum_z -0.30 0.77 -1.83 1.19 1.00 47214
## Tail_ESS
## Intercept 27531
## dominance_Sum 30445
## Gender2 26858
## grandiosity_Sum_z 29178
## vulnerability_Sum_z 29889
## Age 27873
## dominance_Sum:Gender2 29552
## Gender2:grandiosity_Sum_z 29500
## Gender2:vulnerability_Sum_z 28688
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 11.28 0.59 10.19 12.52 1.00 38592 28728
##
## 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_hdi <- bayestestR::hdi(mod_pni_gen, effects = "fixed", component = "conditional", ci = .95)
kable(mod_pni_gen_hdi[
sign(mod_pni_gen_hdi$CI_low) == sign(mod_pni_gen_hdi$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_Intercept
|
0.95
|
14.45
|
23.29
|
|
b_dominance_Sum
|
0.95
|
0.32
|
2.93
|
|
b_Age
|
0.95
|
-0.41
|
-0.12
|
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
## dominanceSum_Intercept 0.06 0.26 -0.43 0.57
## prestigeSum_Intercept 1.00 0.28 0.47 1.55
## leadershipSum_Intercept 0.30 0.26 -0.21 0.82
## dominanceSum_ethicalPreference_z 0.29 0.07 0.16 0.42
## dominanceSum_financialPreference_z -0.18 0.02 -0.21 -0.15
## dominanceSum_socialPreference_z -0.06 0.07 -0.19 0.07
## dominanceSum_healthAndSafetyPreference_z 0.05 0.05 -0.05 0.14
## dominanceSum_recreationalPreference_z 0.03 0.06 -0.09 0.14
## dominanceSum_Gender 0.27 0.12 0.03 0.51
## dominanceSum_Age -0.02 0.01 -0.03 -0.00
## prestigeSum_ethicalPreference_z 0.02 0.08 -0.13 0.17
## prestigeSum_financialPreference_z 0.02 0.06 -0.10 0.14
## prestigeSum_socialPreference_z -0.24 0.01 -0.26 -0.21
## prestigeSum_healthAndSafetyPreference_z -0.05 0.06 -0.18 0.08
## prestigeSum_recreationalPreference_z -0.08 0.06 -0.20 0.04
## prestigeSum_Gender -0.15 0.13 -0.41 0.11
## prestigeSum_Age -0.03 0.01 -0.04 -0.01
## leadershipSum_ethicalPreference_z -0.14 0.07 -0.28 0.00
## leadershipSum_financialPreference_z 0.03 0.05 -0.07 0.14
## leadershipSum_socialPreference_z 0.03 0.05 -0.08 0.13
## leadershipSum_healthAndSafetyPreference_z 0.05 0.06 -0.07 0.16
## leadershipSum_recreationalPreference_z -0.05 0.05 -0.14 0.04
## leadershipSum_Gender -0.09 0.13 -0.34 0.16
## leadershipSum_Age -0.01 0.01 -0.02 0.01
## Rhat Bulk_ESS Tail_ESS
## dominanceSum_Intercept 1.00 43108 30289
## prestigeSum_Intercept 1.00 46976 30549
## leadershipSum_Intercept 1.00 43748 30563
## dominanceSum_ethicalPreference_z 1.00 41604 28252
## dominanceSum_financialPreference_z 1.00 49934 25993
## dominanceSum_socialPreference_z 1.00 38332 28424
## dominanceSum_healthAndSafetyPreference_z 1.00 46005 29959
## dominanceSum_recreationalPreference_z 1.00 42718 27766
## dominanceSum_Gender 1.00 40142 29811
## dominanceSum_Age 1.00 42386 30528
## prestigeSum_ethicalPreference_z 1.00 38609 29534
## prestigeSum_financialPreference_z 1.00 46280 29297
## prestigeSum_socialPreference_z 1.00 53317 27882
## prestigeSum_healthAndSafetyPreference_z 1.00 42732 29870
## prestigeSum_recreationalPreference_z 1.00 43773 29034
## prestigeSum_Gender 1.00 40388 29559
## prestigeSum_Age 1.00 44319 31444
## leadershipSum_ethicalPreference_z 1.00 38352 30119
## leadershipSum_financialPreference_z 1.00 46189 29843
## leadershipSum_socialPreference_z 1.00 43874 30655
## leadershipSum_healthAndSafetyPreference_z 1.00 42668 29996
## leadershipSum_recreationalPreference_z 1.00 44470 29437
## leadershipSum_Gender 1.00 38622 28561
## leadershipSum_Age 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
## rescor(dominanceSum,prestigeSum) 0.34 0.06 0.22 0.45 1.00
## rescor(dominanceSum,leadershipSum) 0.38 0.05 0.27 0.48 1.00
## rescor(prestigeSum,leadershipSum) 0.51 0.05 0.41 0.60 1.00
## Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 35973 30300
## rescor(dominanceSum,leadershipSum) 34850 29072
## rescor(prestigeSum,leadershipSum) 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).
m3_hdi <- bayestestR::hdi(m3, effects = "fixed", component = "conditional", ci = .95)
## Warning: Identical densities found along different segments of the distribution,
## choosing rightmost.
kable(m3_hdi[
sign(m3_hdi$CI_low) == sign(m3_hdi$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
|
0.17
|
0.42
|
|
b_dominanceSum_financialPreference_z
|
0.95
|
-0.21
|
-0.15
|
|
b_dominanceSum_Gender
|
0.95
|
0.03
|
0.51
|
|
b_dominanceSum_Age
|
0.95
|
-0.03
|
0.00
|
|
b_prestigeSum_Intercept
|
0.95
|
0.46
|
1.54
|
|
b_prestigeSum_socialPreference_z
|
0.95
|
-0.26
|
-0.21
|
|
b_prestigeSum_Age
|
0.95
|
-0.04
|
-0.01
|
m3_exp_2 <- fixef(m3)
saveRDS(m3_exp_2, "m3_exp_2.rds")
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"
)
m3_int_gender_fixef <- fixef(m3_int_gender)
saveRDS(m3_int_gender, "m3_int_gender.rds")
saveRDS(m3_int_gender_fixef, "m3_int_gender_fixef.rds")
write.csv(m3_int_gender_fixef, "m3_int_gender_fixef.csv")
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
## dominanceSum_Intercept -0.13 0.26 -0.63
## prestigeSum_Intercept 0.54 0.27 0.02
## leadershipSum_Intercept 0.04 0.26 -0.47
## dominanceSum_ethicalPreference_z 0.02 0.16 -0.30
## dominanceSum_Gender 0.34 0.12 0.10
## dominanceSum_financialPreference_z -0.19 0.02 -0.22
## dominanceSum_socialPreference_z -0.08 0.15 -0.38
## dominanceSum_healthAndSafetyPreference_z -0.04 0.06 -0.16
## dominanceSum_recreationalPreference_z -0.05 0.10 -0.25
## dominanceSum_Age -0.01 0.01 -0.03
## dominanceSum_ethicalPreference_z:Gender 0.11 0.10 -0.08
## dominanceSum_Gender:financialPreference_z 0.10 0.04 0.02
## dominanceSum_Gender:socialPreference_z 0.09 0.10 -0.10
## dominanceSum_Gender:healthAndSafetyPreference_z 0.09 0.06 -0.03
## dominanceSum_Gender:recreationalPreference_z 0.03 0.07 -0.11
## prestigeSum_ethicalPreference_z -0.04 0.16 -0.35
## prestigeSum_Gender 0.08 0.13 -0.17
## prestigeSum_financialPreference_z -0.08 0.13 -0.33
## prestigeSum_socialPreference_z -0.25 0.01 -0.28
## prestigeSum_healthAndSafetyPreference_z -0.06 0.10 -0.25
## prestigeSum_recreationalPreference_z -0.10 0.10 -0.30
## prestigeSum_Age -0.02 0.01 -0.03
## prestigeSum_ethicalPreference_z:Gender 0.04 0.10 -0.15
## prestigeSum_Gender:financialPreference_z 0.07 0.08 -0.08
## prestigeSum_Gender:socialPreference_z 0.31 0.05 0.22
## prestigeSum_Gender:healthAndSafetyPreference_z -0.05 0.08 -0.20
## prestigeSum_Gender:recreationalPreference_z -0.02 0.07 -0.16
## leadershipSum_ethicalPreference_z -0.08 0.17 -0.42
## leadershipSum_Gender 0.04 0.13 -0.21
## leadershipSum_financialPreference_z -0.08 0.09 -0.26
## leadershipSum_socialPreference_z -0.14 0.07 -0.29
## leadershipSum_healthAndSafetyPreference_z 0.06 0.10 -0.13
## leadershipSum_recreationalPreference_z -0.15 0.06 -0.27
## leadershipSum_Age -0.00 0.01 -0.01
## leadershipSum_ethicalPreference_z:Gender -0.04 0.10 -0.24
## leadershipSum_Gender:financialPreference_z 0.11 0.06 -0.02
## leadershipSum_Gender:socialPreference_z 0.30 0.06 0.18
## leadershipSum_Gender:healthAndSafetyPreference_z -0.06 0.07 -0.21
## leadershipSum_Gender:recreationalPreference_z 0.09 0.06 -0.01
## u-95% CI Rhat Bulk_ESS
## dominanceSum_Intercept 0.38 1.00 60311
## prestigeSum_Intercept 1.06 1.00 55460
## leadershipSum_Intercept 0.55 1.00 54450
## dominanceSum_ethicalPreference_z 0.34 1.00 39555
## dominanceSum_Gender 0.58 1.00 60987
## dominanceSum_financialPreference_z -0.16 1.00 81451
## dominanceSum_socialPreference_z 0.21 1.00 38003
## dominanceSum_healthAndSafetyPreference_z 0.09 1.00 51911
## dominanceSum_recreationalPreference_z 0.15 1.00 40757
## dominanceSum_Age -0.00 1.00 62665
## dominanceSum_ethicalPreference_z:Gender 0.31 1.00 37779
## dominanceSum_Gender:financialPreference_z 0.18 1.00 64844
## dominanceSum_Gender:socialPreference_z 0.29 1.00 36694
## dominanceSum_Gender:healthAndSafetyPreference_z 0.21 1.00 48764
## dominanceSum_Gender:recreationalPreference_z 0.17 1.00 38005
## prestigeSum_ethicalPreference_z 0.28 1.00 38355
## prestigeSum_Gender 0.34 1.00 50359
## prestigeSum_financialPreference_z 0.17 1.00 39406
## prestigeSum_socialPreference_z -0.22 1.00 84103
## prestigeSum_healthAndSafetyPreference_z 0.14 1.00 42767
## prestigeSum_recreationalPreference_z 0.10 1.00 42002
## prestigeSum_Age -0.01 1.00 59756
## prestigeSum_ethicalPreference_z:Gender 0.24 1.00 36641
## prestigeSum_Gender:financialPreference_z 0.23 1.00 37583
## prestigeSum_Gender:socialPreference_z 0.40 1.00 59282
## prestigeSum_Gender:healthAndSafetyPreference_z 0.10 1.00 38228
## prestigeSum_Gender:recreationalPreference_z 0.12 1.00 38638
## leadershipSum_ethicalPreference_z 0.25 1.00 35587
## leadershipSum_Gender 0.28 1.00 49559
## leadershipSum_financialPreference_z 0.09 1.00 45054
## leadershipSum_socialPreference_z 0.01 1.00 45591
## leadershipSum_healthAndSafetyPreference_z 0.25 1.00 45420
## leadershipSum_recreationalPreference_z -0.03 1.00 52884
## leadershipSum_Age 0.01 1.00 56744
## leadershipSum_ethicalPreference_z:Gender 0.17 1.00 34003
## leadershipSum_Gender:financialPreference_z 0.23 1.00 42619
## leadershipSum_Gender:socialPreference_z 0.42 1.00 39614
## leadershipSum_Gender:healthAndSafetyPreference_z 0.08 1.00 42070
## leadershipSum_Gender:recreationalPreference_z 0.20 1.00 46407
## Tail_ESS
## dominanceSum_Intercept 28813
## prestigeSum_Intercept 30671
## leadershipSum_Intercept 29788
## dominanceSum_ethicalPreference_z 28419
## dominanceSum_Gender 29332
## dominanceSum_financialPreference_z 28314
## dominanceSum_socialPreference_z 27738
## dominanceSum_healthAndSafetyPreference_z 30155
## dominanceSum_recreationalPreference_z 28528
## dominanceSum_Age 31245
## dominanceSum_ethicalPreference_z:Gender 28381
## dominanceSum_Gender:financialPreference_z 27567
## dominanceSum_Gender:socialPreference_z 27615
## dominanceSum_Gender:healthAndSafetyPreference_z 29800
## dominanceSum_Gender:recreationalPreference_z 28942
## prestigeSum_ethicalPreference_z 29070
## prestigeSum_Gender 31078
## prestigeSum_financialPreference_z 28808
## prestigeSum_socialPreference_z 25983
## prestigeSum_healthAndSafetyPreference_z 31207
## prestigeSum_recreationalPreference_z 28485
## prestigeSum_Age 30700
## prestigeSum_ethicalPreference_z:Gender 29442
## prestigeSum_Gender:financialPreference_z 27891
## prestigeSum_Gender:socialPreference_z 27962
## prestigeSum_Gender:healthAndSafetyPreference_z 29619
## prestigeSum_Gender:recreationalPreference_z 28723
## leadershipSum_ethicalPreference_z 28717
## leadershipSum_Gender 30301
## leadershipSum_financialPreference_z 29233
## leadershipSum_socialPreference_z 30501
## leadershipSum_healthAndSafetyPreference_z 29998
## leadershipSum_recreationalPreference_z 29553
## leadershipSum_Age 29487
## leadershipSum_ethicalPreference_z:Gender 28880
## leadershipSum_Gender:financialPreference_z 29489
## leadershipSum_Gender:socialPreference_z 29267
## leadershipSum_Gender:healthAndSafetyPreference_z 29980
## leadershipSum_Gender:recreationalPreference_z 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
## rescor(dominanceSum,prestigeSum) 0.31 0.06 0.20 0.42 1.00
## rescor(dominanceSum,leadershipSum) 0.37 0.05 0.26 0.47 1.00
## rescor(prestigeSum,leadershipSum) 0.46 0.05 0.37 0.55 1.00
## Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 47676 30279
## rescor(dominanceSum,leadershipSum) 50068 30104
## rescor(prestigeSum,leadershipSum) 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")
m2_exp_2_J <- fixef(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
## ethicalPreferencez_Intercept 0.42 0.18 0.07 0.78
## financialPreferencez_Intercept 0.23 0.20 -0.17 0.63
## socialPreferencez_Intercept 0.67 0.19 0.30 1.03
## healthAndSafetyPreferencez_Intercept 0.48 0.19 0.09 0.85
## recreationalPreferencez_Intercept 0.70 0.19 0.33 1.09
## ethicalPreferencez_dominance_Sum 0.31 0.06 0.19 0.44
## ethicalPreferencez_prestige_Sum -0.06 0.06 -0.19 0.06
## ethicalPreferencez_leadership_Sum -0.18 0.06 -0.30 -0.06
## ethicalPreferencez_PNI_Sum_z 0.05 0.07 -0.09 0.18
## ethicalPreferencez_Gender2 0.27 0.11 0.06 0.49
## ethicalPreferencez_Age -0.02 0.01 -0.03 -0.01
## financialPreferencez_dominance_Sum 0.10 0.07 -0.04 0.24
## financialPreferencez_prestige_Sum -0.04 0.07 -0.18 0.10
## financialPreferencez_leadership_Sum 0.09 0.07 -0.04 0.22
## financialPreferencez_PNI_Sum_z -0.04 0.07 -0.19 0.11
## financialPreferencez_Gender2 0.18 0.12 -0.05 0.42
## financialPreferencez_Age -0.01 0.01 -0.02 0.00
## socialPreferencez_dominance_Sum -0.05 0.06 -0.17 0.08
## socialPreferencez_prestige_Sum -0.04 0.06 -0.17 0.08
## socialPreferencez_leadership_Sum 0.27 0.06 0.15 0.39
## socialPreferencez_PNI_Sum_z 0.17 0.07 0.04 0.30
## socialPreferencez_Gender2 -0.44 0.11 -0.66 -0.22
## socialPreferencez_Age -0.02 0.01 -0.03 -0.00
## healthAndSafetyPreferencez_dominance_Sum 0.27 0.07 0.14 0.41
## healthAndSafetyPreferencez_prestige_Sum -0.26 0.07 -0.39 -0.13
## healthAndSafetyPreferencez_leadership_Sum -0.00 0.06 -0.13 0.12
## healthAndSafetyPreferencez_PNI_Sum_z 0.08 0.07 -0.06 0.22
## healthAndSafetyPreferencez_Gender2 0.02 0.12 -0.21 0.25
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03 -0.01
## recreationalPreferencez_dominance_Sum 0.15 0.07 0.02 0.28
## recreationalPreferencez_prestige_Sum -0.28 0.06 -0.40 -0.16
## recreationalPreferencez_leadership_Sum 0.17 0.06 0.05 0.30
## recreationalPreferencez_PNI_Sum_z 0.04 0.07 -0.10 0.18
## recreationalPreferencez_Gender2 0.23 0.12 0.00 0.46
## recreationalPreferencez_Age -0.03 0.01 -0.04 -0.02
## Rhat Bulk_ESS Tail_ESS
## ethicalPreferencez_Intercept 1.00 55222 33696
## financialPreferencez_Intercept 1.00 61064 32045
## socialPreferencez_Intercept 1.00 57418 31651
## healthAndSafetyPreferencez_Intercept 1.00 50555 32605
## recreationalPreferencez_Intercept 1.00 57216 33904
## ethicalPreferencez_dominance_Sum 1.00 36043 31295
## ethicalPreferencez_prestige_Sum 1.00 38156 32630
## ethicalPreferencez_leadership_Sum 1.00 39557 31941
## ethicalPreferencez_PNI_Sum_z 1.00 34452 31774
## ethicalPreferencez_Gender2 1.00 42048 30612
## ethicalPreferencez_Age 1.00 54916 33657
## financialPreferencez_dominance_Sum 1.00 42156 32241
## financialPreferencez_prestige_Sum 1.00 45641 31272
## financialPreferencez_leadership_Sum 1.00 46417 30892
## financialPreferencez_PNI_Sum_z 1.00 41125 30057
## financialPreferencez_Gender2 1.00 49413 31314
## financialPreferencez_Age 1.00 60226 32747
## socialPreferencez_dominance_Sum 1.00 40262 30617
## socialPreferencez_prestige_Sum 1.00 45940 32174
## socialPreferencez_leadership_Sum 1.00 43397 32549
## socialPreferencez_PNI_Sum_z 1.00 40169 31388
## socialPreferencez_Gender2 1.00 48286 29718
## socialPreferencez_Age 1.00 54792 32771
## healthAndSafetyPreferencez_dominance_Sum 1.00 30803 29838
## healthAndSafetyPreferencez_prestige_Sum 1.00 36127 31474
## healthAndSafetyPreferencez_leadership_Sum 1.00 35167 31445
## healthAndSafetyPreferencez_PNI_Sum_z 1.00 31581 30256
## healthAndSafetyPreferencez_Gender2 1.00 38941 31152
## healthAndSafetyPreferencez_Age 1.00 48712 33058
## recreationalPreferencez_dominance_Sum 1.00 34716 31099
## recreationalPreferencez_prestige_Sum 1.00 44265 31851
## recreationalPreferencez_leadership_Sum 1.00 42618 32937
## recreationalPreferencez_PNI_Sum_z 1.00 36633 31691
## recreationalPreferencez_Gender2 1.00 39517 30924
## recreationalPreferencez_Age 1.00 55083 33804
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sigma_ethicalPreferencez 0.89 0.04 0.82 0.97 1.00
## sigma_financialPreferencez 0.98 0.04 0.90 1.07 1.00
## sigma_socialPreferencez 0.89 0.04 0.82 0.97 1.00
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.87 1.02 1.00
## sigma_recreationalPreferencez 0.94 0.04 0.86 1.02 1.00
## Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 50845 30191
## sigma_financialPreferencez 65899 30831
## sigma_socialPreferencez 56616 29455
## sigma_healthAndSafetyPreferencez 42366 30611
## sigma_recreationalPreferencez 54699 30543
##
## Residual Correlations:
## Estimate Est.Error
## rescor(ethicalPreferencez,financialPreferencez) 0.36 0.05
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06
## rescor(financialPreferencez,socialPreferencez) 0.24 0.06
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.50 0.05
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05
## rescor(ethicalPreferencez,recreationalPreferencez) 0.19 0.06
## rescor(financialPreferencez,recreationalPreferencez) 0.21 0.06
## rescor(socialPreferencez,recreationalPreferencez) 0.39 0.05
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05
## l-95% CI u-95% CI
## rescor(ethicalPreferencez,financialPreferencez) 0.26 0.46
## rescor(ethicalPreferencez,socialPreferencez) 0.01 0.24
## rescor(financialPreferencez,socialPreferencez) 0.13 0.35
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.41 0.58
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.10 0.32
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.20 0.41
## rescor(ethicalPreferencez,recreationalPreferencez) 0.07 0.30
## rescor(financialPreferencez,recreationalPreferencez) 0.10 0.33
## rescor(socialPreferencez,recreationalPreferencez) 0.28 0.48
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.35 0.54
## Rhat Bulk_ESS
## rescor(ethicalPreferencez,financialPreferencez) 1.00 53473
## rescor(ethicalPreferencez,socialPreferencez) 1.00 49885
## rescor(financialPreferencez,socialPreferencez) 1.00 49330
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 1.00 40475
## rescor(financialPreferencez,healthAndSafetyPreferencez) 1.00 47756
## rescor(socialPreferencez,healthAndSafetyPreferencez) 1.00 51757
## rescor(ethicalPreferencez,recreationalPreferencez) 1.00 42957
## rescor(financialPreferencez,recreationalPreferencez) 1.00 44075
## rescor(socialPreferencez,recreationalPreferencez) 1.00 47597
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 1.00 50503
## Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 31801
## rescor(ethicalPreferencez,socialPreferencez) 31689
## rescor(financialPreferencez,socialPreferencez) 30136
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 32390
## rescor(financialPreferencez,healthAndSafetyPreferencez) 31372
## rescor(socialPreferencez,healthAndSafetyPreferencez) 32591
## rescor(ethicalPreferencez,recreationalPreferencez) 32614
## rescor(financialPreferencez,recreationalPreferencez) 31690
## rescor(socialPreferencez,recreationalPreferencez) 32650
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 30615
##
## 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_Intercept
|
0.95
|
0.08
|
0.79
|
|
b_ethicalPreferencez_dominance_Sum
|
0.95
|
0.19
|
0.44
|
|
b_ethicalPreferencez_leadership_Sum
|
0.95
|
-0.30
|
-0.06
|
|
b_ethicalPreferencez_Gender2
|
0.95
|
0.06
|
0.49
|
|
b_ethicalPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_socialPreferencez_Intercept
|
0.95
|
0.30
|
1.02
|
|
b_socialPreferencez_leadership_Sum
|
0.95
|
0.15
|
0.39
|
|
b_socialPreferencez_PNI_Sum_z
|
0.95
|
0.04
|
0.30
|
|
b_socialPreferencez_Gender2
|
0.95
|
-0.65
|
-0.22
|
|
b_socialPreferencez_Age
|
0.95
|
-0.03
|
0.00
|
|
b_healthAndSafetyPreferencez_Intercept
|
0.95
|
0.10
|
0.86
|
|
b_healthAndSafetyPreferencez_dominance_Sum
|
0.95
|
0.14
|
0.41
|
|
b_healthAndSafetyPreferencez_prestige_Sum
|
0.95
|
-0.39
|
-0.13
|
|
b_healthAndSafetyPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_recreationalPreferencez_Intercept
|
0.95
|
0.32
|
1.08
|
|
b_recreationalPreferencez_dominance_Sum
|
0.95
|
0.02
|
0.28
|
|
b_recreationalPreferencez_prestige_Sum
|
0.95
|
-0.41
|
-0.16
|
|
b_recreationalPreferencez_leadership_Sum
|
0.95
|
0.05
|
0.30
|
|
b_recreationalPreferencez_Gender2
|
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")
m2_int_fix <- fixef(m2_int)
saveRDS(m2_int_fix, "m2_int_fix.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
## ethicalPreferencez_Intercept 0.40 0.19 0.03
## financialPreferencez_Intercept 0.25 0.21 -0.16
## socialPreferencez_Intercept 0.63 0.19 0.26
## healthAndSafetyPreferencez_Intercept 0.41 0.20 0.02
## recreationalPreferencez_Intercept 0.65 0.20 0.27
## ethicalPreferencez_dominance_Sum 0.23 0.10 0.03
## ethicalPreferencez_Gender2 0.28 0.11 0.07
## ethicalPreferencez_prestige_Sum -0.10 0.10 -0.29
## ethicalPreferencez_leadership_Sum -0.14 0.09 -0.33
## ethicalPreferencez_PNI_Sum_z -0.02 0.11 -0.23
## ethicalPreferencez_Age -0.02 0.01 -0.03
## ethicalPreferencez_dominance_Sum:Gender2 0.14 0.13 -0.11
## ethicalPreferencez_Gender2:prestige_Sum 0.10 0.13 -0.14
## ethicalPreferencez_Gender2:leadership_Sum -0.05 0.12 -0.29
## ethicalPreferencez_Gender2:PNI_Sum_z 0.13 0.13 -0.13
## financialPreferencez_dominance_Sum 0.10 0.11 -0.12
## financialPreferencez_Gender2 0.18 0.12 -0.06
## financialPreferencez_prestige_Sum -0.08 0.11 -0.29
## financialPreferencez_leadership_Sum 0.10 0.11 -0.11
## financialPreferencez_PNI_Sum_z -0.13 0.12 -0.36
## financialPreferencez_Age -0.01 0.01 -0.02
## financialPreferencez_dominance_Sum:Gender2 0.00 0.14 -0.28
## financialPreferencez_Gender2:prestige_Sum 0.11 0.14 -0.17
## financialPreferencez_Gender2:leadership_Sum 0.01 0.14 -0.26
## financialPreferencez_Gender2:PNI_Sum_z 0.16 0.15 -0.13
## socialPreferencez_dominance_Sum -0.11 0.10 -0.31
## socialPreferencez_Gender2 -0.43 0.11 -0.65
## socialPreferencez_prestige_Sum -0.02 0.10 -0.22
## socialPreferencez_leadership_Sum 0.24 0.10 0.05
## socialPreferencez_PNI_Sum_z 0.25 0.11 0.04
## socialPreferencez_Age -0.01 0.01 -0.03
## socialPreferencez_dominance_Sum:Gender2 0.10 0.13 -0.16
## socialPreferencez_Gender2:prestige_Sum -0.02 0.13 -0.27
## socialPreferencez_Gender2:leadership_Sum 0.03 0.12 -0.21
## socialPreferencez_Gender2:PNI_Sum_z -0.13 0.14 -0.40
## healthAndSafetyPreferencez_dominance_Sum 0.17 0.11 -0.04
## healthAndSafetyPreferencez_Gender2 0.03 0.12 -0.20
## healthAndSafetyPreferencez_prestige_Sum -0.44 0.10 -0.63
## healthAndSafetyPreferencez_leadership_Sum 0.05 0.10 -0.14
## healthAndSafetyPreferencez_PNI_Sum_z 0.27 0.11 0.06
## healthAndSafetyPreferencez_Age -0.02 0.01 -0.03
## healthAndSafetyPreferencez_dominance_Sum:Gender2 0.17 0.14 -0.10
## healthAndSafetyPreferencez_Gender2:prestige_Sum 0.32 0.13 0.06
## healthAndSafetyPreferencez_Gender2:leadership_Sum -0.09 0.13 -0.35
## healthAndSafetyPreferencez_Gender2:PNI_Sum_z -0.30 0.14 -0.58
## recreationalPreferencez_dominance_Sum 0.11 0.11 -0.09
## recreationalPreferencez_Gender2 0.23 0.11 0.00
## recreationalPreferencez_prestige_Sum -0.41 0.08 -0.57
## recreationalPreferencez_leadership_Sum 0.19 0.09 0.00
## recreationalPreferencez_PNI_Sum_z 0.32 0.11 0.11
## recreationalPreferencez_Age -0.03 0.01 -0.04
## recreationalPreferencez_dominance_Sum:Gender2 0.05 0.13 -0.22
## recreationalPreferencez_Gender2:prestige_Sum 0.23 0.12 -0.01
## recreationalPreferencez_Gender2:leadership_Sum -0.07 0.12 -0.31
## recreationalPreferencez_Gender2:PNI_Sum_z -0.48 0.14 -0.76
## u-95% CI Rhat Bulk_ESS
## ethicalPreferencez_Intercept 0.76 1.00 52152
## financialPreferencez_Intercept 0.65 1.00 55167
## socialPreferencez_Intercept 1.00 1.00 55660
## healthAndSafetyPreferencez_Intercept 0.80 1.00 52192
## recreationalPreferencez_Intercept 1.04 1.00 49708
## ethicalPreferencez_dominance_Sum 0.43 1.00 26542
## ethicalPreferencez_Gender2 0.50 1.00 52514
## ethicalPreferencez_prestige_Sum 0.09 1.00 27657
## ethicalPreferencez_leadership_Sum 0.04 1.00 31473
## ethicalPreferencez_PNI_Sum_z 0.19 1.00 25452
## ethicalPreferencez_Age -0.01 1.00 55942
## ethicalPreferencez_dominance_Sum:Gender2 0.40 1.00 26852
## ethicalPreferencez_Gender2:prestige_Sum 0.35 1.00 29118
## ethicalPreferencez_Gender2:leadership_Sum 0.19 1.00 31046
## ethicalPreferencez_Gender2:PNI_Sum_z 0.40 1.00 26174
## financialPreferencez_dominance_Sum 0.33 1.00 28497
## financialPreferencez_Gender2 0.42 1.00 58199
## financialPreferencez_prestige_Sum 0.14 1.00 33052
## financialPreferencez_leadership_Sum 0.30 1.00 35467
## financialPreferencez_PNI_Sum_z 0.11 1.00 27943
## financialPreferencez_Age 0.00 1.00 60199
## financialPreferencez_dominance_Sum:Gender2 0.28 1.00 28325
## financialPreferencez_Gender2:prestige_Sum 0.39 1.00 31588
## financialPreferencez_Gender2:leadership_Sum 0.28 1.00 35748
## financialPreferencez_Gender2:PNI_Sum_z 0.45 1.00 27723
## socialPreferencez_dominance_Sum 0.09 1.00 28368
## socialPreferencez_Gender2 -0.21 1.00 53960
## socialPreferencez_prestige_Sum 0.17 1.00 32388
## socialPreferencez_leadership_Sum 0.42 1.00 35232
## socialPreferencez_PNI_Sum_z 0.46 1.00 25586
## socialPreferencez_Age -0.00 1.00 58290
## socialPreferencez_dominance_Sum:Gender2 0.36 1.00 29080
## socialPreferencez_Gender2:prestige_Sum 0.24 1.00 31070
## socialPreferencez_Gender2:leadership_Sum 0.28 1.00 34946
## socialPreferencez_Gender2:PNI_Sum_z 0.14 1.00 27409
## healthAndSafetyPreferencez_dominance_Sum 0.38 1.00 24190
## healthAndSafetyPreferencez_Gender2 0.26 1.00 47426
## healthAndSafetyPreferencez_prestige_Sum -0.24 1.00 28025
## healthAndSafetyPreferencez_leadership_Sum 0.24 1.00 28196
## healthAndSafetyPreferencez_PNI_Sum_z 0.50 1.00 23803
## healthAndSafetyPreferencez_Age -0.00 1.00 55259
## healthAndSafetyPreferencez_dominance_Sum:Gender2 0.43 1.00 23920
## healthAndSafetyPreferencez_Gender2:prestige_Sum 0.58 1.00 29012
## healthAndSafetyPreferencez_Gender2:leadership_Sum 0.16 1.00 28072
## healthAndSafetyPreferencez_Gender2:PNI_Sum_z -0.02 1.00 24219
## recreationalPreferencez_dominance_Sum 0.32 1.00 24661
## recreationalPreferencez_Gender2 0.46 1.00 50472
## recreationalPreferencez_prestige_Sum -0.24 1.00 32612
## recreationalPreferencez_leadership_Sum 0.37 1.00 33296
## recreationalPreferencez_PNI_Sum_z 0.53 1.00 25334
## recreationalPreferencez_Age -0.02 1.00 53947
## recreationalPreferencez_dominance_Sum:Gender2 0.31 1.00 25249
## recreationalPreferencez_Gender2:prestige_Sum 0.47 1.00 33029
## recreationalPreferencez_Gender2:leadership_Sum 0.18 1.00 32617
## recreationalPreferencez_Gender2:PNI_Sum_z -0.21 1.00 27063
## Tail_ESS
## ethicalPreferencez_Intercept 33147
## financialPreferencez_Intercept 32029
## socialPreferencez_Intercept 32933
## healthAndSafetyPreferencez_Intercept 33352
## recreationalPreferencez_Intercept 33397
## ethicalPreferencez_dominance_Sum 30039
## ethicalPreferencez_Gender2 32451
## ethicalPreferencez_prestige_Sum 30097
## ethicalPreferencez_leadership_Sum 30782
## ethicalPreferencez_PNI_Sum_z 28987
## ethicalPreferencez_Age 32973
## ethicalPreferencez_dominance_Sum:Gender2 29442
## ethicalPreferencez_Gender2:prestige_Sum 30853
## ethicalPreferencez_Gender2:leadership_Sum 30507
## ethicalPreferencez_Gender2:PNI_Sum_z 29651
## financialPreferencez_dominance_Sum 29169
## financialPreferencez_Gender2 31040
## financialPreferencez_prestige_Sum 31117
## financialPreferencez_leadership_Sum 30789
## financialPreferencez_PNI_Sum_z 29955
## financialPreferencez_Age 32761
## financialPreferencez_dominance_Sum:Gender2 30629
## financialPreferencez_Gender2:prestige_Sum 30345
## financialPreferencez_Gender2:leadership_Sum 30748
## financialPreferencez_Gender2:PNI_Sum_z 30146
## socialPreferencez_dominance_Sum 28331
## socialPreferencez_Gender2 31321
## socialPreferencez_prestige_Sum 30614
## socialPreferencez_leadership_Sum 31854
## socialPreferencez_PNI_Sum_z 27840
## socialPreferencez_Age 33933
## socialPreferencez_dominance_Sum:Gender2 29260
## socialPreferencez_Gender2:prestige_Sum 30396
## socialPreferencez_Gender2:leadership_Sum 30517
## socialPreferencez_Gender2:PNI_Sum_z 29451
## healthAndSafetyPreferencez_dominance_Sum 29492
## healthAndSafetyPreferencez_Gender2 32567
## healthAndSafetyPreferencez_prestige_Sum 30188
## healthAndSafetyPreferencez_leadership_Sum 30428
## healthAndSafetyPreferencez_PNI_Sum_z 27715
## healthAndSafetyPreferencez_Age 31622
## healthAndSafetyPreferencez_dominance_Sum:Gender2 28847
## healthAndSafetyPreferencez_Gender2:prestige_Sum 30540
## healthAndSafetyPreferencez_Gender2:leadership_Sum 30590
## healthAndSafetyPreferencez_Gender2:PNI_Sum_z 28774
## recreationalPreferencez_dominance_Sum 28818
## recreationalPreferencez_Gender2 32180
## recreationalPreferencez_prestige_Sum 29138
## recreationalPreferencez_leadership_Sum 30881
## recreationalPreferencez_PNI_Sum_z 29240
## recreationalPreferencez_Age 33965
## recreationalPreferencez_dominance_Sum:Gender2 29207
## recreationalPreferencez_Gender2:prestige_Sum 29962
## recreationalPreferencez_Gender2:leadership_Sum 29940
## recreationalPreferencez_Gender2:PNI_Sum_z 29576
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sigma_ethicalPreferencez 0.88 0.04 0.81 0.96 1.00
## sigma_financialPreferencez 0.98 0.04 0.90 1.07 1.00
## sigma_socialPreferencez 0.89 0.04 0.82 0.97 1.00
## sigma_healthAndSafetyPreferencez 0.94 0.04 0.86 1.02 1.00
## sigma_recreationalPreferencez 0.93 0.04 0.85 1.01 1.00
## Bulk_ESS Tail_ESS
## sigma_ethicalPreferencez 55872 30254
## sigma_financialPreferencez 71205 30822
## sigma_socialPreferencez 61243 32080
## sigma_healthAndSafetyPreferencez 53095 32399
## sigma_recreationalPreferencez 60429 32747
##
## Residual Correlations:
## Estimate Est.Error
## rescor(ethicalPreferencez,financialPreferencez) 0.35 0.05
## rescor(ethicalPreferencez,socialPreferencez) 0.13 0.06
## rescor(financialPreferencez,socialPreferencez) 0.25 0.06
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.51 0.05
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.21 0.06
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.31 0.05
## rescor(ethicalPreferencez,recreationalPreferencez) 0.22 0.06
## rescor(financialPreferencez,recreationalPreferencez) 0.24 0.06
## rescor(socialPreferencez,recreationalPreferencez) 0.39 0.05
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.45 0.05
## l-95% CI u-95% CI
## rescor(ethicalPreferencez,financialPreferencez) 0.24 0.45
## rescor(ethicalPreferencez,socialPreferencez) 0.01 0.24
## rescor(financialPreferencez,socialPreferencez) 0.13 0.36
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 0.41 0.59
## rescor(financialPreferencez,healthAndSafetyPreferencez) 0.10 0.33
## rescor(socialPreferencez,healthAndSafetyPreferencez) 0.20 0.41
## rescor(ethicalPreferencez,recreationalPreferencez) 0.10 0.33
## rescor(financialPreferencez,recreationalPreferencez) 0.13 0.35
## rescor(socialPreferencez,recreationalPreferencez) 0.28 0.48
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 0.35 0.54
## Rhat Bulk_ESS
## rescor(ethicalPreferencez,financialPreferencez) 1.00 60011
## rescor(ethicalPreferencez,socialPreferencez) 1.00 55977
## rescor(financialPreferencez,socialPreferencez) 1.00 56156
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 1.00 46976
## rescor(financialPreferencez,healthAndSafetyPreferencez) 1.00 53473
## rescor(socialPreferencez,healthAndSafetyPreferencez) 1.00 58950
## rescor(ethicalPreferencez,recreationalPreferencez) 1.00 50048
## rescor(financialPreferencez,recreationalPreferencez) 1.00 51036
## rescor(socialPreferencez,recreationalPreferencez) 1.00 54955
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 1.00 55945
## Tail_ESS
## rescor(ethicalPreferencez,financialPreferencez) 32318
## rescor(ethicalPreferencez,socialPreferencez) 31708
## rescor(financialPreferencez,socialPreferencez) 31528
## rescor(ethicalPreferencez,healthAndSafetyPreferencez) 31337
## rescor(financialPreferencez,healthAndSafetyPreferencez) 32201
## rescor(socialPreferencez,healthAndSafetyPreferencez) 31895
## rescor(ethicalPreferencez,recreationalPreferencez) 32997
## rescor(financialPreferencez,recreationalPreferencez) 32087
## rescor(socialPreferencez,recreationalPreferencez) 31920
## rescor(healthAndSafetyPreferencez,recreationalPreferencez) 31716
##
## 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)
## Warning: Identical densities found along different segments of the distribution,
## choosing rightmost.
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_Intercept
|
0.95
|
0.03
|
0.76
|
|
b_ethicalPreferencez_dominance_Sum
|
0.95
|
0.03
|
0.43
|
|
b_ethicalPreferencez_Gender2
|
0.95
|
0.07
|
0.50
|
|
b_ethicalPreferencez_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_socialPreferencez_Intercept
|
0.95
|
0.27
|
1.01
|
|
b_socialPreferencez_Gender2
|
0.95
|
-0.65
|
-0.21
|
|
b_socialPreferencez_leadership_Sum
|
0.95
|
0.05
|
0.43
|
|
b_socialPreferencez_PNI_Sum_z
|
0.95
|
0.04
|
0.46
|
|
b_socialPreferencez_Age
|
0.95
|
-0.03
|
0.00
|
|
b_healthAndSafetyPreferencez_Intercept
|
0.95
|
0.02
|
0.80
|
|
b_healthAndSafetyPreferencez_prestige_Sum
|
0.95
|
-0.64
|
-0.24
|
|
b_healthAndSafetyPreferencez_PNI_Sum_z
|
0.95
|
0.05
|
0.49
|
|
b_healthAndSafetyPreferencez_Age
|
0.95
|
-0.03
|
0.00
|
|
b_healthAndSafetyPreferencez_Gender2:prestige_Sum
|
0.95
|
0.06
|
0.58
|
|
b_healthAndSafetyPreferencez_Gender2:PNI_Sum_z
|
0.95
|
-0.57
|
-0.02
|
|
b_recreationalPreferencez_Intercept
|
0.95
|
0.27
|
1.04
|
|
b_recreationalPreferencez_Gender2
|
0.95
|
0.01
|
0.46
|
|
b_recreationalPreferencez_prestige_Sum
|
0.95
|
-0.57
|
-0.24
|
|
b_recreationalPreferencez_leadership_Sum
|
0.95
|
0.01
|
0.37
|
|
b_recreationalPreferencez_PNI_Sum_z
|
0.95
|
0.10
|
0.53
|
|
b_recreationalPreferencez_Age
|
0.95
|
-0.04
|
-0.02
|
|
b_recreationalPreferencez_Gender2:PNI_Sum_z
|
0.95
|
-0.75
|
-0.21
|
# plot(m2_int, ask = FALSE)
dopl_PNI_1 <- brm(mvbind(dominance_Sum, prestige_Sum, leadership_Sum) ~ grandiose_fantasy_Sum_z + cse_Sum_z + hts_Sum_z + ssse_Sum_z + entitlement_rage_Sum_z + devaluing_Sum_z + exploitativeness_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 = dopl_PNI_1_priors
)
saveRDS(dopl_PNI_1, "dopl_PNI_1.rds")
dopl_PNI_1_fix <- fixef(dopl_PNI_1)
saveRDS(dopl_PNI_1_fix, "dopl_PNI_1_fix.rds")
summary(dopl_PNI_1)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: dominance_Sum ~ grandiose_fantasy_Sum_z + cse_Sum_z + hts_Sum_z + ssse_Sum_z + entitlement_rage_Sum_z + devaluing_Sum_z + exploitativeness_Sum_z + Gender + Age
## prestige_Sum ~ grandiose_fantasy_Sum_z + cse_Sum_z + hts_Sum_z + ssse_Sum_z + entitlement_rage_Sum_z + devaluing_Sum_z + exploitativeness_Sum_z + Gender + Age
## leadership_Sum ~ grandiose_fantasy_Sum_z + cse_Sum_z + hts_Sum_z + ssse_Sum_z + entitlement_rage_Sum_z + devaluing_Sum_z + exploitativeness_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
## dominanceSum_Intercept -0.08 0.16 -0.40 0.24 1.00
## prestigeSum_Intercept 0.19 0.17 -0.15 0.53 1.00
## leadershipSum_Intercept -0.06 0.17 -0.40 0.28 1.00
## dominanceSum_grandiose_fantasy_Sum_z -0.08 0.06 -0.19 0.02 1.00
## dominanceSum_cse_Sum_z 0.07 0.07 -0.06 0.20 1.00
## dominanceSum_hts_Sum_z 0.06 0.06 -0.07 0.18 1.00
## dominanceSum_ssse_Sum_z -0.09 0.06 -0.20 0.03 1.00
## dominanceSum_entitlement_rage_Sum_z 0.41 0.07 0.28 0.55 1.00
## dominanceSum_devaluing_Sum_z -0.03 0.07 -0.16 0.11 1.00
## dominanceSum_exploitativeness_Sum_z 0.35 0.05 0.25 0.45 1.00
## dominanceSum_Gender2 0.31 0.10 0.12 0.50 1.00
## dominanceSum_Age -0.00 0.01 -0.01 0.01 1.00
## prestigeSum_grandiose_fantasy_Sum_z 0.19 0.06 0.07 0.30 1.00
## prestigeSum_cse_Sum_z 0.14 0.07 0.00 0.28 1.00
## prestigeSum_hts_Sum_z -0.19 0.07 -0.32 -0.06 1.00
## prestigeSum_ssse_Sum_z 0.28 0.06 0.16 0.41 1.00
## prestigeSum_entitlement_rage_Sum_z 0.23 0.07 0.09 0.37 1.00
## prestigeSum_devaluing_Sum_z -0.11 0.07 -0.26 0.03 1.00
## prestigeSum_exploitativeness_Sum_z 0.19 0.06 0.08 0.29 1.00
## prestigeSum_Gender2 -0.15 0.10 -0.35 0.05 1.00
## prestigeSum_Age -0.00 0.01 -0.01 0.01 1.00
## leadershipSum_grandiose_fantasy_Sum_z 0.13 0.06 0.02 0.25 1.00
## leadershipSum_cse_Sum_z -0.09 0.07 -0.23 0.04 1.00
## leadershipSum_hts_Sum_z -0.09 0.07 -0.22 0.04 1.00
## leadershipSum_ssse_Sum_z 0.17 0.06 0.05 0.29 1.00
## leadershipSum_entitlement_rage_Sum_z 0.10 0.07 -0.05 0.24 1.00
## leadershipSum_devaluing_Sum_z -0.07 0.07 -0.21 0.06 1.00
## leadershipSum_exploitativeness_Sum_z 0.49 0.06 0.39 0.60 1.00
## leadershipSum_Gender2 -0.32 0.10 -0.52 -0.12 1.00
## leadershipSum_Age 0.01 0.01 -0.00 0.02 1.00
## Bulk_ESS Tail_ESS
## dominanceSum_Intercept 68193 30049
## prestigeSum_Intercept 72559 31890
## leadershipSum_Intercept 71865 30283
## dominanceSum_grandiose_fantasy_Sum_z 60756 30777
## dominanceSum_cse_Sum_z 61185 30479
## dominanceSum_hts_Sum_z 62322 31015
## dominanceSum_ssse_Sum_z 59822 30447
## dominanceSum_entitlement_rage_Sum_z 61419 31166
## dominanceSum_devaluing_Sum_z 65451 30931
## dominanceSum_exploitativeness_Sum_z 63888 31320
## dominanceSum_Gender2 66531 30317
## dominanceSum_Age 64869 30767
## prestigeSum_grandiose_fantasy_Sum_z 61713 31972
## prestigeSum_cse_Sum_z 54228 29417
## prestigeSum_hts_Sum_z 55015 29157
## prestigeSum_ssse_Sum_z 57524 31022
## prestigeSum_entitlement_rage_Sum_z 53868 32087
## prestigeSum_devaluing_Sum_z 50206 31703
## prestigeSum_exploitativeness_Sum_z 55313 31295
## prestigeSum_Gender2 60207 31271
## prestigeSum_Age 69130 33456
## leadershipSum_grandiose_fantasy_Sum_z 62024 30040
## leadershipSum_cse_Sum_z 58904 32571
## leadershipSum_hts_Sum_z 57272 32648
## leadershipSum_ssse_Sum_z 59019 30585
## leadershipSum_entitlement_rage_Sum_z 52012 31455
## leadershipSum_devaluing_Sum_z 50605 31411
## leadershipSum_exploitativeness_Sum_z 58285 31393
## leadershipSum_Gender2 61189 31455
## leadershipSum_Age 69090 32873
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_dominanceSum 0.78 0.03 0.72 0.85 1.00 74017 28486
## sigma_prestigeSum 0.83 0.04 0.76 0.90 1.00 66074 27998
## sigma_leadershipSum 0.82 0.04 0.75 0.89 1.00 71983 30990
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## rescor(dominanceSum,prestigeSum) 0.10 0.06 -0.02 0.22 1.00
## rescor(dominanceSum,leadershipSum) 0.13 0.06 0.01 0.25 1.00
## rescor(prestigeSum,leadershipSum) 0.30 0.06 0.19 0.40 1.00
## Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 68943 29601
## rescor(dominanceSum,leadershipSum) 69489 30211
## rescor(prestigeSum,leadershipSum) 60574 30401
##
## 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).
dopl_PNI_1_hdi <- bayestestR::hdi(dopl_PNI_1, effects = "fixed", component = "conditional", ci = .95)
kable(dopl_PNI_1_hdi[
sign(dopl_PNI_1_hdi$CI_low) == sign(dopl_PNI_1_hdi$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_entitlement_rage_Sum_z
|
0.95
|
0.28
|
0.55
|
|
b_dominanceSum_exploitativeness_Sum_z
|
0.95
|
0.24
|
0.45
|
|
b_dominanceSum_Gender2
|
0.95
|
0.12
|
0.50
|
|
b_prestigeSum_grandiose_fantasy_Sum_z
|
0.95
|
0.07
|
0.30
|
|
b_prestigeSum_cse_Sum_z
|
0.95
|
0.00
|
0.27
|
|
b_prestigeSum_hts_Sum_z
|
0.95
|
-0.32
|
-0.06
|
|
b_prestigeSum_ssse_Sum_z
|
0.95
|
0.16
|
0.40
|
|
b_prestigeSum_entitlement_rage_Sum_z
|
0.95
|
0.08
|
0.37
|
|
b_prestigeSum_exploitativeness_Sum_z
|
0.95
|
0.08
|
0.29
|
|
b_leadershipSum_grandiose_fantasy_Sum_z
|
0.95
|
0.02
|
0.25
|
|
b_leadershipSum_ssse_Sum_z
|
0.95
|
0.06
|
0.29
|
|
b_leadershipSum_exploitativeness_Sum_z
|
0.95
|
0.39
|
0.60
|
|
b_leadershipSum_Gender2
|
0.95
|
-0.52
|
-0.12
|
dopl_PNI_1_int <- brm(mvbind(dominance_Sum, prestige_Sum, leadership_Sum) ~ grandiose_fantasy_Sum_z * Gender + cse_Sum_z * Gender + hts_Sum_z * Gender + ssse_Sum_z * Gender + entitlement_rage_Sum_z * Gender + devaluing_Sum_z * Gender + exploitativeness_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 = dopl_PNI_1_int_priors
)
saveRDS(dopl_PNI_1_int, "dopl_PNI_1_int.rds")
dopl_PNI_1_int_fix <- fixef(dopl_PNI_1_int)
saveRDS(dopl_PNI_1_int_fix, "dopl_PNI_1_int_fix.rds")
summary(dopl_PNI_1_int)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: dominance_Sum ~ grandiose_fantasy_Sum_z * Gender + cse_Sum_z * Gender + hts_Sum_z * Gender + ssse_Sum_z * Gender + entitlement_rage_Sum_z * Gender + devaluing_Sum_z * Gender + exploitativeness_Sum_z * Gender + Age
## prestige_Sum ~ grandiose_fantasy_Sum_z * Gender + cse_Sum_z * Gender + hts_Sum_z * Gender + ssse_Sum_z * Gender + entitlement_rage_Sum_z * Gender + devaluing_Sum_z * Gender + exploitativeness_Sum_z * Gender + Age
## leadership_Sum ~ grandiose_fantasy_Sum_z * Gender + cse_Sum_z * Gender + hts_Sum_z * Gender + ssse_Sum_z * Gender + entitlement_rage_Sum_z * Gender + devaluing_Sum_z * Gender + exploitativeness_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
## dominanceSum_Intercept -0.10 0.17 -0.43
## prestigeSum_Intercept 0.20 0.18 -0.14
## leadershipSum_Intercept -0.03 0.17 -0.36
## dominanceSum_grandiose_fantasy_Sum_z -0.02 0.08 -0.19
## dominanceSum_Gender2 0.30 0.10 0.11
## dominanceSum_cse_Sum_z 0.11 0.09 -0.06
## dominanceSum_hts_Sum_z 0.07 0.10 -0.11
## dominanceSum_ssse_Sum_z -0.01 0.09 -0.20
## dominanceSum_entitlement_rage_Sum_z 0.28 0.10 0.08
## dominanceSum_devaluing_Sum_z -0.01 0.10 -0.20
## dominanceSum_exploitativeness_Sum_z 0.37 0.07 0.22
## dominanceSum_Age -0.00 0.01 -0.01
## dominanceSum_grandiose_fantasy_Sum_z:Gender2 -0.13 0.11 -0.35
## dominanceSum_Gender2:cse_Sum_z -0.03 0.13 -0.29
## dominanceSum_Gender2:hts_Sum_z -0.08 0.13 -0.34
## dominanceSum_Gender2:ssse_Sum_z -0.14 0.12 -0.37
## dominanceSum_Gender2:entitlement_rage_Sum_z 0.28 0.14 0.01
## dominanceSum_Gender2:devaluing_Sum_z -0.03 0.13 -0.29
## dominanceSum_Gender2:exploitativeness_Sum_z -0.07 0.10 -0.27
## prestigeSum_grandiose_fantasy_Sum_z 0.27 0.09 0.09
## prestigeSum_Gender2 -0.15 0.10 -0.35
## prestigeSum_cse_Sum_z 0.20 0.09 0.02
## prestigeSum_hts_Sum_z -0.23 0.10 -0.43
## prestigeSum_ssse_Sum_z 0.24 0.10 0.05
## prestigeSum_entitlement_rage_Sum_z 0.22 0.10 0.02
## prestigeSum_devaluing_Sum_z -0.05 0.10 -0.25
## prestigeSum_exploitativeness_Sum_z 0.20 0.08 0.05
## prestigeSum_Age -0.00 0.01 -0.01
## prestigeSum_grandiose_fantasy_Sum_z:Gender2 -0.14 0.12 -0.37
## prestigeSum_Gender2:cse_Sum_z -0.14 0.14 -0.41
## prestigeSum_Gender2:hts_Sum_z 0.08 0.14 -0.19
## prestigeSum_Gender2:ssse_Sum_z 0.06 0.13 -0.19
## prestigeSum_Gender2:entitlement_rage_Sum_z -0.01 0.15 -0.29
## prestigeSum_Gender2:devaluing_Sum_z -0.09 0.14 -0.37
## prestigeSum_Gender2:exploitativeness_Sum_z -0.05 0.11 -0.27
## leadershipSum_grandiose_fantasy_Sum_z 0.22 0.09 0.05
## leadershipSum_Gender2 -0.32 0.10 -0.52
## leadershipSum_cse_Sum_z 0.11 0.09 -0.07
## leadershipSum_hts_Sum_z -0.13 0.10 -0.33
## leadershipSum_ssse_Sum_z 0.08 0.10 -0.11
## leadershipSum_entitlement_rage_Sum_z -0.07 0.10 -0.27
## leadershipSum_devaluing_Sum_z 0.03 0.10 -0.16
## leadershipSum_exploitativeness_Sum_z 0.54 0.08 0.38
## leadershipSum_Age 0.01 0.01 -0.00
## leadershipSum_grandiose_fantasy_Sum_z:Gender2 -0.13 0.11 -0.35
## leadershipSum_Gender2:cse_Sum_z -0.44 0.14 -0.71
## leadershipSum_Gender2:hts_Sum_z 0.07 0.13 -0.19
## leadershipSum_Gender2:ssse_Sum_z 0.15 0.12 -0.10
## leadershipSum_Gender2:entitlement_rage_Sum_z 0.29 0.14 0.01
## leadershipSum_Gender2:devaluing_Sum_z -0.12 0.14 -0.40
## leadershipSum_Gender2:exploitativeness_Sum_z -0.09 0.11 -0.30
## u-95% CI Rhat Bulk_ESS Tail_ESS
## dominanceSum_Intercept 0.22 1.00 65546 29252
## prestigeSum_Intercept 0.55 1.00 65528 31723
## leadershipSum_Intercept 0.30 1.00 68597 31315
## dominanceSum_grandiose_fantasy_Sum_z 0.14 1.00 35228 31695
## dominanceSum_Gender2 0.49 1.00 78606 29339
## dominanceSum_cse_Sum_z 0.28 1.00 39615 30588
## dominanceSum_hts_Sum_z 0.27 1.00 38974 32206
## dominanceSum_ssse_Sum_z 0.17 1.00 36969 30454
## dominanceSum_entitlement_rage_Sum_z 0.47 1.00 33043 30853
## dominanceSum_devaluing_Sum_z 0.18 1.00 36053 32072
## dominanceSum_exploitativeness_Sum_z 0.52 1.00 45246 30742
## dominanceSum_Age 0.01 1.00 62399 28445
## dominanceSum_grandiose_fantasy_Sum_z:Gender2 0.09 1.00 37145 31055
## dominanceSum_Gender2:cse_Sum_z 0.23 1.00 43313 30763
## dominanceSum_Gender2:hts_Sum_z 0.17 1.00 42326 30516
## dominanceSum_Gender2:ssse_Sum_z 0.10 1.00 37691 30453
## dominanceSum_Gender2:entitlement_rage_Sum_z 0.55 1.00 33180 31533
## dominanceSum_Gender2:devaluing_Sum_z 0.24 1.00 36336 31862
## dominanceSum_Gender2:exploitativeness_Sum_z 0.14 1.00 44847 29414
## prestigeSum_grandiose_fantasy_Sum_z 0.44 1.00 34681 32077
## prestigeSum_Gender2 0.06 1.00 61606 28872
## prestigeSum_cse_Sum_z 0.38 1.00 37151 31852
## prestigeSum_hts_Sum_z -0.03 1.00 33802 31059
## prestigeSum_ssse_Sum_z 0.44 1.00 38073 31837
## prestigeSum_entitlement_rage_Sum_z 0.43 1.00 32689 31454
## prestigeSum_devaluing_Sum_z 0.15 1.00 34292 30933
## prestigeSum_exploitativeness_Sum_z 0.36 1.00 40542 31763
## prestigeSum_Age 0.01 1.00 66521 32441
## prestigeSum_grandiose_fantasy_Sum_z:Gender2 0.09 1.00 37552 31073
## prestigeSum_Gender2:cse_Sum_z 0.14 1.00 38864 31145
## prestigeSum_Gender2:hts_Sum_z 0.35 1.00 35884 31027
## prestigeSum_Gender2:ssse_Sum_z 0.31 1.00 37504 31350
## prestigeSum_Gender2:entitlement_rage_Sum_z 0.27 1.00 35539 31501
## prestigeSum_Gender2:devaluing_Sum_z 0.19 1.00 37264 31855
## prestigeSum_Gender2:exploitativeness_Sum_z 0.17 1.00 42579 31378
## leadershipSum_grandiose_fantasy_Sum_z 0.39 1.00 34695 29533
## leadershipSum_Gender2 -0.12 1.00 67368 30532
## leadershipSum_cse_Sum_z 0.28 1.00 34608 29532
## leadershipSum_hts_Sum_z 0.06 1.00 34957 32050
## leadershipSum_ssse_Sum_z 0.28 1.00 35803 29514
## leadershipSum_entitlement_rage_Sum_z 0.13 1.00 29396 31029
## leadershipSum_devaluing_Sum_z 0.23 1.00 32448 30660
## leadershipSum_exploitativeness_Sum_z 0.69 1.00 38092 31026
## leadershipSum_Age 0.02 1.00 67692 31093
## leadershipSum_grandiose_fantasy_Sum_z:Gender2 0.10 1.00 37841 30633
## leadershipSum_Gender2:cse_Sum_z -0.17 1.00 37250 30950
## leadershipSum_Gender2:hts_Sum_z 0.33 1.00 36925 31637
## leadershipSum_Gender2:ssse_Sum_z 0.39 1.00 36404 30926
## leadershipSum_Gender2:entitlement_rage_Sum_z 0.57 1.00 29757 30121
## leadershipSum_Gender2:devaluing_Sum_z 0.15 1.00 33607 30937
## leadershipSum_Gender2:exploitativeness_Sum_z 0.12 1.00 40784 31059
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma_dominanceSum 0.78 0.03 0.71 0.85 1.00 77559 29297
## sigma_prestigeSum 0.83 0.04 0.76 0.90 1.00 76189 28896
## sigma_leadershipSum 0.81 0.04 0.74 0.88 1.00 64277 28367
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## rescor(dominanceSum,prestigeSum) 0.09 0.06 -0.03 0.21 1.00
## rescor(dominanceSum,leadershipSum) 0.12 0.06 -0.00 0.24 1.00
## rescor(prestigeSum,leadershipSum) 0.28 0.06 0.17 0.39 1.00
## Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 61651 30006
## rescor(dominanceSum,leadershipSum) 62616 30390
## rescor(prestigeSum,leadershipSum) 60740 29075
##
## 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).
dopl_PNI_1_int_hdi <- bayestestR::hdi(dopl_PNI_1_int, effects = "fixed", component = "conditional", ci = .95)
kable(dopl_PNI_1_int_hdi[
sign(dopl_PNI_1_int_hdi$CI_low) == sign(dopl_PNI_1_int_hdi$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_dominanceSum_Gender2
|
0.95
|
0.11
|
0.50
|
|
b_dominanceSum_entitlement_rage_Sum_z
|
0.95
|
0.08
|
0.46
|
|
b_dominanceSum_exploitativeness_Sum_z
|
0.95
|
0.22
|
0.51
|
|
b_dominanceSum_Gender2:entitlement_rage_Sum_z
|
0.95
|
0.02
|
0.56
|
|
b_prestigeSum_grandiose_fantasy_Sum_z
|
0.95
|
0.09
|
0.44
|
|
b_prestigeSum_cse_Sum_z
|
0.95
|
0.02
|
0.38
|
|
b_prestigeSum_hts_Sum_z
|
0.95
|
-0.43
|
-0.03
|
|
b_prestigeSum_ssse_Sum_z
|
0.95
|
0.05
|
0.44
|
|
b_prestigeSum_entitlement_rage_Sum_z
|
0.95
|
0.03
|
0.43
|
|
b_prestigeSum_exploitativeness_Sum_z
|
0.95
|
0.05
|
0.36
|
|
b_leadershipSum_grandiose_fantasy_Sum_z
|
0.95
|
0.05
|
0.39
|
|
b_leadershipSum_Gender2
|
0.95
|
-0.52
|
-0.13
|
|
b_leadershipSum_exploitativeness_Sum_z
|
0.95
|
0.39
|
0.69
|
|
b_leadershipSum_Gender2:cse_Sum_z
|
0.95
|
-0.70
|
-0.16
|
|
b_leadershipSum_Gender2:entitlement_rage_Sum_z
|
0.95
|
0.02
|
0.58
|
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
## dominanceSum_Intercept -0.38 0.24 -0.85 0.09
## prestigeSum_Intercept 0.49 0.25 -0.01 0.99
## leadershipSum_Intercept -0.04 0.26 -0.54 0.47
## dominanceSum_ethicalPreference_z 0.25 0.06 0.13 0.37
## dominanceSum_financialPreference_z -0.18 0.02 -0.21 -0.14
## dominanceSum_socialPreference_z -0.07 0.06 -0.19 0.05
## dominanceSum_healthAndSafetyPreference_z 0.04 0.05 -0.06 0.13
## dominanceSum_recreationalPreference_z 0.05 0.06 -0.06 0.16
## dominanceSum_PNI_Sum_z 0.44 0.06 0.33 0.56
## dominanceSum_Gender 0.27 0.11 0.05 0.50
## dominanceSum_Age -0.00 0.01 -0.01 0.01
## prestigeSum_ethicalPreference_z -0.03 0.07 -0.17 0.10
## prestigeSum_financialPreference_z 0.06 0.06 -0.05 0.18
## prestigeSum_socialPreference_z -0.24 0.01 -0.27 -0.21
## prestigeSum_healthAndSafetyPreference_z -0.06 0.06 -0.18 0.06
## prestigeSum_recreationalPreference_z -0.07 0.06 -0.18 0.05
## prestigeSum_PNI_Sum_z 0.51 0.06 0.39 0.63
## prestigeSum_Gender -0.14 0.12 -0.38 0.09
## prestigeSum_Age -0.01 0.01 -0.02 0.00
## leadershipSum_ethicalPreference_z -0.17 0.07 -0.30 -0.03
## leadershipSum_financialPreference_z 0.05 0.05 -0.06 0.15
## leadershipSum_socialPreference_z 0.03 0.05 -0.08 0.13
## leadershipSum_healthAndSafetyPreference_z 0.04 0.06 -0.07 0.16
## leadershipSum_recreationalPreference_z -0.04 0.05 -0.13 0.05
## leadershipSum_PNI_Sum_z 0.33 0.06 0.21 0.45
## leadershipSum_Gender -0.09 0.12 -0.32 0.15
## leadershipSum_Age 0.01 0.01 -0.01 0.02
## Rhat Bulk_ESS Tail_ESS
## dominanceSum_Intercept 1.00 53318 29544
## prestigeSum_Intercept 1.00 53433 29684
## leadershipSum_Intercept 1.00 50007 30074
## dominanceSum_ethicalPreference_z 1.00 53819 29932
## dominanceSum_financialPreference_z 1.00 64165 27283
## dominanceSum_socialPreference_z 1.00 47382 30378
## dominanceSum_healthAndSafetyPreference_z 1.00 53320 28843
## dominanceSum_recreationalPreference_z 1.00 54941 28500
## dominanceSum_PNI_Sum_z 1.00 51802 29625
## dominanceSum_Gender 1.00 49194 30708
## dominanceSum_Age 1.00 53189 30116
## prestigeSum_ethicalPreference_z 1.00 47042 30439
## prestigeSum_financialPreference_z 1.00 54571 28624
## prestigeSum_socialPreference_z 1.00 63867 26771
## prestigeSum_healthAndSafetyPreference_z 1.00 50923 29456
## prestigeSum_recreationalPreference_z 1.00 54568 29650
## prestigeSum_PNI_Sum_z 1.00 51650 29241
## prestigeSum_Gender 1.00 51286 29551
## prestigeSum_Age 1.00 50262 31360
## leadershipSum_ethicalPreference_z 1.00 47061 29654
## leadershipSum_financialPreference_z 1.00 54406 27849
## leadershipSum_socialPreference_z 1.00 51457 28381
## leadershipSum_healthAndSafetyPreference_z 1.00 51853 30577
## leadershipSum_recreationalPreference_z 1.00 55486 28298
## leadershipSum_PNI_Sum_z 1.00 47641 29268
## leadershipSum_Gender 1.00 48322 29732
## leadershipSum_Age 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
## rescor(dominanceSum,prestigeSum) 0.17 0.06 0.04 0.29 1.00
## rescor(dominanceSum,leadershipSum) 0.29 0.06 0.17 0.40 1.00
## rescor(prestigeSum,leadershipSum) 0.43 0.05 0.32 0.52 1.00
## Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 44587 30591
## rescor(dominanceSum,leadershipSum) 44324 30158
## rescor(prestigeSum,leadershipSum) 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
## dominanceSum_Intercept -0.48 0.23 -0.94
## prestigeSum_Intercept 0.16 0.24 -0.31
## leadershipSum_Intercept -0.22 0.25 -0.71
## dominanceSum_ethicalPreference_z 0.05 0.16 -0.26
## dominanceSum_Gender 0.30 0.11 0.08
## dominanceSum_financialPreference_z -0.19 0.02 -0.22
## dominanceSum_socialPreference_z -0.11 0.14 -0.39
## dominanceSum_healthAndSafetyPreference_z -0.05 0.06 -0.17
## dominanceSum_recreationalPreference_z -0.10 0.10 -0.30
## dominanceSum_PNI_Sum_z 0.73 0.17 0.39
## dominanceSum_Age 0.00 0.01 -0.01
## dominanceSum_ethicalPreference_z:Gender 0.08 0.10 -0.11
## dominanceSum_Gender:financialPreference_z 0.11 0.04 0.04
## dominanceSum_Gender:socialPreference_z 0.04 0.10 -0.15
## dominanceSum_Gender:healthAndSafetyPreference_z 0.10 0.06 -0.01
## dominanceSum_Gender:recreationalPreference_z 0.08 0.07 -0.06
## dominanceSum_Gender:PNI_Sum_z -0.19 0.11 -0.40
## prestigeSum_ethicalPreference_z -0.04 0.15 -0.34
## prestigeSum_Gender 0.04 0.11 -0.19
## prestigeSum_financialPreference_z -0.02 0.12 -0.26
## prestigeSum_socialPreference_z -0.25 0.01 -0.28
## prestigeSum_healthAndSafetyPreference_z -0.10 0.10 -0.30
## prestigeSum_recreationalPreference_z -0.16 0.10 -0.35
## prestigeSum_PNI_Sum_z 1.02 0.17 0.68
## prestigeSum_Age -0.01 0.01 -0.02
## prestigeSum_ethicalPreference_z:Gender 0.04 0.09 -0.15
## prestigeSum_Gender:financialPreference_z 0.05 0.08 -0.10
## prestigeSum_Gender:socialPreference_z 0.25 0.04 0.17
## prestigeSum_Gender:healthAndSafetyPreference_z -0.03 0.07 -0.17
## prestigeSum_Gender:recreationalPreference_z 0.02 0.07 -0.11
## prestigeSum_Gender:PNI_Sum_z -0.39 0.11 -0.60
## leadershipSum_ethicalPreference_z -0.09 0.17 -0.43
## leadershipSum_Gender 0.00 0.12 -0.23
## leadershipSum_financialPreference_z -0.08 0.09 -0.25
## leadershipSum_socialPreference_z -0.15 0.07 -0.30
## leadershipSum_healthAndSafetyPreference_z 0.04 0.10 -0.14
## leadershipSum_recreationalPreference_z -0.16 0.06 -0.28
## leadershipSum_PNI_Sum_z 0.73 0.18 0.37
## leadershipSum_Age 0.01 0.01 -0.00
## leadershipSum_ethicalPreference_z:Gender -0.04 0.10 -0.24
## leadershipSum_Gender:financialPreference_z 0.11 0.06 -0.01
## leadershipSum_Gender:socialPreference_z 0.26 0.06 0.14
## leadershipSum_Gender:healthAndSafetyPreference_z -0.06 0.07 -0.19
## leadershipSum_Gender:recreationalPreference_z 0.10 0.05 -0.00
## leadershipSum_Gender:PNI_Sum_z -0.28 0.11 -0.50
## u-95% CI Rhat Bulk_ESS
## dominanceSum_Intercept -0.03 1.00 67805
## prestigeSum_Intercept 0.64 1.00 59904
## leadershipSum_Intercept 0.27 1.00 61014
## dominanceSum_ethicalPreference_z 0.36 1.00 35155
## dominanceSum_Gender 0.52 1.00 57737
## dominanceSum_financialPreference_z -0.16 1.00 70210
## dominanceSum_socialPreference_z 0.18 1.00 32281
## dominanceSum_healthAndSafetyPreference_z 0.07 1.00 41923
## dominanceSum_recreationalPreference_z 0.10 1.00 33948
## dominanceSum_PNI_Sum_z 1.06 1.00 28180
## dominanceSum_Age 0.01 1.00 67383
## dominanceSum_ethicalPreference_z:Gender 0.27 1.00 34063
## dominanceSum_Gender:financialPreference_z 0.18 1.00 67186
## dominanceSum_Gender:socialPreference_z 0.23 1.00 31272
## dominanceSum_Gender:healthAndSafetyPreference_z 0.21 1.00 40919
## dominanceSum_Gender:recreationalPreference_z 0.21 1.00 33233
## dominanceSum_Gender:PNI_Sum_z 0.02 1.00 28221
## prestigeSum_ethicalPreference_z 0.26 1.00 30409
## prestigeSum_Gender 0.26 1.00 46722
## prestigeSum_financialPreference_z 0.22 1.00 34113
## prestigeSum_socialPreference_z -0.22 1.00 69063
## prestigeSum_healthAndSafetyPreference_z 0.10 1.00 37200
## prestigeSum_recreationalPreference_z 0.03 1.00 34624
## prestigeSum_PNI_Sum_z 1.36 1.00 24610
## prestigeSum_Age 0.00 1.00 67893
## prestigeSum_ethicalPreference_z:Gender 0.22 1.00 29008
## prestigeSum_Gender:financialPreference_z 0.21 1.00 33236
## prestigeSum_Gender:socialPreference_z 0.33 1.00 55379
## prestigeSum_Gender:healthAndSafetyPreference_z 0.12 1.00 36128
## prestigeSum_Gender:recreationalPreference_z 0.15 1.00 32912
## prestigeSum_Gender:PNI_Sum_z -0.17 1.00 24205
## leadershipSum_ethicalPreference_z 0.24 1.00 32042
## leadershipSum_Gender 0.24 1.00 50626
## leadershipSum_financialPreference_z 0.10 1.00 35559
## leadershipSum_socialPreference_z -0.01 1.00 38242
## leadershipSum_healthAndSafetyPreference_z 0.23 1.00 33978
## leadershipSum_recreationalPreference_z -0.04 1.00 47799
## leadershipSum_PNI_Sum_z 1.08 1.00 26329
## leadershipSum_Age 0.02 1.00 61372
## leadershipSum_ethicalPreference_z:Gender 0.17 1.00 30378
## leadershipSum_Gender:financialPreference_z 0.23 1.00 34759
## leadershipSum_Gender:socialPreference_z 0.39 1.00 34480
## leadershipSum_Gender:healthAndSafetyPreference_z 0.08 1.00 32256
## leadershipSum_Gender:recreationalPreference_z 0.21 1.00 40400
## leadershipSum_Gender:PNI_Sum_z -0.06 1.00 26340
## Tail_ESS
## dominanceSum_Intercept 28486
## prestigeSum_Intercept 27068
## leadershipSum_Intercept 30136
## dominanceSum_ethicalPreference_z 28773
## dominanceSum_Gender 28527
## dominanceSum_financialPreference_z 28124
## dominanceSum_socialPreference_z 28617
## dominanceSum_healthAndSafetyPreference_z 30574
## dominanceSum_recreationalPreference_z 28463
## dominanceSum_PNI_Sum_z 27644
## dominanceSum_Age 27434
## dominanceSum_ethicalPreference_z:Gender 27880
## dominanceSum_Gender:financialPreference_z 26517
## dominanceSum_Gender:socialPreference_z 28385
## dominanceSum_Gender:healthAndSafetyPreference_z 31285
## dominanceSum_Gender:recreationalPreference_z 28701
## dominanceSum_Gender:PNI_Sum_z 27421
## prestigeSum_ethicalPreference_z 28755
## prestigeSum_Gender 29554
## prestigeSum_financialPreference_z 29108
## prestigeSum_socialPreference_z 27704
## prestigeSum_healthAndSafetyPreference_z 30174
## prestigeSum_recreationalPreference_z 28837
## prestigeSum_PNI_Sum_z 26951
## prestigeSum_Age 28833
## prestigeSum_ethicalPreference_z:Gender 27913
## prestigeSum_Gender:financialPreference_z 28956
## prestigeSum_Gender:socialPreference_z 28801
## prestigeSum_Gender:healthAndSafetyPreference_z 29883
## prestigeSum_Gender:recreationalPreference_z 28138
## prestigeSum_Gender:PNI_Sum_z 27215
## leadershipSum_ethicalPreference_z 28923
## leadershipSum_Gender 29030
## leadershipSum_financialPreference_z 28104
## leadershipSum_socialPreference_z 29965
## leadershipSum_healthAndSafetyPreference_z 28757
## leadershipSum_recreationalPreference_z 30766
## leadershipSum_PNI_Sum_z 27324
## leadershipSum_Age 29159
## leadershipSum_ethicalPreference_z:Gender 28243
## leadershipSum_Gender:financialPreference_z 28598
## leadershipSum_Gender:socialPreference_z 29579
## leadershipSum_Gender:healthAndSafetyPreference_z 29093
## leadershipSum_Gender:recreationalPreference_z 29995
## leadershipSum_Gender:PNI_Sum_z 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
## rescor(dominanceSum,prestigeSum) 0.14 0.06 0.03 0.26 1.00
## rescor(dominanceSum,leadershipSum) 0.27 0.06 0.15 0.38 1.00
## rescor(prestigeSum,leadershipSum) 0.37 0.05 0.27 0.47 1.00
## Bulk_ESS Tail_ESS
## rescor(dominanceSum,prestigeSum) 51867 28446
## rescor(dominanceSum,leadershipSum) 52577 29982
## rescor(prestigeSum,leadershipSum) 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_z)
m2_model.1 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z)
m3_model.1 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + PNI_Sum_z)
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_z)
m2_model.2 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z)
m3_model.2 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z)
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_z + Age)
m2_model.3 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z + Age)
m3_model.3 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z + 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_z + Age)
m2_model.4 <- bf(riskBenefitSum ~ dominance_Sum * Gender + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z + Age)
m3_model.4 <- bf(riskSum ~ dominance_Sum * Gender + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z + 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_z + Age)
m2_model.5 <- bf(riskBenefitSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum + Gender + PNI_Sum_z + Age)
m3_model.5 <- bf(riskSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum + Gender + PNI_Sum_z + 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_z + Age)
m2_model.6 <- bf(riskBenefitSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Gender + PNI_Sum_z + Age)
m3_model.6 <- bf(riskSum ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Gender + PNI_Sum_z + 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_z + Age)
m2_model.4 <- bf(riskBenefitSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z + Age)
m3_model.4 <- bf(riskSum ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + PNI_Sum_z + 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 7190.4 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 -3591.1 26.1
## p_loo 20.6 1.6
## looic 7182.2 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 -3581.1 26.6
## p_loo 23.3 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 -3580.9 26.6
## p_loo 23.1 1.7
## looic 7161.9 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.4 0.0 0.0
## mediation_model.3 -0.1 0.0
## mediation_model.2 -10.2 6.3
## mediation_model.1 -14.3 7.7
## mediation_model -16.0 8.1
print(mediation_comparison)
## Bayes Factors for Model Comparison
##
## Model BF
## [1] 1.83e-13
## [2] 6.36e-10
## [3] 1.32e-04
## [4] 1.00
##
## * 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 (0.4-3) 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 (0.4-3) 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 (0.4-3) 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 (0.4-3) 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, sample = 5000,
auto.cov.lv.x = TRUE, bcontrol = list(cores = parallel::detectCores())
)
summary(fit_bayes.5)
## blavaan (0.4-3) results of 1000 samples after 500 adapt/burnin iterations
##
## Number of observations 279
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value NA 0.411
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Risk_Benefit =~
## dmnnc_S 1.000
## prstg_S 2.102 0.679 1.242 3.872 1.010 normal(0,10)
## ldrsh_S 1.609 0.379 1.045 2.520 1.007 normal(0,10)
## Gender -0.046 0.277 -0.670 0.551 1.011 normal(0,10)
## Age -5.223 2.807 -10.915 0.590 1.006 normal(0,10)
## Risk_Sum =~
## Gender (dm_S) 1.000
## Age 1.704 9.353 -17.154 20.360 1.004 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Risk_Benefit ~
## Risk_Sum 0.032 6.144 -13.288 13.627 1.002 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .dominance_Sum 0.006 0.060 -0.113 0.120 1.000 normal(0,32)
## .prestige_Sum 0.002 0.061 -0.116 0.119 1.001 normal(0,32)
## .leadership_Sum 0.005 0.063 -0.121 0.135 1.002 normal(0,32)
## .Gender 1.557 0.031 1.495 1.617 1.005 normal(0,32)
## .Age 29.524 0.586 28.381 30.670 1.001 normal(0,32)
## .Risk_Benefit 0.000
## Risk_Sum 0.000
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .dominance_Sum 0.859 0.091 0.688 1.035 1.014 gamma(1,.5)[sd]
## .prestige_Sum 0.415 0.149 0.036 0.661 1.006 gamma(1,.5)[sd]
## .leadership_Sum 0.643 0.099 0.448 0.837 1.004 gamma(1,.5)[sd]
## .Gender 0.241 0.030 0.176 0.294 1.013 gamma(1,.5)[sd]
## .Age 92.745 8.226 78.234 109.662 1.005 gamma(1,.5)[sd]
## .Risk_Benefit 0.095 0.070 0.001 0.253 1.009 gamma(1,.5)[sd]
## Risk_Sum 0.022 0.042 0.000 0.142 1.035 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
## riskSum_Intercept 9.88 1.26 7.43
## riskPerceptionSum_Intercept 13.83 1.25 11.37
## riskBenefitSum_Intercept 7.61 0.96 5.75
## riskSum_ethicalPreference_z -0.07 0.23 -0.52
## riskSum_financialPreference_z -0.13 0.07 -0.26
## riskSum_socialPreference_z -0.05 0.10 -0.25
## riskSum_healthAndSafetyPreference_z -0.09 0.20 -0.48
## riskSum_recreationalPreference_z -0.15 0.14 -0.42
## riskSum_PNI_Sum_z 1.27 0.72 -0.13
## riskSum_Gender 0.29 0.50 -0.69
## riskSum_Age -0.03 0.01 -0.04
## riskPerceptionSum_ethicalPreference_z -0.46 0.20 -0.84
## riskPerceptionSum_financialPreference_z -0.39 0.25 -0.88
## riskPerceptionSum_socialPreference_z -0.03 0.10 -0.23
## riskPerceptionSum_healthAndSafetyPreference_z -0.22 0.02 -0.26
## riskPerceptionSum_recreationalPreference_z -6.41 0.85 -8.06
## riskPerceptionSum_PNI_Sum_z -0.76 0.83 -2.39
## riskPerceptionSum_Gender -0.63 0.35 -1.31
## riskPerceptionSum_Age -0.01 0.02 -0.05
## riskBenefitSum_ethicalPreference_z 0.33 0.12 0.09
## riskBenefitSum_financialPreference_z 0.34 0.12 0.09
## riskBenefitSum_socialPreference_z 0.10 0.06 -0.02
## riskBenefitSum_healthAndSafetyPreference_z 0.18 0.09 0.02
## riskBenefitSum_recreationalPreference_z 0.14 0.08 -0.02
## riskBenefitSum_PNI_Sum_z 0.82 0.64 -0.45
## riskBenefitSum_Gender -0.05 0.05 -0.13
## riskBenefitSum_Age -0.00 0.00 -0.01
## u-95% CI Rhat Bulk_ESS Tail_ESS
## riskSum_Intercept 12.36 1.00 72123 30697
## riskPerceptionSum_Intercept 16.28 1.00 82917 29142
## riskBenefitSum_Intercept 9.49 1.00 57137 31137
## riskSum_ethicalPreference_z 0.39 1.00 85102 26592
## riskSum_financialPreference_z 0.01 1.00 86150 24540
## riskSum_socialPreference_z 0.14 1.00 96780 26218
## riskSum_healthAndSafetyPreference_z 0.29 1.00 93799 26986
## riskSum_recreationalPreference_z 0.12 1.00 88329 27659
## riskSum_PNI_Sum_z 2.69 1.00 60262 30914
## riskSum_Gender 1.27 1.00 98748 27309
## riskSum_Age -0.01 1.00 83706 27591
## riskPerceptionSum_ethicalPreference_z -0.07 1.00 85873 26295
## riskPerceptionSum_financialPreference_z 0.10 1.00 80480 27806
## riskPerceptionSum_socialPreference_z 0.16 1.00 100188 25748
## riskPerceptionSum_healthAndSafetyPreference_z -0.19 1.00 88256 26501
## riskPerceptionSum_recreationalPreference_z -4.73 1.00 81635 28786
## riskPerceptionSum_PNI_Sum_z 0.87 1.00 76646 28893
## riskPerceptionSum_Gender 0.05 1.00 98350 27165
## riskPerceptionSum_Age 0.02 1.00 91420 28286
## riskBenefitSum_ethicalPreference_z 0.57 1.00 82592 27362
## riskBenefitSum_financialPreference_z 0.58 1.00 83311 27025
## riskBenefitSum_socialPreference_z 0.22 1.00 92496 27209
## riskBenefitSum_healthAndSafetyPreference_z 0.35 1.00 85515 28013
## riskBenefitSum_recreationalPreference_z 0.29 1.00 88803 26428
## riskBenefitSum_PNI_Sum_z 2.07 1.00 61314 31860
## riskBenefitSum_Gender 0.04 1.00 98112 26616
## riskBenefitSum_Age 0.00 1.00 36041 24182
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_riskSum 29.57 0.40 28.80 30.35 1.00 61375
## sigma_riskPerceptionSum 38.23 0.45 37.36 39.10 1.00 66665
## sigma_riskBenefitSum 25.73 0.37 25.01 26.46 1.00 56797
## Tail_ESS
## sigma_riskSum 29396
## sigma_riskPerceptionSum 30650
## sigma_riskBenefitSum 31232
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(riskSum,riskPerceptionSum) 0.79 0.01 0.76 0.81
## rescor(riskSum,riskBenefitSum) 0.88 0.01 0.86 0.89
## rescor(riskPerceptionSum,riskBenefitSum) 0.82 0.01 0.80 0.84
## Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 1.00 71999 32042
## rescor(riskSum,riskBenefitSum) 1.00 69892 30642
## rescor(riskPerceptionSum,riskBenefitSum) 1.00 71859 32695
##
## 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
|
7.43
|
12.36
|
|
b_riskSum_Age
|
0.95
|
-0.04
|
-0.01
|
|
b_riskPerceptionSum_Intercept
|
0.95
|
11.44
|
16.33
|
|
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
|
-8.04
|
-4.72
|
|
b_riskBenefitSum_Intercept
|
0.95
|
5.73
|
9.48
|
|
b_riskBenefitSum_ethicalPreference_z
|
0.95
|
0.09
|
0.57
|
|
b_riskBenefitSum_financialPreference_z
|
0.95
|
0.09
|
0.58
|
|
b_riskBenefitSum_healthAndSafetyPreference_z
|
0.95
|
0.02
|
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
## riskSum_Intercept 9.37 1.40 6.62 12.12 1.00
## riskPerceptionSum_Intercept 14.68 1.04 12.64 16.74 1.00
## riskBenefitSum_Intercept 9.06 1.57 5.98 12.13 1.00
## riskSum_dominance_Sum 1.04 0.51 0.04 2.03 1.00
## riskSum_prestige_Sum 0.16 0.34 -0.49 0.82 1.00
## riskSum_leadership_Sum -0.22 0.18 -0.56 0.13 1.00
## riskSum_PNI_Sum_z 4.25 1.78 0.80 7.76 1.00
## riskSum_Gender 0.37 0.52 -0.65 1.38 1.00
## riskSum_Age -0.02 0.02 -0.05 0.02 1.00
## riskPerceptionSum_dominance_Sum -3.09 0.86 -4.79 -1.41 1.00
## riskPerceptionSum_prestige_Sum 0.07 0.40 -0.72 0.86 1.00
## riskPerceptionSum_leadership_Sum -0.16 0.23 -0.61 0.29 1.00
## riskPerceptionSum_PNI_Sum_z 2.11 2.33 -2.44 6.73 1.00
## riskPerceptionSum_Gender -0.64 0.20 -1.03 -0.24 1.00
## riskPerceptionSum_Age -0.04 0.00 -0.05 -0.03 1.00
## riskBenefitSum_dominance_Sum 0.55 0.41 -0.25 1.35 1.00
## riskBenefitSum_prestige_Sum -0.30 0.32 -0.92 0.31 1.00
## riskBenefitSum_leadership_Sum -0.20 0.17 -0.54 0.14 1.00
## riskBenefitSum_PNI_Sum_z 3.53 1.57 0.45 6.62 1.00
## riskBenefitSum_Gender 0.17 0.62 -1.04 1.38 1.00
## riskBenefitSum_Age -0.06 0.03 -0.11 -0.00 1.00
## Bulk_ESS Tail_ESS
## riskSum_Intercept 75585 30399
## riskPerceptionSum_Intercept 72007 29691
## riskBenefitSum_Intercept 73906 27928
## riskSum_dominance_Sum 80708 25470
## riskSum_prestige_Sum 84374 27802
## riskSum_leadership_Sum 90789 27746
## riskSum_PNI_Sum_z 33717 29857
## riskSum_Gender 87391 27976
## riskSum_Age 83645 27312
## riskPerceptionSum_dominance_Sum 82355 27930
## riskPerceptionSum_prestige_Sum 90829 28180
## riskPerceptionSum_leadership_Sum 89242 28447
## riskPerceptionSum_PNI_Sum_z 34866 30561
## riskPerceptionSum_Gender 89673 26947
## riskPerceptionSum_Age 45067 24737
## riskBenefitSum_dominance_Sum 83883 29329
## riskBenefitSum_prestige_Sum 78875 26889
## riskBenefitSum_leadership_Sum 83861 26634
## riskBenefitSum_PNI_Sum_z 34172 30157
## riskBenefitSum_Gender 84761 26734
## riskBenefitSum_Age 82393 27059
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_riskSum 29.56 0.40 28.79 30.36 1.00 66927
## sigma_riskPerceptionSum 38.42 0.45 37.54 39.30 1.00 65585
## sigma_riskBenefitSum 26.00 0.38 25.27 26.74 1.00 56606
## Tail_ESS
## sigma_riskSum 28314
## sigma_riskPerceptionSum 30771
## sigma_riskBenefitSum 31845
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(riskSum,riskPerceptionSum) 0.78 0.01 0.76 0.80
## rescor(riskSum,riskBenefitSum) 0.87 0.01 0.85 0.89
## rescor(riskPerceptionSum,riskBenefitSum) 0.78 0.01 0.76 0.81
## Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 1.00 63179 28918
## rescor(riskSum,riskBenefitSum) 1.00 76432 31033
## rescor(riskPerceptionSum,riskBenefitSum) 1.00 77337 29499
##
## 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
|
6.71
|
12.21
|
|
b_riskSum_dominance_Sum
|
0.95
|
0.04
|
2.03
|
|
b_riskSum_PNI_Sum_z
|
0.95
|
0.81
|
7.76
|
|
b_riskPerceptionSum_Intercept
|
0.95
|
12.63
|
16.72
|
|
b_riskPerceptionSum_dominance_Sum
|
0.95
|
-4.72
|
-1.36
|
|
b_riskPerceptionSum_Gender
|
0.95
|
-1.04
|
-0.25
|
|
b_riskPerceptionSum_Age
|
0.95
|
-0.05
|
-0.03
|
|
b_riskBenefitSum_Intercept
|
0.95
|
6.04
|
12.18
|
|
b_riskBenefitSum_PNI_Sum_z
|
0.95
|
0.49
|
6.65
|
|
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
## riskSum_Intercept 9.27 1.40 6.51 12.03
## riskPerceptionSum_Intercept 14.85 1.04 12.80 16.90
## riskBenefitSum_Intercept 9.20 1.57 6.12 12.27
## riskSum_dominance_Sum 0.56 0.49 -0.39 1.53
## riskSum_Gender 0.32 0.52 -0.70 1.36
## riskSum_prestige_Sum 0.22 0.42 -0.60 1.03
## riskSum_leadership_Sum -0.31 0.19 -0.68 0.06
## riskSum_PNI_Sum_z 0.39 0.88 -1.35 2.11
## riskSum_Age -0.01 0.02 -0.05 0.03
## riskSum_dominance_Sum:Gender 1.00 0.50 0.01 1.97
## riskSum_Gender:prestige_Sum -0.30 0.28 -0.85 0.24
## riskSum_Gender:leadership_Sum -0.26 0.39 -1.04 0.50
## riskSum_Gender:PNI_Sum_z 0.78 0.70 -0.61 2.14
## riskPerceptionSum_dominance_Sum -2.22 0.88 -3.94 -0.50
## riskPerceptionSum_Gender -0.63 0.20 -1.02 -0.24
## riskPerceptionSum_prestige_Sum -0.17 0.38 -0.92 0.58
## riskPerceptionSum_leadership_Sum -0.03 0.54 -1.09 1.02
## riskPerceptionSum_PNI_Sum_z -0.19 0.93 -2.01 1.61
## riskPerceptionSum_Age -0.04 0.00 -0.05 -0.03
## riskPerceptionSum_dominance_Sum:Gender -1.39 0.53 -2.43 -0.35
## riskPerceptionSum_Gender:prestige_Sum -0.10 0.52 -1.11 0.91
## riskPerceptionSum_Gender:leadership_Sum 0.29 0.71 -1.11 1.68
## riskPerceptionSum_Gender:PNI_Sum_z 0.25 0.80 -1.31 1.80
## riskBenefitSum_dominance_Sum 0.46 0.46 -0.44 1.37
## riskBenefitSum_Gender 0.12 0.62 -1.09 1.33
## riskBenefitSum_prestige_Sum -0.26 0.45 -1.14 0.62
## riskBenefitSum_leadership_Sum -0.34 0.18 -0.69 0.01
## riskBenefitSum_PNI_Sum_z 0.79 0.85 -0.90 2.44
## riskBenefitSum_Age -0.06 0.03 -0.11 -0.01
## riskBenefitSum_dominance_Sum:Gender 0.64 0.40 -0.15 1.43
## riskBenefitSum_Gender:prestige_Sum -0.66 0.20 -1.05 -0.27
## riskBenefitSum_Gender:leadership_Sum 0.07 0.40 -0.70 0.85
## riskBenefitSum_Gender:PNI_Sum_z 0.27 0.67 -1.03 1.58
## Rhat Bulk_ESS Tail_ESS
## riskSum_Intercept 1.00 71527 30276
## riskPerceptionSum_Intercept 1.00 73465 29965
## riskBenefitSum_Intercept 1.00 80125 29180
## riskSum_dominance_Sum 1.00 82300 30105
## riskSum_Gender 1.00 90760 28170
## riskSum_prestige_Sum 1.00 85702 27969
## riskSum_leadership_Sum 1.00 85577 27169
## riskSum_PNI_Sum_z 1.00 65589 28183
## riskSum_Age 1.00 77978 26858
## riskSum_dominance_Sum:Gender 1.00 66255 30249
## riskSum_Gender:prestige_Sum 1.00 78958 29089
## riskSum_Gender:leadership_Sum 1.00 66273 31633
## riskSum_Gender:PNI_Sum_z 1.00 56102 29631
## riskPerceptionSum_dominance_Sum 1.00 76158 30137
## riskPerceptionSum_Gender 1.00 83914 27246
## riskPerceptionSum_prestige_Sum 1.00 80715 26438
## riskPerceptionSum_leadership_Sum 1.00 78150 30203
## riskPerceptionSum_PNI_Sum_z 1.00 71248 28203
## riskPerceptionSum_Age 1.00 39379 24609
## riskPerceptionSum_dominance_Sum:Gender 1.00 76112 29080
## riskPerceptionSum_Gender:prestige_Sum 1.00 85798 28884
## riskPerceptionSum_Gender:leadership_Sum 1.00 70216 29134
## riskPerceptionSum_Gender:PNI_Sum_z 1.00 64270 29994
## riskBenefitSum_dominance_Sum 1.00 76822 27601
## riskBenefitSum_Gender 1.00 84383 27773
## riskBenefitSum_prestige_Sum 1.00 80232 29637
## riskBenefitSum_leadership_Sum 1.00 83005 28088
## riskBenefitSum_PNI_Sum_z 1.00 67907 29959
## riskBenefitSum_Age 1.00 79270 27634
## riskBenefitSum_dominance_Sum:Gender 1.00 66260 30264
## riskBenefitSum_Gender:prestige_Sum 1.00 79002 29052
## riskBenefitSum_Gender:leadership_Sum 1.00 64258 31488
## riskBenefitSum_Gender:PNI_Sum_z 1.00 61250 30766
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_riskSum 29.54 0.40 28.76 30.32 1.00 64333
## sigma_riskPerceptionSum 38.39 0.45 37.52 39.28 1.00 68845
## sigma_riskBenefitSum 25.97 0.37 25.26 26.71 1.00 65638
## Tail_ESS
## sigma_riskSum 28997
## sigma_riskPerceptionSum 30940
## sigma_riskBenefitSum 30084
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(riskSum,riskPerceptionSum) 0.78 0.01 0.76 0.80
## rescor(riskSum,riskBenefitSum) 0.87 0.01 0.85 0.89
## rescor(riskPerceptionSum,riskBenefitSum) 0.79 0.01 0.77 0.81
## Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 1.00 70332 29943
## rescor(riskSum,riskBenefitSum) 1.00 65275 30153
## rescor(riskPerceptionSum,riskBenefitSum) 1.00 77517 28418
##
## 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
|
6.47
|
11.99
|
|
b_riskPerceptionSum_Intercept
|
0.95
|
12.82
|
16.91
|
|
b_riskPerceptionSum_dominance_Sum
|
0.95
|
-3.94
|
-0.51
|
|
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
|
-2.41
|
-0.34
|
|
b_riskBenefitSum_Intercept
|
0.95
|
6.12
|
12.26
|
|
b_riskBenefitSum_Age
|
0.95
|
-0.11
|
-0.01
|
|
b_riskBenefitSum_Gender:prestige_Sum
|
0.95
|
-1.05
|
-0.27
|
m_7 <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z) ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_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_m7)
summary(m_7)
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: vulnerability_Sum_z ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
## grandiosity_Sum_z ~ 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 = 500; thin = 1;
## total post-warmup draws = 38000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## vulnerabilitySumz_Intercept 0.95 0.26 0.45
## grandiositySumz_Intercept 0.12 0.25 -0.38
## vulnerabilitySumz_ethicalPreference_z 0.16 0.08 0.01
## vulnerabilitySumz_financialPreference_z -0.12 0.06 -0.24
## vulnerabilitySumz_socialPreference_z 0.14 0.07 0.01
## vulnerabilitySumz_healthAndSafetyPreference_z 0.05 0.08 -0.10
## vulnerabilitySumz_recreationalPreference_z -0.06 0.07 -0.20
## vulnerabilitySumz_Gender -0.04 0.12 -0.28
## vulnerabilitySumz_Age -0.03 0.01 -0.04
## grandiositySumz_ethicalPreference_z 0.04 0.08 -0.11
## grandiositySumz_financialPreference_z 0.02 0.06 -0.11
## grandiositySumz_socialPreference_z 0.36 0.07 0.22
## grandiositySumz_healthAndSafetyPreference_z -0.08 0.08 -0.23
## grandiositySumz_recreationalPreference_z -0.06 0.07 -0.20
## grandiositySumz_Gender 0.30 0.12 0.06
## grandiositySumz_Age -0.02 0.01 -0.03
## u-95% CI Rhat Bulk_ESS Tail_ESS
## vulnerabilitySumz_Intercept 1.45 1.00 49384 32369
## grandiositySumz_Intercept 0.61 1.00 47924 32268
## vulnerabilitySumz_ethicalPreference_z 0.31 1.00 34695 30651
## vulnerabilitySumz_financialPreference_z 0.01 1.00 41035 30532
## vulnerabilitySumz_socialPreference_z 0.28 1.00 39780 31067
## vulnerabilitySumz_healthAndSafetyPreference_z 0.20 1.00 36592 30217
## vulnerabilitySumz_recreationalPreference_z 0.08 1.00 39092 30188
## vulnerabilitySumz_Gender 0.21 1.00 43080 30148
## vulnerabilitySumz_Age -0.02 1.00 53041 32437
## grandiositySumz_ethicalPreference_z 0.19 1.00 33195 29780
## grandiositySumz_financialPreference_z 0.14 1.00 39576 29769
## grandiositySumz_socialPreference_z 0.48 1.00 39165 31947
## grandiositySumz_healthAndSafetyPreference_z 0.07 1.00 34549 28614
## grandiositySumz_recreationalPreference_z 0.07 1.00 38387 30900
## grandiositySumz_Gender 0.54 1.00 42072 31012
## grandiositySumz_Age -0.01 1.00 50462 32810
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.94 0.04 0.86 1.02 1.00 44064
## sigma_grandiositySumz 0.93 0.04 0.86 1.01 1.00 47034
## Tail_ESS
## sigma_vulnerabilitySumz 29899
## sigma_grandiositySumz 29844
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.48 0.05 0.39 0.57
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 41551 30111
##
## 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).
m_7_hdi <- bayestestR::hdi(m_7, effects = "fixed", component = "conditional", ci = .95)
kable(m_7_hdi[
sign(m_7_hdi$CI_low) == sign(m_7_hdi$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_vulnerabilitySumz_Intercept
|
0.95
|
0.45
|
1.46
|
|
b_vulnerabilitySumz_ethicalPreference_z
|
0.95
|
0.01
|
0.31
|
|
b_vulnerabilitySumz_socialPreference_z
|
0.95
|
0.01
|
0.28
|
|
b_vulnerabilitySumz_Age
|
0.95
|
-0.04
|
-0.02
|
|
b_grandiositySumz_socialPreference_z
|
0.95
|
0.23
|
0.49
|
|
b_grandiositySumz_Gender
|
0.95
|
0.05
|
0.53
|
|
b_grandiositySumz_Age
|
0.95
|
-0.03
|
-0.01
|
m_7_int <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z) ~ 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 = 500, backend = "cmdstanr", save_pars = save_pars(all = TRUE), prior = prior_m7_int)
summary(m_7_int)
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: vulnerability_Sum_z ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## grandiosity_Sum_z ~ 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 = 500; thin = 1;
## total post-warmup draws = 38000
##
## Population-Level Effects:
## Estimate Est.Error
## vulnerabilitySumz_Intercept 0.87 0.26
## grandiositySumz_Intercept 0.11 0.26
## vulnerabilitySumz_ethicalPreference_z -0.21 0.26
## vulnerabilitySumz_Gender -0.04 0.12
## vulnerabilitySumz_financialPreference_z -0.49 0.22
## vulnerabilitySumz_socialPreference_z 0.09 0.21
## vulnerabilitySumz_healthAndSafetyPreference_z 0.23 0.25
## vulnerabilitySumz_recreationalPreference_z 0.46 0.24
## vulnerabilitySumz_Age -0.03 0.01
## vulnerabilitySumz_ethicalPreference_z:Gender 0.22 0.15
## vulnerabilitySumz_Gender:financialPreference_z 0.24 0.13
## vulnerabilitySumz_Gender:socialPreference_z 0.03 0.13
## vulnerabilitySumz_Gender:healthAndSafetyPreference_z -0.11 0.15
## vulnerabilitySumz_Gender:recreationalPreference_z -0.32 0.14
## grandiositySumz_ethicalPreference_z -0.10 0.26
## grandiositySumz_Gender 0.27 0.12
## grandiositySumz_financialPreference_z -0.16 0.22
## grandiositySumz_socialPreference_z 0.25 0.21
## grandiositySumz_healthAndSafetyPreference_z -0.12 0.24
## grandiositySumz_recreationalPreference_z 0.57 0.23
## grandiositySumz_Age -0.02 0.01
## grandiositySumz_ethicalPreference_z:Gender 0.08 0.15
## grandiositySumz_Gender:financialPreference_z 0.12 0.13
## grandiositySumz_Gender:socialPreference_z 0.06 0.13
## grandiositySumz_Gender:healthAndSafetyPreference_z 0.03 0.15
## grandiositySumz_Gender:recreationalPreference_z -0.39 0.14
## l-95% CI u-95% CI Rhat
## vulnerabilitySumz_Intercept 0.37 1.37 1.00
## grandiositySumz_Intercept -0.39 0.62 1.00
## vulnerabilitySumz_ethicalPreference_z -0.73 0.30 1.00
## vulnerabilitySumz_Gender -0.28 0.20 1.00
## vulnerabilitySumz_financialPreference_z -0.91 -0.07 1.00
## vulnerabilitySumz_socialPreference_z -0.32 0.49 1.00
## vulnerabilitySumz_healthAndSafetyPreference_z -0.25 0.71 1.00
## vulnerabilitySumz_recreationalPreference_z -0.01 0.92 1.00
## vulnerabilitySumz_Age -0.04 -0.02 1.00
## vulnerabilitySumz_ethicalPreference_z:Gender -0.07 0.51 1.00
## vulnerabilitySumz_Gender:financialPreference_z -0.01 0.48 1.00
## vulnerabilitySumz_Gender:socialPreference_z -0.23 0.29 1.00
## vulnerabilitySumz_Gender:healthAndSafetyPreference_z -0.40 0.18 1.00
## vulnerabilitySumz_Gender:recreationalPreference_z -0.59 -0.05 1.00
## grandiositySumz_ethicalPreference_z -0.61 0.40 1.00
## grandiositySumz_Gender 0.03 0.51 1.00
## grandiositySumz_financialPreference_z -0.59 0.26 1.00
## grandiositySumz_socialPreference_z -0.15 0.66 1.00
## grandiositySumz_healthAndSafetyPreference_z -0.60 0.37 1.00
## grandiositySumz_recreationalPreference_z 0.11 1.03 1.00
## grandiositySumz_Age -0.03 -0.01 1.00
## grandiositySumz_ethicalPreference_z:Gender -0.21 0.38 1.00
## grandiositySumz_Gender:financialPreference_z -0.13 0.37 1.00
## grandiositySumz_Gender:socialPreference_z -0.20 0.31 1.00
## grandiositySumz_Gender:healthAndSafetyPreference_z -0.26 0.31 1.00
## grandiositySumz_Gender:recreationalPreference_z -0.66 -0.12 1.00
## Bulk_ESS Tail_ESS
## vulnerabilitySumz_Intercept 57775 31979
## grandiositySumz_Intercept 53839 32163
## vulnerabilitySumz_ethicalPreference_z 23288 25448
## vulnerabilitySumz_Gender 54547 32102
## vulnerabilitySumz_financialPreference_z 27895 27818
## vulnerabilitySumz_socialPreference_z 26210 26796
## vulnerabilitySumz_healthAndSafetyPreference_z 23323 27134
## vulnerabilitySumz_recreationalPreference_z 27084 27444
## vulnerabilitySumz_Age 61622 30981
## vulnerabilitySumz_ethicalPreference_z:Gender 23496 26442
## vulnerabilitySumz_Gender:financialPreference_z 27810 27377
## vulnerabilitySumz_Gender:socialPreference_z 27901 27295
## vulnerabilitySumz_Gender:healthAndSafetyPreference_z 23468 26940
## vulnerabilitySumz_Gender:recreationalPreference_z 27562 28258
## grandiositySumz_ethicalPreference_z 25568 27783
## grandiositySumz_Gender 52490 30492
## grandiositySumz_financialPreference_z 27623 26643
## grandiositySumz_socialPreference_z 26589 27501
## grandiositySumz_healthAndSafetyPreference_z 24801 27838
## grandiositySumz_recreationalPreference_z 27758 28070
## grandiositySumz_Age 57525 32047
## grandiositySumz_ethicalPreference_z:Gender 25806 28136
## grandiositySumz_Gender:financialPreference_z 27084 26454
## grandiositySumz_Gender:socialPreference_z 27656 28353
## grandiositySumz_Gender:healthAndSafetyPreference_z 25323 28425
## grandiositySumz_Gender:recreationalPreference_z 28019 27698
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.92 0.04 0.85 1.01 1.00 53437
## sigma_grandiositySumz 0.92 0.04 0.85 1.00 1.00 55935
## Tail_ESS
## sigma_vulnerabilitySumz 30868
## sigma_grandiositySumz 30877
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.47 0.05 0.37 0.56
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 52424 30488
##
## 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).
m_7_int_hdi <- bayestestR::hdi(m_7_int, effects = "fixed", component = "conditional", ci = .95)
kable(m_7_int_hdi[
sign(m_7_int_hdi$CI_low) == sign(m_7_int_hdi$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_vulnerabilitySumz_Intercept
|
0.95
|
0.37
|
1.37
|
|
b_vulnerabilitySumz_financialPreference_z
|
0.95
|
-0.92
|
-0.07
|
|
b_vulnerabilitySumz_Age
|
0.95
|
-0.04
|
-0.02
|
|
b_vulnerabilitySumz_Gender:recreationalPreference_z
|
0.95
|
-0.59
|
-0.05
|
|
b_grandiositySumz_Gender
|
0.95
|
0.03
|
0.51
|
|
b_grandiositySumz_recreationalPreference_z
|
0.95
|
0.11
|
1.02
|
|
b_grandiositySumz_Age
|
0.95
|
-0.03
|
0.00
|
|
b_grandiositySumz_Gender:recreationalPreference_z
|
0.95
|
-0.66
|
-0.12
|
corr_pni <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z, dominance_Sum, leadership_Sum, prestige_Sum) ~ 1,
data = Experiment_2_demographics_Gender, family = student(), cores = parallel::detectCores(),
prior = c(
prior(gamma(2, 0.1), class = "nu"),
prior(normal(0, 1), class = "Intercept"),
prior(normal(0, 1), class = "sigma", resp = "vulnerabilitySumz"),
prior(normal(0, 1), class = "sigma", resp = "grandiositySumz"),
prior(normal(0.05, 0.23), class = "sigma", resp = "dominanceSum"),
prior(normal(0.04, 0.58), class = "sigma", resp = "leadershipSum"),
prior(normal(0.12, 0.30), class = "sigma", resp = "prestigeSum")
), iter = 5000, warmup = 500, backend = "cmdstanr"
)
summary(corr_pni)
## Family: MV(student, student, student, student, student)
## Links: mu = identity; sigma = identity; nu = identity
## mu = identity; sigma = identity; nu = identity
## mu = identity; sigma = identity; nu = identity
## mu = identity; sigma = identity; nu = identity
## mu = identity; sigma = identity; nu = identity
## Formula: vulnerability_Sum_z ~ 1
## grandiosity_Sum_z ~ 1
## dominance_Sum ~ 1
## leadership_Sum ~ 1
## prestige_Sum ~ 1
## Data: Experiment_2_demographics_Gender (Number of observations: 279)
## Draws: 4 chains, each with iter = 5000; warmup = 500; thin = 1;
## total post-warmup draws = 18000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## vulnerabilitySumz_Intercept 0.01 0.06 -0.11 0.13 1.00 15855
## grandiositySumz_Intercept 0.01 0.06 -0.11 0.12 1.00 12439
## dominanceSum_Intercept 0.02 0.06 -0.10 0.14 1.00 17181
## leadershipSum_Intercept -0.00 0.06 -0.12 0.11 1.00 15468
## prestigeSum_Intercept -0.00 0.06 -0.12 0.11 1.00 15218
## Tail_ESS
## vulnerabilitySumz_Intercept 15556
## grandiositySumz_Intercept 14221
## dominanceSum_Intercept 14185
## leadershipSum_Intercept 14322
## prestigeSum_Intercept 14040
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.97 0.04 0.89 1.06 1.00 17450
## sigma_grandiositySumz 0.96 0.04 0.88 1.04 1.00 14411
## sigma_dominanceSum 0.95 0.04 0.88 1.04 1.00 19686
## sigma_leadershipSum 0.96 0.04 0.88 1.05 1.00 16469
## sigma_prestigeSum 0.95 0.04 0.87 1.04 1.00 17704
## nu 27.91 10.78 13.74 55.42 1.00 18274
## nu_vulnerabilitySumz 1.00 0.00 1.00 1.00 NA NA
## nu_grandiositySumz 1.00 0.00 1.00 1.00 NA NA
## nu_dominanceSum 1.00 0.00 1.00 1.00 NA NA
## nu_leadershipSum 1.00 0.00 1.00 1.00 NA NA
## nu_prestigeSum 1.00 0.00 1.00 1.00 NA NA
## Tail_ESS
## sigma_vulnerabilitySumz 14011
## sigma_grandiositySumz 14775
## sigma_dominanceSum 14654
## sigma_leadershipSum 13663
## sigma_prestigeSum 14889
## nu 14594
## nu_vulnerabilitySumz NA
## nu_grandiositySumz NA
## nu_dominanceSum NA
## nu_leadershipSum NA
## nu_prestigeSum NA
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.52 0.04 0.43 0.60
## rescor(vulnerabilitySumz,dominanceSum) 0.43 0.05 0.33 0.52
## rescor(grandiositySumz,dominanceSum) 0.35 0.05 0.25 0.45
## rescor(vulnerabilitySumz,leadershipSum) 0.12 0.06 -0.00 0.23
## rescor(grandiositySumz,leadershipSum) 0.47 0.05 0.38 0.56
## rescor(dominanceSum,leadershipSum) 0.29 0.05 0.18 0.39
## rescor(vulnerabilitySumz,prestigeSum) 0.31 0.05 0.20 0.41
## rescor(grandiositySumz,prestigeSum) 0.51 0.05 0.42 0.59
## rescor(dominanceSum,prestigeSum) 0.28 0.05 0.17 0.38
## rescor(leadershipSum,prestigeSum) 0.46 0.05 0.36 0.55
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 12767 14291
## rescor(vulnerabilitySumz,dominanceSum) 1.00 20541 14030
## rescor(grandiositySumz,dominanceSum) 1.00 20024 14438
## rescor(vulnerabilitySumz,leadershipSum) 1.00 15448 14288
## rescor(grandiositySumz,leadershipSum) 1.00 18555 14544
## rescor(dominanceSum,leadershipSum) 1.00 19590 14192
## rescor(vulnerabilitySumz,prestigeSum) 1.00 14083 12742
## rescor(grandiositySumz,prestigeSum) 1.00 19032 13671
## rescor(dominanceSum,prestigeSum) 1.00 20220 14413
## rescor(leadershipSum,prestigeSum) 1.00 18964 13670
##
## 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).
bmod_1 <- "Narcissism =~ ethicalQuestionsBenefitSum + financialQuestionsBenefitSum + socialQuestionsBenefitSum + healthAndSafetyQuestionsBenefitSum + recreationalQuestionsBenefitSum
Narcissism_int =~ ethicalQuestionsBenefitSum*Gender + financialQuestionsBenefitSum*Gender + socialQuestionsBenefitSum*Gender + healthAndSafetyQuestionsBenefitSum*Gender + recreationalQuestionsBenefitSum*Gender
Narcissism ~ Narcissism_int"
bmod_1_fit <- bsem(bmod_1,
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(bmod_1_fit)
## blavaan (0.4-3) 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
## Narcissism =~
## ethcQBS 1.000
## fnncQBS 0.642 0.119 0.432 0.892 1.000
## sclQsBS 0.562 0.125 0.337 0.821 1.001
## hlASQBS 1.329 0.160 1.052 1.690 1.001
## rcrtQBS 0.982 0.140 0.740 1.284 1.001
## Narcissism_int =~
## Gender (eQBS) 1.000
## Prior
##
##
## normal(0,10)
## normal(0,10)
## normal(0,10)
## normal(0,10)
##
##
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## Narcissism ~
## Narcissism_int 0.182 0.090 0.011 0.362 1.000 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .ethclQstnsBnfS -0.020 0.058 -0.139 0.094 0.999 normal(0,32)
## .fnnclQstnsBnfS -0.004 0.059 -0.121 0.117 1.000 normal(0,32)
## .sclQstnsBnftSm -0.037 0.059 -0.154 0.078 1.000 normal(0,32)
## .hlthAndSftyQBS -0.020 0.060 -0.136 0.099 0.999 normal(0,32)
## .rcrtnlQstnsBnS -0.014 0.061 -0.132 0.104 1.000 normal(0,32)
## .Gender 1.556 0.029 1.500 1.612 1.000 normal(0,32)
## .Narcissism 0.000
## Narcissism_int 0.000
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Rhat Prior
## .ethclQstnsBnfS 0.583 0.065 0.465 0.720 1.000 gamma(1,.5)[sd]
## .fnnclQstnsBnfS 0.821 0.076 0.685 0.981 0.999 gamma(1,.5)[sd]
## .sclQstnsBnftSm 0.857 0.077 0.722 1.024 1.001 gamma(1,.5)[sd]
## .hlthAndSftyQBS 0.323 0.071 0.182 0.463 1.000 gamma(1,.5)[sd]
## .rcrtnlQstnsBnS 0.633 0.069 0.508 0.779 1.000 gamma(1,.5)[sd]
## .Gender 0.000
## .Narcissism 0.387 0.078 0.244 0.550 1.001 gamma(1,.5)[sd]
## Narcissism_int 0.250 0.022 0.212 0.295 1.000 gamma(1,.5)[sd]
graph_sem(bmod_1_fit)
pni_model_dopl <- brm(PNI_Sum_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = prior_pni_model_dopl
)
pni_model_dopl_fix <- fixef(pni_model_dopl)
saveRDS(pni_model_dopl_fix, "pni_model_dopl_fix.rds")
summary(pni_model_dopl)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: PNI_Sum_z ~ dominance_Sum + prestige_Sum + leadership_Sum + 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
## Intercept 0.69 0.16 0.38 1.00 1.00 52918 28896
## dominance_Sum 0.33 0.05 0.23 0.43 1.00 45848 30055
## prestige_Sum 0.29 0.06 0.18 0.40 1.00 42260 29201
## leadership_Sum 0.05 0.06 -0.06 0.16 1.00 43331 29938
## Gender2 -0.07 0.10 -0.26 0.13 1.00 47543 28092
## Age -0.02 0.00 -0.03 -0.01 1.00 51424 29968
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.80 0.03 0.73 0.87 1.00 48116 28125
##
## 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).
pni_model_dopl_hdi <- bayestestR::hdi(pni_model_dopl, effects = "fixed", component = "conditional", ci = .95)
kable(pni_model_dopl_hdi[
sign(pni_model_dopl_hdi$CI_low) == sign(pni_model_dopl_hdi$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_Intercept
|
0.95
|
0.38
|
1.01
|
|
b_dominance_Sum
|
0.95
|
0.23
|
0.43
|
|
b_prestige_Sum
|
0.95
|
0.18
|
0.40
|
|
b_Age
|
0.95
|
-0.03
|
-0.01
|
pni_model_dopl_int <- brm(PNI_Sum_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = prior_pni_model_dopl_int
)
summary(pni_model_dopl_int)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: PNI_Sum_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 = 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
## Intercept 0.81 0.21 0.40 1.22 1.00 48500
## dominance_Sum 0.51 0.18 0.17 0.86 1.00 25335
## Gender -0.10 0.10 -0.30 0.09 1.00 45121
## prestige_Sum 0.42 0.18 0.07 0.78 1.00 22489
## leadership_Sum 0.14 0.19 -0.22 0.51 1.00 22079
## Age -0.02 0.01 -0.03 -0.01 1.00 54284
## dominance_Sum:Gender -0.11 0.11 -0.32 0.09 1.00 25474
## Gender:prestige_Sum -0.10 0.11 -0.31 0.12 1.00 22975
## Gender:leadership_Sum -0.07 0.11 -0.28 0.15 1.00 22280
## Tail_ESS
## Intercept 26897
## dominance_Sum 23881
## Gender 24659
## prestige_Sum 24509
## leadership_Sum 23250
## Age 26951
## dominance_Sum:Gender 24722
## Gender:prestige_Sum 24657
## Gender:leadership_Sum 23346
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.79 0.03 0.73 0.87 1.00 44543 25804
##
## 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).
pni_model_dopl_int_hdi <- bayestestR::hdi(pni_model_dopl_int, effects = "fixed", component = "conditional", ci = .95)
kable(pni_model_dopl_int_hdi[
sign(pni_model_dopl_int_hdi$CI_low) == sign(pni_model_dopl_int_hdi$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_Intercept
|
0.95
|
0.39
|
1.21
|
|
b_dominance_Sum
|
0.95
|
0.17
|
0.86
|
|
b_prestige_Sum
|
0.95
|
0.07
|
0.77
|
|
b_Age
|
0.95
|
-0.03
|
-0.01
|
pni_multi_dopl <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z) ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = prior_pni_multi_dopl
)
summary(pni_multi_dopl)
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: vulnerability_Sum_z ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
## grandiosity_Sum_z ~ dominance_Sum + prestige_Sum + leadership_Sum + 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
## vulnerabilitySumz_Intercept 0.80 0.17 0.47 1.13 1.00
## grandiositySumz_Intercept 0.33 0.16 0.03 0.64 1.00
## vulnerabilitySumz_dominance_Sum 0.39 0.06 0.28 0.50 1.00
## vulnerabilitySumz_prestige_Sum 0.20 0.06 0.09 0.32 1.00
## vulnerabilitySumz_leadership_Sum -0.11 0.06 -0.22 0.01 1.00
## vulnerabilitySumz_Gender2 -0.24 0.11 -0.45 -0.03 1.00
## vulnerabilitySumz_Age -0.02 0.01 -0.03 -0.01 1.00
## grandiositySumz_dominance_Sum 0.14 0.05 0.03 0.24 1.00
## grandiositySumz_prestige_Sum 0.34 0.05 0.23 0.44 1.00
## grandiositySumz_leadership_Sum 0.28 0.05 0.18 0.39 1.00
## grandiositySumz_Gender2 0.16 0.10 -0.03 0.34 1.00
## grandiositySumz_Age -0.01 0.00 -0.02 -0.00 1.00
## Bulk_ESS Tail_ESS
## vulnerabilitySumz_Intercept 45605 29189
## grandiositySumz_Intercept 49625 29578
## vulnerabilitySumz_dominance_Sum 32744 28936
## vulnerabilitySumz_prestige_Sum 31809 28015
## vulnerabilitySumz_leadership_Sum 31141 29291
## vulnerabilitySumz_Gender2 33380 29620
## vulnerabilitySumz_Age 42763 29164
## grandiositySumz_dominance_Sum 31588 29194
## grandiositySumz_prestige_Sum 33721 28944
## grandiositySumz_leadership_Sum 31305 29025
## grandiositySumz_Gender2 33666 29582
## grandiositySumz_Age 47156 28885
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.85 0.04 0.78 0.92 1.00 41257
## sigma_grandiositySumz 0.78 0.03 0.71 0.85 1.00 43548
## Tail_ESS
## sigma_vulnerabilitySumz 29481
## sigma_grandiositySumz 29199
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.42 0.05 0.32 0.51
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 37701 29118
##
## 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).
pni_multi_dopl_hdi <- bayestestR::hdi(pni_multi_dopl, effects = "fixed", component = "conditional", ci = .95)
kable(pni_multi_dopl_hdi[
sign(pni_multi_dopl_hdi$CI_low) == sign(pni_multi_dopl_hdi$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_vulnerabilitySumz_Intercept
|
0.95
|
0.47
|
1.13
|
|
b_vulnerabilitySumz_dominance_Sum
|
0.95
|
0.28
|
0.50
|
|
b_vulnerabilitySumz_prestige_Sum
|
0.95
|
0.08
|
0.32
|
|
b_vulnerabilitySumz_Gender2
|
0.95
|
-0.44
|
-0.03
|
|
b_vulnerabilitySumz_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_grandiositySumz_Intercept
|
0.95
|
0.03
|
0.64
|
|
b_grandiositySumz_dominance_Sum
|
0.95
|
0.04
|
0.24
|
|
b_grandiositySumz_prestige_Sum
|
0.95
|
0.23
|
0.45
|
|
b_grandiositySumz_leadership_Sum
|
0.95
|
0.17
|
0.39
|
|
b_grandiositySumz_Age
|
0.95
|
-0.02
|
0.00
|
pni_multi_dopl_fix <- fixef(pni_multi_dopl)
saveRDS(pni_multi_dopl_fix, "pni_multi_dopl_fix.rds")
pni_multi_dopl_int <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z) ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = prior_pni_multi_dopl_int
)
summary(pni_multi_dopl_int)
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: vulnerability_Sum_z ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age
## grandiosity_Sum_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 = 10000; warmup = 1000; thin = 1;
## total post-warmup draws = 36000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI
## vulnerabilitySumz_Intercept 1.01 0.22 0.57 1.45
## grandiositySumz_Intercept 0.24 0.20 -0.16 0.64
## vulnerabilitySumz_dominance_Sum 0.44 0.19 0.08 0.80
## vulnerabilitySumz_Gender -0.23 0.11 -0.44 -0.02
## vulnerabilitySumz_prestige_Sum 0.40 0.19 0.02 0.77
## vulnerabilitySumz_leadership_Sum -0.01 0.20 -0.40 0.37
## vulnerabilitySumz_Age -0.02 0.01 -0.03 -0.01
## vulnerabilitySumz_dominance_Sum:Gender -0.03 0.11 -0.25 0.19
## vulnerabilitySumz_Gender:prestige_Sum -0.13 0.12 -0.36 0.10
## vulnerabilitySumz_Gender:leadership_Sum -0.06 0.12 -0.30 0.17
## grandiositySumz_dominance_Sum 0.45 0.17 0.12 0.78
## grandiositySumz_Gender 0.13 0.10 -0.06 0.31
## grandiositySumz_prestige_Sum 0.33 0.18 -0.02 0.67
## grandiositySumz_leadership_Sum 0.36 0.18 0.00 0.71
## grandiositySumz_Age -0.01 0.00 -0.02 -0.00
## grandiositySumz_dominance_Sum:Gender -0.20 0.10 -0.40 0.00
## grandiositySumz_Gender:prestige_Sum -0.01 0.11 -0.22 0.20
## grandiositySumz_Gender:leadership_Sum -0.05 0.11 -0.26 0.16
## Rhat Bulk_ESS Tail_ESS
## vulnerabilitySumz_Intercept 1.00 45771 27738
## grandiositySumz_Intercept 1.00 44551 28638
## vulnerabilitySumz_dominance_Sum 1.00 17106 21611
## vulnerabilitySumz_Gender 1.00 38006 27435
## vulnerabilitySumz_prestige_Sum 1.00 15502 22670
## vulnerabilitySumz_leadership_Sum 1.00 16019 22583
## vulnerabilitySumz_Age 1.00 51304 29164
## vulnerabilitySumz_dominance_Sum:Gender 1.00 17341 22239
## vulnerabilitySumz_Gender:prestige_Sum 1.00 15716 22553
## vulnerabilitySumz_Gender:leadership_Sum 1.00 16249 22445
## grandiositySumz_dominance_Sum 1.00 17513 22547
## grandiositySumz_Gender 1.00 36697 28898
## grandiositySumz_prestige_Sum 1.00 14496 21410
## grandiositySumz_leadership_Sum 1.00 15228 22718
## grandiositySumz_Age 1.00 50195 29773
## grandiositySumz_dominance_Sum:Gender 1.00 17814 22905
## grandiositySumz_Gender:prestige_Sum 1.00 14580 21364
## grandiositySumz_Gender:leadership_Sum 1.00 15317 22640
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.85 0.04 0.78 0.92 1.00 39127
## sigma_grandiositySumz 0.77 0.03 0.71 0.84 1.00 38682
## Tail_ESS
## sigma_vulnerabilitySumz 27164
## sigma_grandiositySumz 26832
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.42 0.05 0.31 0.51
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 36052 27391
##
## 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).
pni_multi_dopl_int_hdi <- bayestestR::hdi(pni_multi_dopl_int, effects = "fixed", component = "conditional", ci = .95)
kable(pni_multi_dopl_int_hdi[
sign(pni_multi_dopl_int_hdi$CI_low) == sign(pni_multi_dopl_int_hdi$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_vulnerabilitySumz_Intercept
|
0.95
|
0.56
|
1.44
|
|
b_vulnerabilitySumz_dominance_Sum
|
0.95
|
0.07
|
0.80
|
|
b_vulnerabilitySumz_Gender
|
0.95
|
-0.44
|
-0.02
|
|
b_vulnerabilitySumz_prestige_Sum
|
0.95
|
0.01
|
0.77
|
|
b_vulnerabilitySumz_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_grandiositySumz_dominance_Sum
|
0.95
|
0.11
|
0.78
|
|
b_grandiositySumz_leadership_Sum
|
0.95
|
0.01
|
0.72
|
|
b_grandiositySumz_Age
|
0.95
|
-0.02
|
0.00
|
pni_model_dospert <- brm(PNI_Sum_z ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = pni_dospert_prior
)
summary(pni_model_dospert)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: PNI_Sum_z ~ 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
## Intercept 0.71 0.25 0.21 1.20 1.00 57668
## ethicalPreference_z 0.13 0.08 -0.02 0.27 1.00 33254
## financialPreference_z -0.07 0.06 -0.20 0.05 1.00 46053
## socialPreference_z 0.26 0.07 0.12 0.39 1.00 41702
## healthAndSafetyPreference_z -0.00 0.08 -0.15 0.15 1.00 32941
## recreationalPreference_z -0.07 0.07 -0.21 0.07 1.00 37323
## Gender 0.11 0.12 -0.13 0.35 1.00 43048
## Age -0.03 0.01 -0.04 -0.02 1.00 59491
## Tail_ESS
## Intercept 29091
## ethicalPreference_z 28666
## financialPreference_z 28255
## socialPreference_z 29096
## healthAndSafetyPreference_z 28148
## recreationalPreference_z 28833
## Gender 29373
## Age 28576
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.93 0.04 0.85 1.01 1.00 55388 27771
##
## 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).
pni_model_dospert_hdi <- bayestestR::hdi(pni_model_dospert, effects = "fixed", component = "conditional", ci = .95)
kable(pni_model_dospert_hdi[
sign(pni_model_dospert_hdi$CI_low) == sign(pni_model_dospert_hdi$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_Intercept
|
0.95
|
0.20
|
1.20
|
|
b_socialPreference_z
|
0.95
|
0.12
|
0.39
|
|
b_Age
|
0.95
|
-0.04
|
-0.02
|
pni_model_dospert_int <- brm(PNI_Sum_z ~ 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, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = pni_dospert_prior_int
)
summary(pni_model_dospert_int)
## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: PNI_Sum_z ~ 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
## Intercept 0.64 0.26 0.14 1.14 1.00
## ethicalPreference_z -0.19 0.25 -0.69 0.31 1.00
## Gender 0.10 0.12 -0.14 0.34 1.00
## financialPreference_z -0.43 0.22 -0.85 -0.00 1.00
## socialPreference_z 0.17 0.21 -0.23 0.57 1.00
## healthAndSafetyPreference_z 0.10 0.24 -0.37 0.58 1.00
## recreationalPreference_z 0.59 0.23 0.13 1.05 1.00
## Age -0.03 0.01 -0.04 -0.01 1.00
## ethicalPreference_z:Gender 0.19 0.15 -0.10 0.48 1.00
## Gender:financialPreference_z 0.22 0.13 -0.03 0.47 1.00
## Gender:socialPreference_z 0.05 0.13 -0.20 0.30 1.00
## Gender:healthAndSafetyPreference_z -0.06 0.15 -0.35 0.22 1.00
## Gender:recreationalPreference_z -0.41 0.14 -0.67 -0.14 1.00
## Bulk_ESS Tail_ESS
## Intercept 45498 25907
## ethicalPreference_z 16575 22221
## Gender 40147 28174
## financialPreference_z 18830 22922
## socialPreference_z 18211 21921
## healthAndSafetyPreference_z 15169 20631
## recreationalPreference_z 18275 23080
## Age 47065 24826
## ethicalPreference_z:Gender 16541 22601
## Gender:financialPreference_z 18897 22611
## Gender:socialPreference_z 19280 23094
## Gender:healthAndSafetyPreference_z 15425 18705
## Gender:recreationalPreference_z 18368 23204
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.91 0.04 0.83 0.99 1.00 40602 26356
##
## 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).
pni_model_dospert_int_hdi <- bayestestR::hdi(pni_model_dospert_int, effects = "fixed", component = "conditional", ci = .95)
kable(pni_model_dospert_int_hdi[
sign(pni_model_dospert_int_hdi$CI_low) == sign(pni_model_dospert_int_hdi$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_Intercept
|
0.95
|
0.15
|
1.15
|
|
b_financialPreference_z
|
0.95
|
-0.85
|
0.00
|
|
b_recreationalPreference_z
|
0.95
|
0.13
|
1.05
|
|
b_Age
|
0.95
|
-0.04
|
-0.01
|
|
b_Gender:recreationalPreference_z
|
0.95
|
-0.69
|
-0.15
|
pni_multi_dospert <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z) ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = prior_multi_dospert_prior
)
summary(pni_multi_dospert)
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: vulnerability_Sum_z ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
## grandiosity_Sum_z ~ 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
## vulnerabilitySumz_Intercept 0.95 0.25 0.45
## grandiositySumz_Intercept 0.12 0.25 -0.37
## vulnerabilitySumz_ethicalPreference_z 0.16 0.08 0.01
## vulnerabilitySumz_financialPreference_z -0.12 0.06 -0.24
## vulnerabilitySumz_socialPreference_z 0.14 0.07 0.01
## vulnerabilitySumz_healthAndSafetyPreference_z 0.05 0.08 -0.10
## vulnerabilitySumz_recreationalPreference_z -0.06 0.07 -0.20
## vulnerabilitySumz_Gender -0.04 0.12 -0.28
## vulnerabilitySumz_Age -0.03 0.01 -0.04
## grandiositySumz_ethicalPreference_z 0.04 0.08 -0.11
## grandiositySumz_financialPreference_z 0.02 0.06 -0.11
## grandiositySumz_socialPreference_z 0.35 0.07 0.22
## grandiositySumz_healthAndSafetyPreference_z -0.08 0.08 -0.23
## grandiositySumz_recreationalPreference_z -0.06 0.07 -0.20
## grandiositySumz_Gender 0.30 0.12 0.06
## grandiositySumz_Age -0.02 0.01 -0.03
## u-95% CI Rhat Bulk_ESS Tail_ESS
## vulnerabilitySumz_Intercept 1.44 1.00 43818 30868
## grandiositySumz_Intercept 0.61 1.00 45279 31150
## vulnerabilitySumz_ethicalPreference_z 0.31 1.00 32320 28567
## vulnerabilitySumz_financialPreference_z 0.01 1.00 38469 28727
## vulnerabilitySumz_socialPreference_z 0.28 1.00 37844 28552
## vulnerabilitySumz_healthAndSafetyPreference_z 0.20 1.00 32528 28670
## vulnerabilitySumz_recreationalPreference_z 0.08 1.00 34719 27988
## vulnerabilitySumz_Gender 0.20 1.00 38947 29009
## vulnerabilitySumz_Age -0.02 1.00 45283 30136
## grandiositySumz_ethicalPreference_z 0.19 1.00 30783 27645
## grandiositySumz_financialPreference_z 0.14 1.00 38597 28630
## grandiositySumz_socialPreference_z 0.49 1.00 35848 28389
## grandiositySumz_healthAndSafetyPreference_z 0.07 1.00 30611 28390
## grandiositySumz_recreationalPreference_z 0.08 1.00 35672 27044
## grandiositySumz_Gender 0.53 1.00 39540 29448
## grandiositySumz_Age -0.01 1.00 45592 29843
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.94 0.04 0.86 1.02 1.00 40419
## sigma_grandiositySumz 0.93 0.04 0.86 1.01 1.00 38788
## Tail_ESS
## sigma_vulnerabilitySumz 28159
## sigma_grandiositySumz 25879
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.49 0.05 0.39 0.57
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 39548 29780
##
## 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).
pni_multi_dospert_hdi <- bayestestR::hdi(pni_multi_dospert, effects = "fixed", component = "conditional", ci = .95)
kable(pni_multi_dospert_hdi[
sign(pni_multi_dospert_hdi$CI_low) == sign(pni_multi_dospert_hdi$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_vulnerabilitySumz_Intercept
|
0.95
|
0.45
|
1.44
|
|
b_vulnerabilitySumz_ethicalPreference_z
|
0.95
|
0.01
|
0.31
|
|
b_vulnerabilitySumz_socialPreference_z
|
0.95
|
0.01
|
0.27
|
|
b_vulnerabilitySumz_Age
|
0.95
|
-0.04
|
-0.02
|
|
b_grandiositySumz_socialPreference_z
|
0.95
|
0.22
|
0.48
|
|
b_grandiositySumz_Gender
|
0.95
|
0.06
|
0.54
|
|
b_grandiositySumz_Age
|
0.95
|
-0.03
|
-0.01
|
pni_multi_dospert_int <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z) ~ 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, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = prior_multi_dospert_int
)
summary(pni_multi_dospert_int)
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: vulnerability_Sum_z ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## grandiosity_Sum_z ~ 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
## vulnerabilitySumz_Intercept 0.82 0.20
## grandiositySumz_Intercept 0.38 0.20
## vulnerabilitySumz_ethicalPreference_z 0.01 0.13
## vulnerabilitySumz_Gender2 -0.04 0.12
## vulnerabilitySumz_financialPreference_z -0.27 0.10
## vulnerabilitySumz_socialPreference_z 0.12 0.10
## vulnerabilitySumz_healthAndSafetyPreference_z 0.12 0.12
## vulnerabilitySumz_recreationalPreference_z 0.15 0.11
## vulnerabilitySumz_Age -0.03 0.01
## vulnerabilitySumz_ethicalPreference_z:Gender2 0.22 0.15
## vulnerabilitySumz_Gender2:financialPreference_z 0.25 0.13
## vulnerabilitySumz_Gender2:socialPreference_z 0.03 0.13
## vulnerabilitySumz_Gender2:healthAndSafetyPreference_z -0.11 0.15
## vulnerabilitySumz_Gender2:recreationalPreference_z -0.34 0.14
## grandiositySumz_ethicalPreference_z -0.02 0.13
## grandiositySumz_Gender2 0.27 0.12
## grandiositySumz_financialPreference_z -0.05 0.11
## grandiositySumz_socialPreference_z 0.30 0.10
## grandiositySumz_healthAndSafetyPreference_z -0.10 0.12
## grandiositySumz_recreationalPreference_z 0.20 0.11
## grandiositySumz_Age -0.02 0.01
## grandiositySumz_ethicalPreference_z:Gender2 0.08 0.15
## grandiositySumz_Gender2:financialPreference_z 0.13 0.13
## grandiositySumz_Gender2:socialPreference_z 0.06 0.13
## grandiositySumz_Gender2:healthAndSafetyPreference_z 0.04 0.15
## grandiositySumz_Gender2:recreationalPreference_z -0.41 0.14
## l-95% CI u-95% CI Rhat
## vulnerabilitySumz_Intercept 0.44 1.21 1.00
## grandiositySumz_Intercept -0.01 0.77 1.00
## vulnerabilitySumz_ethicalPreference_z -0.24 0.26 1.00
## vulnerabilitySumz_Gender2 -0.28 0.21 1.00
## vulnerabilitySumz_financialPreference_z -0.47 -0.06 1.00
## vulnerabilitySumz_socialPreference_z -0.07 0.31 1.00
## vulnerabilitySumz_healthAndSafetyPreference_z -0.11 0.36 1.00
## vulnerabilitySumz_recreationalPreference_z -0.07 0.38 1.00
## vulnerabilitySumz_Age -0.04 -0.02 1.00
## vulnerabilitySumz_ethicalPreference_z:Gender2 -0.08 0.53 1.00
## vulnerabilitySumz_Gender2:financialPreference_z -0.01 0.51 1.00
## vulnerabilitySumz_Gender2:socialPreference_z -0.23 0.29 1.00
## vulnerabilitySumz_Gender2:healthAndSafetyPreference_z -0.41 0.20 1.00
## vulnerabilitySumz_Gender2:recreationalPreference_z -0.62 -0.07 1.00
## grandiositySumz_ethicalPreference_z -0.27 0.23 1.00
## grandiositySumz_Gender2 0.03 0.51 1.00
## grandiositySumz_financialPreference_z -0.26 0.15 1.00
## grandiositySumz_socialPreference_z 0.11 0.49 1.00
## grandiositySumz_healthAndSafetyPreference_z -0.33 0.14 1.00
## grandiositySumz_recreationalPreference_z -0.03 0.42 1.00
## grandiositySumz_Age -0.03 -0.00 1.00
## grandiositySumz_ethicalPreference_z:Gender2 -0.22 0.39 1.00
## grandiositySumz_Gender2:financialPreference_z -0.13 0.38 1.00
## grandiositySumz_Gender2:socialPreference_z -0.20 0.32 1.00
## grandiositySumz_Gender2:healthAndSafetyPreference_z -0.26 0.33 1.00
## grandiositySumz_Gender2:recreationalPreference_z -0.69 -0.13 1.00
## Bulk_ESS Tail_ESS
## vulnerabilitySumz_Intercept 45837 30295
## grandiositySumz_Intercept 45047 28970
## vulnerabilitySumz_ethicalPreference_z 22591 26159
## vulnerabilitySumz_Gender2 41156 28666
## vulnerabilitySumz_financialPreference_z 25262 27816
## vulnerabilitySumz_socialPreference_z 25591 28083
## vulnerabilitySumz_healthAndSafetyPreference_z 22965 26441
## vulnerabilitySumz_recreationalPreference_z 24557 25870
## vulnerabilitySumz_Age 45971 29656
## vulnerabilitySumz_ethicalPreference_z:Gender2 23136 27545
## vulnerabilitySumz_Gender2:financialPreference_z 25747 27042
## vulnerabilitySumz_Gender2:socialPreference_z 27287 28272
## vulnerabilitySumz_Gender2:healthAndSafetyPreference_z 23352 26285
## vulnerabilitySumz_Gender2:recreationalPreference_z 25721 25444
## grandiositySumz_ethicalPreference_z 21888 25252
## grandiositySumz_Gender2 41817 29507
## grandiositySumz_financialPreference_z 27278 28105
## grandiositySumz_socialPreference_z 25661 25771
## grandiositySumz_healthAndSafetyPreference_z 21942 25310
## grandiositySumz_recreationalPreference_z 25081 26578
## grandiositySumz_Age 44220 29291
## grandiositySumz_ethicalPreference_z:Gender2 22115 25303
## grandiositySumz_Gender2:financialPreference_z 27917 28195
## grandiositySumz_Gender2:socialPreference_z 26579 28115
## grandiositySumz_Gender2:healthAndSafetyPreference_z 22593 26085
## grandiositySumz_Gender2:recreationalPreference_z 25429 26866
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.92 0.04 0.85 1.01 1.00 42091
## sigma_grandiositySumz 0.92 0.04 0.85 1.00 1.00 43368
## Tail_ESS
## sigma_vulnerabilitySumz 27557
## sigma_grandiositySumz 28027
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.47 0.05 0.37 0.55
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 40214 29073
##
## 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).
pni_multi_dospert_int_hdi <- bayestestR::hdi(pni_multi_dospert_int, effects = "fixed", component = "conditional", ci = .95)
kable(pni_multi_dospert_int_hdi[
sign(pni_multi_dospert_int_hdi$CI_low) == sign(pni_multi_dospert_int_hdi$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_vulnerabilitySumz_Intercept
|
0.95
|
0.43
|
1.21
|
|
b_vulnerabilitySumz_financialPreference_z
|
0.95
|
-0.47
|
-0.06
|
|
b_vulnerabilitySumz_Age
|
0.95
|
-0.04
|
-0.02
|
|
b_vulnerabilitySumz_Gender2:recreationalPreference_z
|
0.95
|
-0.62
|
-0.07
|
|
b_grandiositySumz_Gender2
|
0.95
|
0.03
|
0.51
|
|
b_grandiositySumz_socialPreference_z
|
0.95
|
0.11
|
0.49
|
|
b_grandiositySumz_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_grandiositySumz_Gender2:recreationalPreference_z
|
0.95
|
-0.68
|
-0.13
|
pni_multi_dospert_int_Age <- brm(mvbind(vulnerability_Sum_z, grandiosity_Sum_z) ~ ethicalPreference_z * Age + financialPreference_z * Age + socialPreference_z * Age + healthAndSafetyPreference_z * Age + recreationalPreference_z * Age + Gender,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = prior_multi_dospert_int_Age_prior
)
summary(pni_multi_dospert_int_Age)
## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: vulnerability_Sum_z ~ ethicalPreference_z * Age + financialPreference_z * Age + socialPreference_z * Age + healthAndSafetyPreference_z * Age + recreationalPreference_z * Age + Gender
## grandiosity_Sum_z ~ ethicalPreference_z * Age + financialPreference_z * Age + socialPreference_z * Age + healthAndSafetyPreference_z * Age + recreationalPreference_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
## vulnerabilitySumz_Intercept 0.86 0.27 0.34
## grandiositySumz_Intercept 0.07 0.26 -0.45
## vulnerabilitySumz_ethicalPreference_z -0.05 0.25 -0.54
## vulnerabilitySumz_Age -0.03 0.01 -0.04
## vulnerabilitySumz_financialPreference_z -0.12 0.21 -0.53
## vulnerabilitySumz_socialPreference_z 0.21 0.21 -0.20
## vulnerabilitySumz_healthAndSafetyPreference_z -0.09 0.27 -0.62
## vulnerabilitySumz_recreationalPreference_z -0.00 0.22 -0.43
## vulnerabilitySumz_Gender -0.02 0.13 -0.27
## vulnerabilitySumz_ethicalPreference_z:Age 0.01 0.01 -0.01
## vulnerabilitySumz_Age:financialPreference_z 0.00 0.01 -0.01
## vulnerabilitySumz_Age:socialPreference_z -0.00 0.01 -0.02
## vulnerabilitySumz_Age:healthAndSafetyPreference_z 0.01 0.01 -0.01
## vulnerabilitySumz_Age:recreationalPreference_z -0.00 0.01 -0.02
## grandiositySumz_ethicalPreference_z 0.01 0.25 -0.48
## grandiositySumz_Age -0.02 0.01 -0.03
## grandiositySumz_financialPreference_z -0.14 0.21 -0.54
## grandiositySumz_socialPreference_z 0.15 0.21 -0.26
## grandiositySumz_healthAndSafetyPreference_z -0.24 0.27 -0.76
## grandiositySumz_recreationalPreference_z 0.37 0.21 -0.05
## grandiositySumz_Gender 0.30 0.12 0.06
## grandiositySumz_ethicalPreference_z:Age 0.00 0.01 -0.02
## grandiositySumz_Age:financialPreference_z 0.01 0.01 -0.01
## grandiositySumz_Age:socialPreference_z 0.01 0.01 -0.01
## grandiositySumz_Age:healthAndSafetyPreference_z 0.01 0.01 -0.01
## grandiositySumz_Age:recreationalPreference_z -0.01 0.01 -0.03
## u-95% CI Rhat Bulk_ESS
## vulnerabilitySumz_Intercept 1.38 1.00 50294
## grandiositySumz_Intercept 0.58 1.00 50681
## vulnerabilitySumz_ethicalPreference_z 0.44 1.00 18284
## vulnerabilitySumz_Age -0.01 1.00 42989
## vulnerabilitySumz_financialPreference_z 0.29 1.00 23824
## vulnerabilitySumz_socialPreference_z 0.62 1.00 24010
## vulnerabilitySumz_healthAndSafetyPreference_z 0.44 1.00 17958
## vulnerabilitySumz_recreationalPreference_z 0.42 1.00 23317
## vulnerabilitySumz_Gender 0.23 1.00 46207
## vulnerabilitySumz_ethicalPreference_z:Age 0.02 1.00 18033
## vulnerabilitySumz_Age:financialPreference_z 0.01 1.00 23566
## vulnerabilitySumz_Age:socialPreference_z 0.01 1.00 24130
## vulnerabilitySumz_Age:healthAndSafetyPreference_z 0.02 1.00 18151
## vulnerabilitySumz_Age:recreationalPreference_z 0.01 1.00 22758
## grandiositySumz_ethicalPreference_z 0.51 1.00 17745
## grandiositySumz_Age -0.00 1.00 42366
## grandiositySumz_financialPreference_z 0.27 1.00 22943
## grandiositySumz_socialPreference_z 0.55 1.00 24316
## grandiositySumz_healthAndSafetyPreference_z 0.28 1.00 18161
## grandiositySumz_recreationalPreference_z 0.79 1.00 22139
## grandiositySumz_Gender 0.55 1.00 44793
## grandiositySumz_ethicalPreference_z:Age 0.02 1.00 17624
## grandiositySumz_Age:financialPreference_z 0.02 1.00 22813
## grandiositySumz_Age:socialPreference_z 0.02 1.00 24034
## grandiositySumz_Age:healthAndSafetyPreference_z 0.02 1.00 18280
## grandiositySumz_Age:recreationalPreference_z -0.00 1.00 21742
## Tail_ESS
## vulnerabilitySumz_Intercept 29029
## grandiositySumz_Intercept 28246
## vulnerabilitySumz_ethicalPreference_z 24269
## vulnerabilitySumz_Age 29683
## vulnerabilitySumz_financialPreference_z 25464
## vulnerabilitySumz_socialPreference_z 26468
## vulnerabilitySumz_healthAndSafetyPreference_z 23028
## vulnerabilitySumz_recreationalPreference_z 25130
## vulnerabilitySumz_Gender 29369
## vulnerabilitySumz_ethicalPreference_z:Age 24570
## vulnerabilitySumz_Age:financialPreference_z 26107
## vulnerabilitySumz_Age:socialPreference_z 26096
## vulnerabilitySumz_Age:healthAndSafetyPreference_z 22440
## vulnerabilitySumz_Age:recreationalPreference_z 25658
## grandiositySumz_ethicalPreference_z 24589
## grandiositySumz_Age 28951
## grandiositySumz_financialPreference_z 26242
## grandiositySumz_socialPreference_z 25970
## grandiositySumz_healthAndSafetyPreference_z 23432
## grandiositySumz_recreationalPreference_z 25543
## grandiositySumz_Gender 28958
## grandiositySumz_ethicalPreference_z:Age 24158
## grandiositySumz_Age:financialPreference_z 26969
## grandiositySumz_Age:socialPreference_z 25838
## grandiositySumz_Age:healthAndSafetyPreference_z 22280
## grandiositySumz_Age:recreationalPreference_z 25786
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_vulnerabilitySumz 0.94 0.04 0.87 1.03 1.00 51443
## sigma_grandiositySumz 0.93 0.04 0.85 1.01 1.00 50639
## Tail_ESS
## sigma_vulnerabilitySumz 28529
## sigma_grandiositySumz 28460
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(vulnerabilitySumz,grandiositySumz) 0.49 0.05 0.39 0.57
## Rhat Bulk_ESS Tail_ESS
## rescor(vulnerabilitySumz,grandiositySumz) 1.00 46807 27782
##
## 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).
pni_multi_dospert_int_Age_hdi <- bayestestR::hdi(pni_multi_dospert_int_Age, effects = "fixed", component = "conditional", ci = .95)
kable(pni_multi_dospert_int_Age_hdi[
sign(pni_multi_dospert_int_Age_hdi$CI_low) == sign(pni_multi_dospert_int_Age_hdi$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_vulnerabilitySumz_Intercept
|
0.95
|
0.35
|
1.39
|
|
b_vulnerabilitySumz_Age
|
0.95
|
-0.04
|
-0.01
|
|
b_grandiositySumz_Age
|
0.95
|
-0.03
|
0.00
|
|
b_grandiositySumz_Gender
|
0.95
|
0.05
|
0.54
|
|
b_grandiositySumz_Age:recreationalPreference_z
|
0.95
|
-0.03
|
0.00
|
multi_model_dospert <- brm(mvbind(grandiose_fantasy_Sum_z, exploitativeness_Sum_z, ssse_Sum_z) ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = multi_model_dospert_prior
)
multi_model_dospert_fix <- fixef(multi_model_dospert)
saveRDS(multi_model_dospert_fix, "multi_model_dospert_fix.rds")
summary(multi_model_dospert)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: grandiose_fantasy_Sum_z ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
## exploitativeness_Sum_z ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
## ssse_Sum_z ~ 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
## grandiosefantasySumz_Intercept 0.42 0.20 0.03
## exploitativenessSumz_Intercept 0.19 0.20 -0.20
## ssseSumz_Intercept 0.30 0.20 -0.08
## grandiosefantasySumz_ethicalPreference_z 0.04 0.08 -0.11
## grandiosefantasySumz_financialPreference_z 0.03 0.07 -0.10
## grandiosefantasySumz_socialPreference_z 0.28 0.07 0.14
## grandiosefantasySumz_healthAndSafetyPreference_z -0.18 0.08 -0.34
## grandiosefantasySumz_recreationalPreference_z -0.03 0.07 -0.18
## grandiosefantasySumz_Gender2 0.20 0.13 -0.05
## grandiosefantasySumz_Age -0.02 0.01 -0.03
## exploitativenessSumz_ethicalPreference_z -0.00 0.08 -0.16
## exploitativenessSumz_financialPreference_z 0.00 0.07 -0.12
## exploitativenessSumz_socialPreference_z 0.22 0.07 0.09
## exploitativenessSumz_healthAndSafetyPreference_z 0.10 0.08 -0.06
## exploitativenessSumz_recreationalPreference_z -0.02 0.07 -0.16
## exploitativenessSumz_Gender2 0.40 0.13 0.15
## exploitativenessSumz_Age -0.01 0.01 -0.03
## ssseSumz_ethicalPreference_z 0.04 0.08 -0.11
## ssseSumz_financialPreference_z -0.00 0.07 -0.13
## ssseSumz_socialPreference_z 0.28 0.07 0.14
## ssseSumz_healthAndSafetyPreference_z -0.07 0.08 -0.22
## ssseSumz_recreationalPreference_z -0.10 0.07 -0.24
## ssseSumz_Gender2 0.06 0.13 -0.19
## ssseSumz_Age -0.01 0.01 -0.02
## u-95% CI Rhat Bulk_ESS
## grandiosefantasySumz_Intercept 0.81 1.00 49619
## exploitativenessSumz_Intercept 0.58 1.00 50228
## ssseSumz_Intercept 0.69 1.00 47036
## grandiosefantasySumz_ethicalPreference_z 0.20 1.00 35819
## grandiosefantasySumz_financialPreference_z 0.16 1.00 39925
## grandiosefantasySumz_socialPreference_z 0.42 1.00 41186
## grandiosefantasySumz_healthAndSafetyPreference_z -0.03 1.00 36992
## grandiosefantasySumz_recreationalPreference_z 0.11 1.00 39273
## grandiosefantasySumz_Gender2 0.45 1.00 41109
## grandiosefantasySumz_Age -0.01 1.00 47845
## exploitativenessSumz_ethicalPreference_z 0.15 1.00 39559
## exploitativenessSumz_financialPreference_z 0.13 1.00 45560
## exploitativenessSumz_socialPreference_z 0.36 1.00 44584
## exploitativenessSumz_healthAndSafetyPreference_z 0.25 1.00 40800
## exploitativenessSumz_recreationalPreference_z 0.13 1.00 42302
## exploitativenessSumz_Gender2 0.65 1.00 44555
## exploitativenessSumz_Age -0.00 1.00 47767
## ssseSumz_ethicalPreference_z 0.20 1.00 35643
## ssseSumz_financialPreference_z 0.13 1.00 40079
## ssseSumz_socialPreference_z 0.42 1.00 39954
## ssseSumz_healthAndSafetyPreference_z 0.09 1.00 36715
## ssseSumz_recreationalPreference_z 0.05 1.00 38894
## ssseSumz_Gender2 0.30 1.00 41415
## ssseSumz_Age 0.00 1.00 45491
## Tail_ESS
## grandiosefantasySumz_Intercept 30933
## exploitativenessSumz_Intercept 29866
## ssseSumz_Intercept 30437
## grandiosefantasySumz_ethicalPreference_z 29653
## grandiosefantasySumz_financialPreference_z 29344
## grandiosefantasySumz_socialPreference_z 29677
## grandiosefantasySumz_healthAndSafetyPreference_z 28504
## grandiosefantasySumz_recreationalPreference_z 30223
## grandiosefantasySumz_Gender2 28231
## grandiosefantasySumz_Age 30280
## exploitativenessSumz_ethicalPreference_z 29142
## exploitativenessSumz_financialPreference_z 28481
## exploitativenessSumz_socialPreference_z 29875
## exploitativenessSumz_healthAndSafetyPreference_z 29886
## exploitativenessSumz_recreationalPreference_z 29964
## exploitativenessSumz_Gender2 29989
## exploitativenessSumz_Age 29877
## ssseSumz_ethicalPreference_z 29444
## ssseSumz_financialPreference_z 29513
## ssseSumz_socialPreference_z 29529
## ssseSumz_healthAndSafetyPreference_z 28636
## ssseSumz_recreationalPreference_z 30234
## ssseSumz_Gender2 27773
## ssseSumz_Age 29802
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_grandiosefantasySumz 0.96 0.04 0.88 1.04 1.00 44574
## sigma_exploitativenessSumz 0.96 0.04 0.88 1.04 1.00 50486
## sigma_ssseSumz 0.96 0.04 0.88 1.05 1.00 44574
## Tail_ESS
## sigma_grandiosefantasySumz 29637
## sigma_exploitativenessSumz 28745
## sigma_ssseSumz 28062
##
## Residual Correlations:
## Estimate Est.Error l-95% CI
## rescor(grandiosefantasySumz,exploitativenessSumz) 0.19 0.06 0.07
## rescor(grandiosefantasySumz,ssseSumz) 0.40 0.05 0.29
## rescor(exploitativenessSumz,ssseSumz) 0.22 0.06 0.11
## u-95% CI Rhat Bulk_ESS
## rescor(grandiosefantasySumz,exploitativenessSumz) 0.30 1.00 45882
## rescor(grandiosefantasySumz,ssseSumz) 0.49 1.00 42546
## rescor(exploitativenessSumz,ssseSumz) 0.33 1.00 48661
## Tail_ESS
## rescor(grandiosefantasySumz,exploitativenessSumz) 27506
## rescor(grandiosefantasySumz,ssseSumz) 28261
## rescor(exploitativenessSumz,ssseSumz) 27281
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
multi_model_dospert_hdi <- bayestestR::hdi(multi_model_dospert, effects = "fixed", component = "conditional", ci = .95)
kable(multi_model_dospert_hdi[
sign(multi_model_dospert_hdi$CI_low) == sign(multi_model_dospert_hdi$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_grandiosefantasySumz_Intercept
|
0.95
|
0.02
|
0.80
|
|
b_grandiosefantasySumz_socialPreference_z
|
0.95
|
0.15
|
0.42
|
|
b_grandiosefantasySumz_healthAndSafetyPreference_z
|
0.95
|
-0.34
|
-0.03
|
|
b_grandiosefantasySumz_Age
|
0.95
|
-0.03
|
-0.01
|
|
b_exploitativenessSumz_socialPreference_z
|
0.95
|
0.08
|
0.36
|
|
b_exploitativenessSumz_Gender2
|
0.95
|
0.15
|
0.64
|
|
b_exploitativenessSumz_Age
|
0.95
|
-0.03
|
0.00
|
|
b_ssseSumz_socialPreference_z
|
0.95
|
0.15
|
0.42
|
multi_model_dospert_int <- brm(mvbind(grandiose_fantasy_Sum_z, exploitativeness_Sum_z, ssse_Sum_z) ~ 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, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = multi_model_dospert_int_prior
)
summary(multi_model_dospert_int)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: grandiose_fantasy_Sum_z ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## exploitativeness_Sum_z ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## ssse_Sum_z ~ 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
## grandiosefantasySumz_Intercept 0.21 0.27
## exploitativenessSumz_Intercept -0.20 0.27
## ssseSumz_Intercept 0.24 0.27
## grandiosefantasySumz_ethicalPreference_z -0.19 0.26
## grandiosefantasySumz_Gender 0.19 0.13
## grandiosefantasySumz_financialPreference_z 0.04 0.23
## grandiosefantasySumz_socialPreference_z 0.17 0.22
## grandiosefantasySumz_healthAndSafetyPreference_z -0.18 0.25
## grandiosefantasySumz_recreationalPreference_z 0.35 0.24
## grandiosefantasySumz_Age -0.02 0.01
## grandiosefantasySumz_ethicalPreference_z:Gender 0.14 0.15
## grandiosefantasySumz_Gender:financialPreference_z -0.00 0.13
## grandiosefantasySumz_Gender:socialPreference_z 0.06 0.14
## grandiosefantasySumz_Gender:healthAndSafetyPreference_z 0.00 0.15
## grandiosefantasySumz_Gender:recreationalPreference_z -0.24 0.14
## exploitativenessSumz_ethicalPreference_z -0.16 0.27
## exploitativenessSumz_Gender 0.37 0.13
## exploitativenessSumz_financialPreference_z -0.13 0.22
## exploitativenessSumz_socialPreference_z 0.02 0.21
## exploitativenessSumz_healthAndSafetyPreference_z -0.01 0.25
## exploitativenessSumz_recreationalPreference_z 0.69 0.24
## exploitativenessSumz_Age -0.01 0.01
## exploitativenessSumz_ethicalPreference_z:Gender 0.10 0.15
## exploitativenessSumz_Gender:financialPreference_z 0.09 0.13
## exploitativenessSumz_Gender:socialPreference_z 0.12 0.13
## exploitativenessSumz_Gender:healthAndSafetyPreference_z 0.07 0.15
## exploitativenessSumz_Gender:recreationalPreference_z -0.43 0.14
## ssseSumz_ethicalPreference_z 0.13 0.27
## ssseSumz_Gender 0.04 0.13
## ssseSumz_financialPreference_z -0.31 0.23
## ssseSumz_socialPreference_z 0.36 0.21
## ssseSumz_healthAndSafetyPreference_z -0.02 0.25
## ssseSumz_recreationalPreference_z 0.22 0.24
## ssseSumz_Age -0.01 0.01
## ssseSumz_ethicalPreference_z:Gender -0.05 0.15
## ssseSumz_Gender:financialPreference_z 0.19 0.13
## ssseSumz_Gender:socialPreference_z -0.05 0.13
## ssseSumz_Gender:healthAndSafetyPreference_z -0.03 0.15
## ssseSumz_Gender:recreationalPreference_z -0.19 0.14
## l-95% CI u-95% CI Rhat
## grandiosefantasySumz_Intercept -0.32 0.74 1.00
## exploitativenessSumz_Intercept -0.73 0.32 1.00
## ssseSumz_Intercept -0.29 0.76 1.00
## grandiosefantasySumz_ethicalPreference_z -0.71 0.33 1.00
## grandiosefantasySumz_Gender -0.06 0.44 1.00
## grandiosefantasySumz_financialPreference_z -0.40 0.48 1.00
## grandiosefantasySumz_socialPreference_z -0.25 0.59 1.00
## grandiosefantasySumz_healthAndSafetyPreference_z -0.68 0.32 1.00
## grandiosefantasySumz_recreationalPreference_z -0.12 0.83 1.00
## grandiosefantasySumz_Age -0.03 -0.00 1.00
## grandiosefantasySumz_ethicalPreference_z:Gender -0.16 0.44 1.00
## grandiosefantasySumz_Gender:financialPreference_z -0.26 0.26 1.00
## grandiosefantasySumz_Gender:socialPreference_z -0.20 0.32 1.00
## grandiosefantasySumz_Gender:healthAndSafetyPreference_z -0.30 0.30 1.00
## grandiosefantasySumz_Gender:recreationalPreference_z -0.52 0.04 1.00
## exploitativenessSumz_ethicalPreference_z -0.69 0.36 1.00
## exploitativenessSumz_Gender 0.12 0.62 1.00
## exploitativenessSumz_financialPreference_z -0.56 0.31 1.00
## exploitativenessSumz_socialPreference_z -0.40 0.44 1.00
## exploitativenessSumz_healthAndSafetyPreference_z -0.50 0.49 1.00
## exploitativenessSumz_recreationalPreference_z 0.21 1.15 1.00
## exploitativenessSumz_Age -0.02 0.00 1.00
## exploitativenessSumz_ethicalPreference_z:Gender -0.20 0.40 1.00
## exploitativenessSumz_Gender:financialPreference_z -0.17 0.34 1.00
## exploitativenessSumz_Gender:socialPreference_z -0.14 0.39 1.00
## exploitativenessSumz_Gender:healthAndSafetyPreference_z -0.23 0.36 1.00
## exploitativenessSumz_Gender:recreationalPreference_z -0.70 -0.15 1.00
## ssseSumz_ethicalPreference_z -0.39 0.65 1.00
## ssseSumz_Gender -0.21 0.29 1.00
## ssseSumz_financialPreference_z -0.75 0.13 1.00
## ssseSumz_socialPreference_z -0.06 0.78 1.00
## ssseSumz_healthAndSafetyPreference_z -0.51 0.48 1.00
## ssseSumz_recreationalPreference_z -0.26 0.70 1.00
## ssseSumz_Age -0.02 0.00 1.00
## ssseSumz_ethicalPreference_z:Gender -0.35 0.25 1.00
## ssseSumz_Gender:financialPreference_z -0.07 0.45 1.00
## ssseSumz_Gender:socialPreference_z -0.32 0.21 1.00
## ssseSumz_Gender:healthAndSafetyPreference_z -0.33 0.26 1.00
## ssseSumz_Gender:recreationalPreference_z -0.47 0.09 1.00
## Bulk_ESS Tail_ESS
## grandiosefantasySumz_Intercept 55578 28987
## exploitativenessSumz_Intercept 62198 28628
## ssseSumz_Intercept 52953 30356
## grandiosefantasySumz_ethicalPreference_z 27046 27357
## grandiosefantasySumz_Gender 58864 29123
## grandiosefantasySumz_financialPreference_z 33804 27815
## grandiosefantasySumz_socialPreference_z 32660 28806
## grandiosefantasySumz_healthAndSafetyPreference_z 26373 28008
## grandiosefantasySumz_recreationalPreference_z 29881 27937
## grandiosefantasySumz_Age 58052 30009
## grandiosefantasySumz_ethicalPreference_z:Gender 27048 28011
## grandiosefantasySumz_Gender:financialPreference_z 34445 26736
## grandiosefantasySumz_Gender:socialPreference_z 33144 28090
## grandiosefantasySumz_Gender:healthAndSafetyPreference_z 26333 27883
## grandiosefantasySumz_Gender:recreationalPreference_z 29446 27541
## exploitativenessSumz_ethicalPreference_z 29799 27793
## exploitativenessSumz_Gender 62927 26605
## exploitativenessSumz_financialPreference_z 36202 27605
## exploitativenessSumz_socialPreference_z 32617 27163
## exploitativenessSumz_healthAndSafetyPreference_z 26897 27175
## exploitativenessSumz_recreationalPreference_z 34061 28480
## exploitativenessSumz_Age 63522 29275
## exploitativenessSumz_ethicalPreference_z:Gender 29922 27715
## exploitativenessSumz_Gender:financialPreference_z 36545 27918
## exploitativenessSumz_Gender:socialPreference_z 33627 27545
## exploitativenessSumz_Gender:healthAndSafetyPreference_z 26877 27196
## exploitativenessSumz_Gender:recreationalPreference_z 34180 27946
## ssseSumz_ethicalPreference_z 25840 26426
## ssseSumz_Gender 55688 30258
## ssseSumz_financialPreference_z 31771 26884
## ssseSumz_socialPreference_z 31446 28194
## ssseSumz_healthAndSafetyPreference_z 26056 25947
## ssseSumz_recreationalPreference_z 30948 27845
## ssseSumz_Age 57468 30257
## ssseSumz_ethicalPreference_z:Gender 25826 26890
## ssseSumz_Gender:financialPreference_z 31731 27813
## ssseSumz_Gender:socialPreference_z 32591 28682
## ssseSumz_Gender:healthAndSafetyPreference_z 26567 26837
## ssseSumz_Gender:recreationalPreference_z 31084 27180
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_grandiosefantasySumz 0.96 0.04 0.88 1.04 1.00 61376
## sigma_exploitativenessSumz 0.95 0.04 0.87 1.03 1.00 65407
## sigma_ssseSumz 0.96 0.04 0.88 1.05 1.00 60518
## Tail_ESS
## sigma_grandiosefantasySumz 27531
## sigma_exploitativenessSumz 27054
## sigma_ssseSumz 29353
##
## Residual Correlations:
## Estimate Est.Error l-95% CI
## rescor(grandiosefantasySumz,exploitativenessSumz) 0.17 0.06 0.05
## rescor(grandiosefantasySumz,ssseSumz) 0.39 0.05 0.29
## rescor(exploitativenessSumz,ssseSumz) 0.21 0.06 0.09
## u-95% CI Rhat Bulk_ESS
## rescor(grandiosefantasySumz,exploitativenessSumz) 0.28 1.00 64327
## rescor(grandiosefantasySumz,ssseSumz) 0.49 1.00 57499
## rescor(exploitativenessSumz,ssseSumz) 0.32 1.00 62018
## Tail_ESS
## rescor(grandiosefantasySumz,exploitativenessSumz) 28237
## rescor(grandiosefantasySumz,ssseSumz) 30542
## rescor(exploitativenessSumz,ssseSumz) 29486
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
multi_model_dospert_int_hdi <- bayestestR::hdi(multi_model_dospert_int, effects = "fixed", component = "conditional", ci = .95)
## Warning: Identical densities found along different segments of the distribution,
## choosing rightmost.
kable(multi_model_dospert_int_hdi[
sign(multi_model_dospert_int_hdi$CI_low) == sign(multi_model_dospert_int_hdi$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_grandiosefantasySumz_Age
|
0.95
|
-0.03
|
0.00
|
|
b_exploitativenessSumz_Gender
|
0.95
|
0.13
|
0.63
|
|
b_exploitativenessSumz_recreationalPreference_z
|
0.95
|
0.22
|
1.16
|
|
b_exploitativenessSumz_Gender:recreationalPreference_z
|
0.95
|
-0.70
|
-0.15
|
multi_2_model_dospert_int <- brm(mvbind(cse_Sum_z, devaluing_Sum_z, entitlement_rage_Sum_z, hts_Sum_z) ~ 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, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = multi_2_model_dospert_int_prior
)
multi_2_model_dospert_int_fixef <- fixef(multi_2_model_dospert_int)
summary(multi_2_model_dospert_int)
## Family: MV(gaussian, gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: cse_Sum_z ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## devaluing_Sum_z ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## entitlement_rage_Sum_z ~ ethicalPreference_z * Gender + financialPreference_z * Gender + socialPreference_z * Gender + healthAndSafetyPreference_z * Gender + recreationalPreference_z * Gender + Age
## hts_Sum_z ~ 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
## cseSumz_Intercept 0.74 0.20
## devaluingSumz_Intercept 0.79 0.20
## entitlementrageSumz_Intercept 0.70 0.20
## htsSumz_Intercept 0.53 0.20
## cseSumz_ethicalPreference_z 0.22 0.13
## cseSumz_Gender2 0.12 0.12
## cseSumz_financialPreference_z -0.34 0.10
## cseSumz_socialPreference_z 0.14 0.10
## cseSumz_healthAndSafetyPreference_z -0.03 0.12
## cseSumz_recreationalPreference_z 0.15 0.11
## cseSumz_Age -0.03 0.01
## cseSumz_ethicalPreference_z:Gender2 0.00 0.15
## cseSumz_Gender2:financialPreference_z 0.27 0.13
## cseSumz_Gender2:socialPreference_z -0.03 0.13
## cseSumz_Gender2:healthAndSafetyPreference_z 0.00 0.15
## cseSumz_Gender2:recreationalPreference_z -0.27 0.14
## devaluingSumz_ethicalPreference_z -0.22 0.13
## devaluingSumz_Gender2 -0.09 0.13
## devaluingSumz_financialPreference_z 0.01 0.11
## devaluingSumz_socialPreference_z 0.02 0.10
## devaluingSumz_healthAndSafetyPreference_z 0.28 0.12
## devaluingSumz_recreationalPreference_z -0.01 0.12
## devaluingSumz_Age -0.02 0.01
## devaluingSumz_ethicalPreference_z:Gender2 0.38 0.15
## devaluingSumz_Gender2:financialPreference_z 0.05 0.13
## devaluingSumz_Gender2:socialPreference_z 0.10 0.14
## devaluingSumz_Gender2:healthAndSafetyPreference_z -0.18 0.15
## devaluingSumz_Gender2:recreationalPreference_z -0.19 0.14
## entitlementrageSumz_ethicalPreference_z 0.06 0.13
## entitlementrageSumz_Gender2 0.02 0.13
## entitlementrageSumz_financialPreference_z -0.17 0.11
## entitlementrageSumz_socialPreference_z 0.18 0.10
## entitlementrageSumz_healthAndSafetyPreference_z -0.07 0.12
## entitlementrageSumz_recreationalPreference_z 0.08 0.12
## entitlementrageSumz_Age -0.02 0.01
## entitlementrageSumz_ethicalPreference_z:Gender2 0.17 0.16
## entitlementrageSumz_Gender2:financialPreference_z 0.17 0.13
## entitlementrageSumz_Gender2:socialPreference_z -0.01 0.14
## entitlementrageSumz_Gender2:healthAndSafetyPreference_z 0.01 0.15
## entitlementrageSumz_Gender2:recreationalPreference_z -0.25 0.14
## htsSumz_ethicalPreference_z 0.00 0.13
## htsSumz_Gender2 -0.12 0.13
## htsSumz_financialPreference_z -0.34 0.11
## htsSumz_socialPreference_z 0.06 0.10
## htsSumz_healthAndSafetyPreference_z 0.16 0.12
## htsSumz_recreationalPreference_z 0.26 0.12
## htsSumz_Age -0.02 0.01
## htsSumz_ethicalPreference_z:Gender2 0.14 0.16
## htsSumz_Gender2:financialPreference_z 0.29 0.13
## htsSumz_Gender2:socialPreference_z 0.04 0.14
## htsSumz_Gender2:healthAndSafetyPreference_z -0.14 0.15
## htsSumz_Gender2:recreationalPreference_z -0.38 0.14
## l-95% CI u-95% CI Rhat
## cseSumz_Intercept 0.35 1.13 1.00
## devaluingSumz_Intercept 0.39 1.18 1.00
## entitlementrageSumz_Intercept 0.30 1.09 1.00
## htsSumz_Intercept 0.13 0.92 1.00
## cseSumz_ethicalPreference_z -0.02 0.47 1.00
## cseSumz_Gender2 -0.12 0.36 1.00
## cseSumz_financialPreference_z -0.55 -0.14 1.00
## cseSumz_socialPreference_z -0.05 0.32 1.00
## cseSumz_healthAndSafetyPreference_z -0.26 0.20 1.00
## cseSumz_recreationalPreference_z -0.08 0.37 1.00
## cseSumz_Age -0.04 -0.01 1.00
## cseSumz_ethicalPreference_z:Gender2 -0.30 0.30 1.00
## cseSumz_Gender2:financialPreference_z 0.01 0.52 1.00
## cseSumz_Gender2:socialPreference_z -0.29 0.23 1.00
## cseSumz_Gender2:healthAndSafetyPreference_z -0.29 0.30 1.00
## cseSumz_Gender2:recreationalPreference_z -0.55 0.01 1.00
## devaluingSumz_ethicalPreference_z -0.47 0.04 1.00
## devaluingSumz_Gender2 -0.33 0.16 1.00
## devaluingSumz_financialPreference_z -0.20 0.22 1.00
## devaluingSumz_socialPreference_z -0.17 0.21 1.00
## devaluingSumz_healthAndSafetyPreference_z 0.05 0.52 1.00
## devaluingSumz_recreationalPreference_z -0.24 0.21 1.00
## devaluingSumz_Age -0.04 -0.01 1.00
## devaluingSumz_ethicalPreference_z:Gender2 0.07 0.67 1.00
## devaluingSumz_Gender2:financialPreference_z -0.21 0.31 1.00
## devaluingSumz_Gender2:socialPreference_z -0.16 0.37 1.00
## devaluingSumz_Gender2:healthAndSafetyPreference_z -0.48 0.12 1.00
## devaluingSumz_Gender2:recreationalPreference_z -0.47 0.09 1.00
## entitlementrageSumz_ethicalPreference_z -0.19 0.32 1.00
## entitlementrageSumz_Gender2 -0.23 0.27 1.00
## entitlementrageSumz_financialPreference_z -0.38 0.04 1.00
## entitlementrageSumz_socialPreference_z -0.02 0.37 1.00
## entitlementrageSumz_healthAndSafetyPreference_z -0.30 0.17 1.00
## entitlementrageSumz_recreationalPreference_z -0.15 0.31 1.00
## entitlementrageSumz_Age -0.04 -0.01 1.00
## entitlementrageSumz_ethicalPreference_z:Gender2 -0.13 0.48 1.00
## entitlementrageSumz_Gender2:financialPreference_z -0.09 0.43 1.00
## entitlementrageSumz_Gender2:socialPreference_z -0.28 0.25 1.00
## entitlementrageSumz_Gender2:healthAndSafetyPreference_z -0.28 0.32 1.00
## entitlementrageSumz_Gender2:recreationalPreference_z -0.53 0.04 1.00
## htsSumz_ethicalPreference_z -0.25 0.26 1.00
## htsSumz_Gender2 -0.37 0.12 1.00
## htsSumz_financialPreference_z -0.55 -0.13 1.00
## htsSumz_socialPreference_z -0.14 0.25 1.00
## htsSumz_healthAndSafetyPreference_z -0.07 0.40 1.00
## htsSumz_recreationalPreference_z 0.03 0.49 1.00
## htsSumz_Age -0.03 -0.00 1.00
## htsSumz_ethicalPreference_z:Gender2 -0.16 0.45 1.00
## htsSumz_Gender2:financialPreference_z 0.03 0.55 1.00
## htsSumz_Gender2:socialPreference_z -0.23 0.31 1.00
## htsSumz_Gender2:healthAndSafetyPreference_z -0.44 0.17 1.00
## htsSumz_Gender2:recreationalPreference_z -0.66 -0.10 1.00
## Bulk_ESS Tail_ESS
## cseSumz_Intercept 38165 30763
## devaluingSumz_Intercept 34845 29277
## entitlementrageSumz_Intercept 37733 30959
## htsSumz_Intercept 38242 32141
## cseSumz_ethicalPreference_z 18363 24816
## cseSumz_Gender2 33310 30538
## cseSumz_financialPreference_z 21032 26705
## cseSumz_socialPreference_z 20768 26417
## cseSumz_healthAndSafetyPreference_z 18934 25104
## cseSumz_recreationalPreference_z 19874 25748
## cseSumz_Age 38483 30729
## cseSumz_ethicalPreference_z:Gender2 18803 24569
## cseSumz_Gender2:financialPreference_z 21246 26182
## cseSumz_Gender2:socialPreference_z 22286 28598
## cseSumz_Gender2:healthAndSafetyPreference_z 19674 25646
## cseSumz_Gender2:recreationalPreference_z 20147 26411
## devaluingSumz_ethicalPreference_z 17255 23862
## devaluingSumz_Gender2 29951 29126
## devaluingSumz_financialPreference_z 19851 25850
## devaluingSumz_socialPreference_z 17980 24965
## devaluingSumz_healthAndSafetyPreference_z 18175 25003
## devaluingSumz_recreationalPreference_z 18158 25308
## devaluingSumz_Age 35175 30589
## devaluingSumz_ethicalPreference_z:Gender2 17846 23927
## devaluingSumz_Gender2:financialPreference_z 19397 25710
## devaluingSumz_Gender2:socialPreference_z 19799 26625
## devaluingSumz_Gender2:healthAndSafetyPreference_z 18745 25113
## devaluingSumz_Gender2:recreationalPreference_z 18889 25016
## entitlementrageSumz_ethicalPreference_z 18002 23706
## entitlementrageSumz_Gender2 32122 30231
## entitlementrageSumz_financialPreference_z 20647 26796
## entitlementrageSumz_socialPreference_z 19390 25349
## entitlementrageSumz_healthAndSafetyPreference_z 18577 25271
## entitlementrageSumz_recreationalPreference_z 18799 25361
## entitlementrageSumz_Age 38096 31567
## entitlementrageSumz_ethicalPreference_z:Gender2 18218 24035
## entitlementrageSumz_Gender2:financialPreference_z 20348 25562
## entitlementrageSumz_Gender2:socialPreference_z 21330 27539
## entitlementrageSumz_Gender2:healthAndSafetyPreference_z 19272 25653
## entitlementrageSumz_Gender2:recreationalPreference_z 18903 26280
## htsSumz_ethicalPreference_z 18768 24986
## htsSumz_Gender2 32617 27957
## htsSumz_financialPreference_z 22443 27411
## htsSumz_socialPreference_z 20303 26507
## htsSumz_healthAndSafetyPreference_z 18998 26111
## htsSumz_recreationalPreference_z 19665 26045
## htsSumz_Age 38652 31140
## htsSumz_ethicalPreference_z:Gender2 19686 25084
## htsSumz_Gender2:financialPreference_z 22030 26824
## htsSumz_Gender2:socialPreference_z 22574 28140
## htsSumz_Gender2:healthAndSafetyPreference_z 19767 25200
## htsSumz_Gender2:recreationalPreference_z 19574 25847
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_cseSumz 0.93 0.04 0.85 1.01 1.00 42949
## sigma_devaluingSumz 0.94 0.04 0.87 1.03 1.00 38758
## sigma_entitlementrageSumz 0.95 0.04 0.87 1.03 1.00 39495
## sigma_htsSumz 0.95 0.04 0.87 1.03 1.00 45081
## Tail_ESS
## sigma_cseSumz 28149
## sigma_devaluingSumz 29967
## sigma_entitlementrageSumz 28611
## sigma_htsSumz 29007
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(cseSumz,devaluingSumz) 0.50 0.05 0.41 0.59
## rescor(cseSumz,entitlementrageSumz) 0.50 0.05 0.41 0.59
## rescor(devaluingSumz,entitlementrageSumz) 0.59 0.04 0.50 0.66
## rescor(cseSumz,htsSumz) 0.50 0.05 0.40 0.58
## rescor(devaluingSumz,htsSumz) 0.54 0.04 0.45 0.62
## rescor(entitlementrageSumz,htsSumz) 0.44 0.05 0.33 0.53
## Rhat Bulk_ESS Tail_ESS
## rescor(cseSumz,devaluingSumz) 1.00 32923 30474
## rescor(cseSumz,entitlementrageSumz) 1.00 36417 29786
## rescor(devaluingSumz,entitlementrageSumz) 1.00 42045 30154
## rescor(cseSumz,htsSumz) 1.00 36651 28149
## rescor(devaluingSumz,htsSumz) 1.00 42024 28754
## rescor(entitlementrageSumz,htsSumz) 1.00 42229 28873
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
multi_2_model_dospert_int_hdi_est <- bayestestR::hdi(multi_2_model_dospert_int, effects = "fixed", component = "conditional", ci = .95)
kable(multi_2_model_dospert_int_hdi[
sign(multi_2_model_dospert_int_hdi$CI_low) == sign(multi_2_model_dospert_int_hdi$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_cseSumz_Intercept
|
0.95
|
0.36
|
1.14
|
|
b_cseSumz_financialPreference_z
|
0.95
|
-0.54
|
-0.14
|
|
b_cseSumz_Age
|
0.95
|
-0.04
|
-0.01
|
|
b_cseSumz_Gender2:financialPreference_z
|
0.95
|
0.02
|
0.52
|
|
b_devaluingSumz_Intercept
|
0.95
|
0.39
|
1.17
|
|
b_devaluingSumz_healthAndSafetyPreference_z
|
0.95
|
0.05
|
0.52
|
|
b_devaluingSumz_Age
|
0.95
|
-0.04
|
-0.01
|
|
b_devaluingSumz_ethicalPreference_z:Gender2
|
0.95
|
0.07
|
0.67
|
|
b_entitlementrageSumz_Intercept
|
0.95
|
0.30
|
1.09
|
|
b_entitlementrageSumz_Age
|
0.95
|
-0.04
|
-0.01
|
|
b_htsSumz_Intercept
|
0.95
|
0.14
|
0.93
|
|
b_htsSumz_financialPreference_z
|
0.95
|
-0.54
|
-0.13
|
|
b_htsSumz_recreationalPreference_z
|
0.95
|
0.04
|
0.49
|
|
b_htsSumz_Age
|
0.95
|
-0.03
|
0.00
|
|
b_htsSumz_Gender2:financialPreference_z
|
0.95
|
0.03
|
0.55
|
|
b_htsSumz_Gender2:recreationalPreference_z
|
0.95
|
-0.66
|
-0.10
|
pni_risk_dospert <- brm(mvbind(riskSum_z, riskPerceptionSum_z, riskBenefitSum_z) ~ dominance_Sum + prestige_Sum + leadership_Sum + grandiosity_Sum_z + vulnerability_Sum_z + Gender + Age,
data = Experiment_2_demographics_Gender, cores = parallel::detectCores(), iter = 10000, warmup = 1000, backend = "cmdstanr", save_pars = save_pars(all = TRUE),
prior = pni_risk_dospert_multi_prior
)
saveRDS(pni_risk_dospert, "pni_risk_dospert.rds")
pni_risk_dospert_fixef <- fixef(pni_risk_dospert)
saveRDS(pni_risk_dospert_fix, "pni_risk_dospert_fix.rds")
summary(pni_risk_dospert)
## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: riskSum_z ~ dominance_Sum + prestige_Sum + leadership_Sum + grandiosity_Sum_z + vulnerability_Sum_z + Gender + Age
## riskPerceptionSum_z ~ dominance_Sum + prestige_Sum + leadership_Sum + grandiosity_Sum_z + vulnerability_Sum_z + Gender + Age
## riskBenefitSum_z ~ dominance_Sum + prestige_Sum + leadership_Sum + grandiosity_Sum_z + vulnerability_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
## riskSumz_Intercept 0.19 0.19 -0.19 0.57
## riskPerceptionSumz_Intercept -0.04 0.20 -0.43 0.35
## riskBenefitSumz_Intercept 0.73 0.19 0.35 1.10
## riskSumz_dominance_Sum 0.27 0.07 0.13 0.40
## riskSumz_prestige_Sum 0.01 0.07 -0.13 0.14
## riskSumz_leadership_Sum -0.01 0.07 -0.14 0.13
## riskSumz_grandiosity_Sum_z 0.14 0.08 -0.01 0.30
## riskSumz_vulnerability_Sum_z -0.02 0.07 -0.16 0.12
## riskSumz_Gender2 0.25 0.12 0.02 0.48
## riskSumz_Age -0.01 0.01 -0.02 0.00
## riskPerceptionSumz_dominance_Sum -0.25 0.07 -0.38 -0.11
## riskPerceptionSumz_prestige_Sum 0.03 0.07 -0.11 0.17
## riskPerceptionSumz_leadership_Sum 0.09 0.07 -0.05 0.24
## riskPerceptionSumz_grandiosity_Sum_z 0.03 0.08 -0.13 0.19
## riskPerceptionSumz_vulnerability_Sum_z 0.12 0.08 -0.03 0.27
## riskPerceptionSumz_Gender2 -0.33 0.12 -0.57 -0.09
## riskPerceptionSumz_Age 0.01 0.01 -0.00 0.02
## riskBenefitSumz_dominance_Sum 0.22 0.07 0.09 0.35
## riskBenefitSumz_prestige_Sum -0.14 0.07 -0.27 -0.00
## riskBenefitSumz_leadership_Sum 0.09 0.07 -0.05 0.23
## riskBenefitSumz_grandiosity_Sum_z 0.10 0.08 -0.06 0.25
## riskBenefitSumz_vulnerability_Sum_z -0.02 0.07 -0.16 0.13
## riskBenefitSumz_Gender2 0.15 0.12 -0.07 0.38
## riskBenefitSumz_Age -0.03 0.01 -0.04 -0.02
## Rhat Bulk_ESS Tail_ESS
## riskSumz_Intercept 1.00 49833 30687
## riskPerceptionSumz_Intercept 1.00 61091 29309
## riskBenefitSumz_Intercept 1.00 55376 28464
## riskSumz_dominance_Sum 1.00 38070 30140
## riskSumz_prestige_Sum 1.00 41360 27597
## riskSumz_leadership_Sum 1.00 33832 30711
## riskSumz_grandiosity_Sum_z 1.00 32854 30033
## riskSumz_vulnerability_Sum_z 1.00 31708 28217
## riskSumz_Gender2 1.00 40124 29682
## riskSumz_Age 1.00 50415 29928
## riskPerceptionSumz_dominance_Sum 1.00 45150 28938
## riskPerceptionSumz_prestige_Sum 1.00 52208 29808
## riskPerceptionSumz_leadership_Sum 1.00 44069 30293
## riskPerceptionSumz_grandiosity_Sum_z 1.00 41618 29030
## riskPerceptionSumz_vulnerability_Sum_z 1.00 40129 30024
## riskPerceptionSumz_Gender2 1.00 46599 29433
## riskPerceptionSumz_Age 1.00 60734 29516
## riskBenefitSumz_dominance_Sum 1.00 41705 29591
## riskBenefitSumz_prestige_Sum 1.00 47194 28802
## riskBenefitSumz_leadership_Sum 1.00 37779 30262
## riskBenefitSumz_grandiosity_Sum_z 1.00 35084 29829
## riskBenefitSumz_vulnerability_Sum_z 1.00 34189 29024
## riskBenefitSumz_Gender2 1.00 44194 29459
## riskBenefitSumz_Age 1.00 55823 31142
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_riskSumz 0.92 0.04 0.85 1.00 1.00 55858
## sigma_riskPerceptionSumz 0.96 0.04 0.88 1.05 1.00 63096
## sigma_riskBenefitSumz 0.92 0.04 0.85 1.00 1.00 60726
## Tail_ESS
## sigma_riskSumz 30911
## sigma_riskPerceptionSumz 29697
## sigma_riskBenefitSumz 28271
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(riskSumz,riskPerceptionSumz) -0.36 0.05 -0.46 -0.26
## rescor(riskSumz,riskBenefitSumz) 0.49 0.05 0.40 0.58
## rescor(riskPerceptionSumz,riskBenefitSumz) -0.18 0.06 -0.29 -0.06
## Rhat Bulk_ESS Tail_ESS
## rescor(riskSumz,riskPerceptionSumz) 1.00 60910 27765
## rescor(riskSumz,riskBenefitSumz) 1.00 54171 31124
## rescor(riskPerceptionSumz,riskBenefitSumz) 1.00 63078 27302
##
## 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).
pni_risk_dospert_hdi <- bayestestR::hdi(pni_risk_dospert, effects = "fixed", component = "conditional", ci = .95)
kable(pni_risk_dospert_hdi[
sign(pni_risk_dospert_hdi$CI_low) == sign(pni_risk_dospert_hdi$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_riskSumz_dominance_Sum
|
0.95
|
0.13
|
0.40
|
|
b_riskSumz_Gender2
|
0.95
|
0.02
|
0.48
|
|
b_riskPerceptionSumz_dominance_Sum
|
0.95
|
-0.38
|
-0.11
|
|
b_riskPerceptionSumz_Gender2
|
0.95
|
-0.57
|
-0.09
|
|
b_riskBenefitSumz_Intercept
|
0.95
|
0.35
|
1.10
|
|
b_riskBenefitSumz_dominance_Sum
|
0.95
|
0.09
|
0.35
|
|
b_riskBenefitSumz_prestige_Sum
|
0.95
|
-0.27
|
0.00
|
|
b_riskBenefitSumz_Age
|
0.95
|
-0.04
|
-0.02
|
hdi_tables <- mget(ls(pattern = "_hdi"))
hdi_tables <- data.table(hdi_tables)
for (i in hdi_tables) {
i <- i %>%
mutate(
Parameter = gsub("z_", " * ", Parameter),
Parameter = gsub("_Sum", " ", Parameter),
Parameter = gsub("Risk", "Risk ", Parameter),
Parameter = gsub("b_", " ", Parameter),
Parameter = gsub("Sum_", " * ", Parameter)
)
}
for (i in hdi_tables) {
data.table(hdi_tables)
}
for (i in c()) {
assign(i, transform(get(i), Parameter = gsub("z_", " * ", Parameter)))
}
for (i in hdi_tables) {
i <- as.data.table(i)
}
list2env(
lapply(mget(ls(pattern = "_hdi")), function(w) transform(w, Parameter = gsub("z_", " * ", Parameter))),
envir = .GlobalEnv
)
model_bayes.1 <- "
#Measurement
Preference_1 = ~ dominance_Sum + prestige_Sum + leadership_Sum + Gender + Age
Preference_2 = ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + Age
Preference_3 = ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z + Age
Preference_4 = ~ dominance_Sum * Gender + prestige_Sum * Gender + leadership_Sum * Gender + PNI_Sum_z * Gender + Age
# Structure
Preference_3 ~ Preference_2 + Preference_1
Preference_4 ~ Preference_2 + Preference_3
Preference_1 ~~ Preference_2
Preference_3 ~~ Preference_4
"
future::plan("multisession")
# options(future.globals.maxSize = 10485760000)
test_bayes_fit <- blavaan::bsem(
model = model_bayes.1, data = Experiment_2_demographics_Gender, auto.var = TRUE, auto.fix.first = TRUE, n.chains = 4, seed = 1234, target = "cmdstan",
auto.cov.lv.x = TRUE, ordered = "Gender"
)
bmod_blavaan <- "
# equation where dopl is predicted by dospert
generalRiskPreference_z ~ ethicalPreference_z + financialPreference_z + socialPreference_z + healthAndSafetyPreference_z + recreationalPreference_z + Gender + Age
generalExpectedBenefits_z ~ vulnerability_Sum_z + grandiosity_Sum_z
generalPerceievedRisk_z ~ cse_Sum_z + devaluing_Sum_z + entitlement_rage_Sum_z + hts_Sum_z + Gender + Age
dominance_Sum ~ financialPreference_z + vulnerability_Sum_z + grandiosity_Sum_z + Gender + Age
generalRiskPreference_z ~ ~ generalExpectedBenefits_z
generalPerceievedRisk_z ~ ~ dominance_Sum
generalRiskPreference_z ~ ~ dominance_Sum
generalPerceievedRisk_z ~ ~ generalExpectedBenefits_z
generalRiskPreference_z ~ ~ generalPerceievedRisk_z
generalExpectedBenefits_z ~ ~ dominance_Sum
"
# Blavaan analysis
blavaan_fit <- blavaan(bmod_blavaan, data = Experiment_2_demographics_Gender, bcontrol = list(cores = parallel::detectCores()), auto.var = T, auto.fix.first = T, n.chains = 4, seed = 1234, target = "cmdstan", auto.cov.lv.x = T, ordered = "Gender")
saveRDS(blavaan_fit, "blavaan_fit.rds")
summary(blavaan_fit)
graph_sem(blavaan_fit)
all.fi <- list.files("../Analyzable data/", pattern = "DOSPERT", full.names = TRUE)
library(readr)
ans <- sapply(all.fi, function(i) {
eachline <- fread(i, select = "time_elapsed")
return(ans)
})
write_lines(ans, "/Output_Files/output.csv")
matrix_list.fi <- data.frame(matrix(unlist(fileList)), stringsAsFactors = F)
matrix_list.fi <- data.frame(Reduce(rbind, ans))
