Experiment 1 Analysis | DoPL and DOSPERT
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## - CmdStan version: 2.29.2
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## arlibrary(tidybayes)##
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## hdilibrary(ggthemes)
library(correlation)
load("clean_analysis_redux.RData")
experiment_dataset_analysis <- read.csv("./experiment_dataset_analysis.csv")experiment_dataset_analysis_scaled <- subset(experiment_dataset_analysis, select = c(Age, Gender, DoPLSum:generalRiskPreference))
experiment_dataset_analysis_scaled$Gender <- as.factor(experiment_dataset_analysis_scaled$Gender)
experiment_dataset_analysis_scaled[c(28:33)] <- lapply(experiment_dataset_analysis_scaled[c(28:33)], function(x) c(scale(x)))experiment_dataset_analysis_scaled_complete <- experiment_dataset_analysis_scaled[complete.cases(experiment_dataset_analysis_scaled), ]
exp_1_corr <- correlation(experiment_dataset_analysis_scaled[, c(1, 3:33)],
bayesian = TRUE
)exp_1_corr[c(1:3, 5:9)]## Parameter1 | Parameter2 | rho | CI | pd | % in ROPE | Prior_Distribution
## -------------------------------------------------------------------------------------------------------------------------------------------------------
## Age | DoPLSum | -0.18 | [-0.34, 0.00] | 98.10%* | 18.20% | beta
## Age | dominanceSum | -0.20 | [-0.37, -0.02] | 98.08%* | 15.17% | beta
## Age | prestigeSum | -0.10 | [-0.28, 0.07] | 85.40% | 48.10% | beta
## Age | leadershipSum | -0.11 | [-0.28, 0.09] | 86.83% | 45.75% | beta
## Age | UMSSum | -0.19 | [-0.37, -0.02] | 98.25%* | 16.12% | beta
## Age | UMSIntimacySum | -0.17 | [-0.35, 0.00] | 96.83% | 21.65% | beta
## Age | UMSAffiliationSum | -0.17 | [-0.35, 0.00] | 96.67% | 21.70% | beta
## Age | riskSum | -0.11 | [-0.29, 0.08] | 86.78% | 45.95% | beta
## Age | riskPerceptionSum | -0.11 | [-0.29, 0.07] | 87.78% | 45.42% | beta
## Age | riskBenefitSum | -0.03 | [-0.21, 0.15] | 61.50% | 69.55% | beta
## Age | ethicalQuestionsRiskSum | -0.12 | [-0.31, 0.06] | 90.30% | 40.02% | beta
## Age | financialQuestionsRiskSum | -0.08 | [-0.26, 0.10] | 80.73% | 55.23% | beta
## Age | healthAndSafetyQuestionsRiskSum | -0.06 | [-0.24, 0.13] | 74.80% | 60.32% | beta
## Age | recreationalQuestionsRiskSum | -0.07 | [-0.25, 0.12] | 75.55% | 59.25% | beta
## Age | socialQuestionsRiskSum | -0.10 | [-0.28, 0.08] | 84.05% | 48.93% | beta
## Age | ethicalQuestionsBenefitSum | 0.02 | [-0.18, 0.20] | 55.90% | 70.20% | beta
## Age | financialQuestionsBenefitSum | -0.07 | [-0.25, 0.12] | 76.12% | 59.67% | beta
## Age | healthAndSafetyQuestionsBenefitSum | 0.03 | [-0.15, 0.22] | 64.55% | 67.03% | beta
## Age | recreationalQuestionsBenefitSum | 6.77e-03 | [-0.18, 0.19] | 53.02% | 71.03% | beta
## Age | socialQuestionsBenefitSum | -0.10 | [-0.28, 0.10] | 84.20% | 49.10% | beta
## Age | ethicalQuestionsPerceptionSum | -0.12 | [-0.31, 0.05] | 90.92% | 39.48% | beta
## Age | financialQuestionsPerceptionSum | -0.08 | [-0.26, 0.10] | 80.17% | 55.77% | beta
## Age | healthAndSafetyQuestionsPerceptionSum | -0.10 | [-0.28, 0.08] | 86.80% | 48.05% | beta
## Age | recreationalQuestionsPerceptionSum | -0.15 | [-0.32, 0.02] | 95.17% | 28.15% | beta
## Age | socialQuestionsPerceptionSum | -8.14e-04 | [-0.19, 0.19] | 50.48% | 69.83% | beta
## Age | ethicalPreference | 0.04 | [-0.14, 0.22] | 65.72% | 68.00% | beta
## Age | financialPreference | -0.07 | [-0.26, 0.10] | 77.72% | 59.20% | beta
## Age | socialPreference | -0.10 | [-0.27, 0.09] | 84.20% | 49.33% | beta
## Age | healthAndSafetyPreference | 0.05 | [-0.14, 0.24] | 69.25% | 64.30% | beta
## Age | recreationalPreference | 0.06 | [-0.12, 0.24] | 71.90% | 63.45% | beta
## Age | generalRiskPreference | -0.02 | [-0.21, 0.15] | 59.05% | 70.35% | beta
## DoPLSum | dominanceSum | 0.73 | [ 0.63, 0.81] | 100%*** | 0% | beta
## DoPLSum | prestigeSum | 0.73 | [ 0.64, 0.81] | 100%*** | 0% | beta
## DoPLSum | leadershipSum | 0.73 | [ 0.65, 0.82] | 100%*** | 0% | beta
## DoPLSum | UMSSum | 0.38 | [ 0.21, 0.54] | 100%*** | 0.07% | beta
## DoPLSum | UMSIntimacySum | 0.25 | [ 0.07, 0.42] | 99.62%** | 4.80% | beta
## DoPLSum | UMSAffiliationSum | 0.38 | [ 0.21, 0.52] | 100%*** | 0.05% | beta
## DoPLSum | riskSum | 0.41 | [ 0.25, 0.55] | 100%*** | 0% | beta
## DoPLSum | riskPerceptionSum | -6.55e-03 | [-0.18, 0.17] | 52.98% | 72.88% | beta
## DoPLSum | riskBenefitSum | 0.26 | [ 0.09, 0.43] | 99.72%** | 3.52% | beta
## DoPLSum | ethicalQuestionsRiskSum | 0.34 | [ 0.19, 0.51] | 100%*** | 0.33% | beta
## DoPLSum | financialQuestionsRiskSum | 0.34 | [ 0.19, 0.50] | 100%*** | 0.40% | beta
## DoPLSum | healthAndSafetyQuestionsRiskSum | 0.30 | [ 0.13, 0.46] | 99.80%** | 1.32% | beta
## DoPLSum | recreationalQuestionsRiskSum | 0.31 | [ 0.14, 0.47] | 100%*** | 0.90% | beta
## DoPLSum | socialQuestionsRiskSum | 0.28 | [ 0.10, 0.44] | 99.98%*** | 2.95% | beta
## DoPLSum | ethicalQuestionsBenefitSum | 0.27 | [ 0.10, 0.43] | 99.83%** | 3.15% | beta
## DoPLSum | financialQuestionsBenefitSum | 0.18 | [ 0.02, 0.36] | 97.92%* | 16.30% | beta
## DoPLSum | healthAndSafetyQuestionsBenefitSum | 0.20 | [ 0.02, 0.37] | 98.98%* | 12.38% | beta
## DoPLSum | recreationalQuestionsBenefitSum | 0.22 | [ 0.05, 0.40] | 99.22%** | 9.57% | beta
## DoPLSum | socialQuestionsBenefitSum | 0.28 | [ 0.11, 0.44] | 99.88%** | 2.30% | beta
## DoPLSum | ethicalQuestionsPerceptionSum | -0.05 | [-0.23, 0.14] | 68.62% | 65.12% | beta
## DoPLSum | financialQuestionsPerceptionSum | 0.03 | [-0.16, 0.20] | 63.45% | 68.55% | beta
## DoPLSum | healthAndSafetyQuestionsPerceptionSum | 0.02 | [-0.16, 0.20] | 60.17% | 69.25% | beta
## DoPLSum | recreationalQuestionsPerceptionSum | -0.02 | [-0.20, 0.16] | 58.23% | 69.92% | beta
## DoPLSum | socialQuestionsPerceptionSum | -0.01 | [-0.18, 0.19] | 56.17% | 71.60% | beta
## DoPLSum | ethicalPreference | 0.27 | [ 0.10, 0.44] | 99.78%** | 2.85% | beta
## DoPLSum | financialPreference | 0.18 | [ 0.01, 0.37] | 97.60%* | 18.00% | beta
## DoPLSum | socialPreference | 0.27 | [ 0.10, 0.45] | 99.92%*** | 3.17% | beta
## DoPLSum | healthAndSafetyPreference | 0.20 | [ 0.01, 0.37] | 98.20%* | 15.02% | beta
## DoPLSum | recreationalPreference | 0.20 | [ 0.02, 0.37] | 98.40%* | 14.90% | beta
## DoPLSum | generalRiskPreference | 0.26 | [ 0.09, 0.42] | 99.88%** | 3.88% | beta
## dominanceSum | prestigeSum | 0.37 | [ 0.20, 0.52] | 100%*** | 0.22% | beta
## dominanceSum | leadershipSum | 0.27 | [ 0.10, 0.43] | 99.88%** | 2.93% | beta
## dominanceSum | UMSSum | 0.13 | [-0.05, 0.30] | 92.12% | 36.18% | beta
## dominanceSum | UMSIntimacySum | 8.89e-03 | [-0.18, 0.18] | 53.57% | 71.40% | beta
## dominanceSum | UMSAffiliationSum | 0.18 | [-0.01, 0.35] | 96.60% | 22.55% | beta
## dominanceSum | riskSum | 0.41 | [ 0.25, 0.56] | 100%*** | 0% | beta
## dominanceSum | riskPerceptionSum | -0.12 | [-0.31, 0.05] | 89.03% | 42.70% | beta
## dominanceSum | riskBenefitSum | 0.25 | [ 0.08, 0.42] | 99.88%** | 4.70% | beta
## dominanceSum | ethicalQuestionsRiskSum | 0.37 | [ 0.22, 0.53] | 100%*** | 0.10% | beta
## dominanceSum | financialQuestionsRiskSum | 0.32 | [ 0.16, 0.48] | 99.98%*** | 0.65% | beta
## dominanceSum | healthAndSafetyQuestionsRiskSum | 0.34 | [ 0.16, 0.49] | 99.98%*** | 0.25% | beta
## dominanceSum | recreationalQuestionsRiskSum | 0.36 | [ 0.19, 0.52] | 100%*** | 0% | beta
## dominanceSum | socialQuestionsRiskSum | 0.20 | [ 0.02, 0.36] | 98.58%* | 14.62% | beta
## dominanceSum | ethicalQuestionsBenefitSum | 0.27 | [ 0.11, 0.44] | 99.80%** | 2.70% | beta
## dominanceSum | financialQuestionsBenefitSum | 0.15 | [-0.03, 0.32] | 94.15% | 30.05% | beta
## dominanceSum | healthAndSafetyQuestionsBenefitSum | 0.22 | [ 0.05, 0.39] | 99.10%** | 10.72% | beta
## dominanceSum | recreationalQuestionsBenefitSum | 0.30 | [ 0.14, 0.47] | 100%*** | 1.55% | beta
## dominanceSum | socialQuestionsBenefitSum | 0.19 | [ 0.02, 0.36] | 98.42%* | 14.52% | beta
## dominanceSum | ethicalQuestionsPerceptionSum | -0.18 | [-0.36, -0.02] | 98.05%* | 16.60% | beta
## dominanceSum | financialQuestionsPerceptionSum | -0.09 | [-0.28, 0.09] | 82.33% | 51.78% | beta
## dominanceSum | healthAndSafetyQuestionsPerceptionSum | -0.07 | [-0.26, 0.10] | 79.10% | 58.43% | beta
## dominanceSum | recreationalQuestionsPerceptionSum | -0.12 | [-0.30, 0.05] | 91.33% | 39.48% | beta
## dominanceSum | socialQuestionsPerceptionSum | -0.02 | [-0.21, 0.16] | 59.55% | 70.73% | beta
## dominanceSum | ethicalPreference | 0.29 | [ 0.12, 0.46] | 99.78%** | 1.70% | beta
## dominanceSum | financialPreference | 0.14 | [-0.03, 0.32] | 93.05% | 34.23% | beta
## dominanceSum | socialPreference | 0.20 | [ 0.02, 0.37] | 98.12%* | 14.90% | beta
## dominanceSum | healthAndSafetyPreference | 0.22 | [ 0.04, 0.39] | 99.15%** | 9.18% | beta
## dominanceSum | recreationalPreference | 0.31 | [ 0.15, 0.47] | 99.95%*** | 0.85% | beta
## dominanceSum | generalRiskPreference | 0.26 | [ 0.09, 0.42] | 99.95%*** | 4.15% | beta
## prestigeSum | leadershipSum | 0.36 | [ 0.20, 0.52] | 100%*** | 0.07% | beta
## prestigeSum | UMSSum | 0.45 | [ 0.30, 0.60] | 100%*** | 0% | beta
## prestigeSum | UMSIntimacySum | 0.43 | [ 0.28, 0.58] | 100%*** | 0% | beta
## prestigeSum | UMSAffiliationSum | 0.38 | [ 0.22, 0.54] | 100%*** | 0% | beta
## prestigeSum | riskSum | 0.31 | [ 0.14, 0.47] | 100%*** | 0.68% | beta
## prestigeSum | riskPerceptionSum | 0.12 | [-0.05, 0.30] | 89.78% | 41.12% | beta
## prestigeSum | riskBenefitSum | 0.22 | [ 0.05, 0.39] | 99.17%** | 9.05% | beta
## prestigeSum | ethicalQuestionsRiskSum | 0.28 | [ 0.11, 0.44] | 99.88%** | 2.08% | beta
## prestigeSum | financialQuestionsRiskSum | 0.25 | [ 0.09, 0.42] | 99.72%** | 4.10% | beta
## prestigeSum | healthAndSafetyQuestionsRiskSum | 0.28 | [ 0.10, 0.44] | 99.88%** | 2.53% | beta
## prestigeSum | recreationalQuestionsRiskSum | 0.17 | [-0.01, 0.34] | 96.85% | 22.00% | beta
## prestigeSum | socialQuestionsRiskSum | 0.27 | [ 0.09, 0.44] | 99.60%** | 3.20% | beta
## prestigeSum | ethicalQuestionsBenefitSum | 0.20 | [ 0.02, 0.37] | 98.42%* | 13.90% | beta
## prestigeSum | financialQuestionsBenefitSum | 0.20 | [ 0.02, 0.37] | 98.47%* | 14.67% | beta
## prestigeSum | healthAndSafetyQuestionsBenefitSum | 0.18 | [ 0.01, 0.37] | 97.55%* | 18.73% | beta
## prestigeSum | recreationalQuestionsBenefitSum | 0.12 | [-0.05, 0.30] | 91.07% | 39.95% | beta
## prestigeSum | socialQuestionsBenefitSum | 0.27 | [ 0.10, 0.44] | 99.75%** | 3.08% | beta
## prestigeSum | ethicalQuestionsPerceptionSum | 0.11 | [-0.07, 0.29] | 89.05% | 43.12% | beta
## prestigeSum | financialQuestionsPerceptionSum | 0.10 | [-0.08, 0.28] | 86.90% | 47.77% | beta
## prestigeSum | healthAndSafetyQuestionsPerceptionSum | 0.10 | [-0.07, 0.28] | 86.80% | 47.27% | beta
## prestigeSum | recreationalQuestionsPerceptionSum | 0.13 | [-0.04, 0.31] | 92.77% | 35.25% | beta
## prestigeSum | socialQuestionsPerceptionSum | 0.06 | [-0.14, 0.22] | 72.72% | 63.20% | beta
## prestigeSum | ethicalPreference | 0.18 | [ 0.00, 0.35] | 97.20%* | 19.07% | beta
## prestigeSum | financialPreference | 0.21 | [ 0.03, 0.38] | 98.47%* | 12.28% | beta
## prestigeSum | socialPreference | 0.26 | [ 0.09, 0.42] | 99.72%** | 3.55% | beta
## prestigeSum | healthAndSafetyPreference | 0.16 | [-0.03, 0.33] | 95.55% | 24.18% | beta
## prestigeSum | recreationalPreference | 0.07 | [-0.10, 0.27] | 77.65% | 59.08% | beta
## prestigeSum | generalRiskPreference | 0.21 | [ 0.03, 0.38] | 98.88%* | 10.38% | beta
## leadershipSum | UMSSum | 0.29 | [ 0.13, 0.47] | 100%*** | 1.57% | beta
## leadershipSum | UMSIntimacySum | 0.17 | [-0.02, 0.34] | 96.17% | 21.95% | beta
## leadershipSum | UMSAffiliationSum | 0.31 | [ 0.13, 0.47] | 99.95%*** | 0.95% | beta
## leadershipSum | riskSum | 0.21 | [ 0.05, 0.39] | 99.00%* | 10.32% | beta
## leadershipSum | riskPerceptionSum | 4.96e-03 | [-0.17, 0.20] | 51.92% | 71.70% | beta
## leadershipSum | riskBenefitSum | 0.12 | [-0.05, 0.30] | 89.90% | 40.35% | beta
## leadershipSum | ethicalQuestionsRiskSum | 0.12 | [-0.06, 0.29] | 90.30% | 40.05% | beta
## leadershipSum | financialQuestionsRiskSum | 0.19 | [ 0.03, 0.37] | 97.95%* | 16.23% | beta
## leadershipSum | healthAndSafetyQuestionsRiskSum | 0.08 | [-0.11, 0.26] | 80.47% | 54.02% | beta
## leadershipSum | recreationalQuestionsRiskSum | 0.15 | [-0.03, 0.32] | 94.40% | 28.65% | beta
## leadershipSum | socialQuestionsRiskSum | 0.17 | [-0.01, 0.34] | 96.08% | 23.00% | beta
## leadershipSum | ethicalQuestionsBenefitSum | 0.14 | [-0.04, 0.32] | 93.50% | 31.52% | beta
## leadershipSum | financialQuestionsBenefitSum | 0.08 | [-0.10, 0.27] | 81.42% | 54.75% | beta
## leadershipSum | healthAndSafetyQuestionsBenefitSum | 0.08 | [-0.11, 0.25] | 79.67% | 56.47% | beta
## leadershipSum | recreationalQuestionsBenefitSum | 0.05 | [-0.13, 0.23] | 71.90% | 64.12% | beta
## leadershipSum | socialQuestionsBenefitSum | 0.16 | [-0.01, 0.35] | 96.08% | 23.95% | beta
## leadershipSum | ethicalQuestionsPerceptionSum | -0.01 | [-0.19, 0.17] | 56.40% | 72.50% | beta
## leadershipSum | financialQuestionsPerceptionSum | 0.07 | [-0.11, 0.25] | 75.83% | 59.85% | beta
## leadershipSum | healthAndSafetyQuestionsPerceptionSum | 0.04 | [-0.14, 0.23] | 68.33% | 65.20% | beta
## leadershipSum | recreationalQuestionsPerceptionSum | -0.03 | [-0.21, 0.17] | 62.90% | 67.40% | beta
## leadershipSum | socialQuestionsPerceptionSum | -0.05 | [-0.22, 0.13] | 71.43% | 63.62% | beta
## leadershipSum | ethicalPreference | 0.14 | [-0.04, 0.32] | 94.23% | 30.95% | beta
## leadershipSum | financialPreference | 0.09 | [-0.10, 0.27] | 82.12% | 52.83% | beta
## leadershipSum | socialPreference | 0.16 | [-0.02, 0.33] | 96.43% | 23.55% | beta
## leadershipSum | healthAndSafetyPreference | 0.07 | [-0.10, 0.26] | 78.05% | 59.48% | beta
## leadershipSum | recreationalPreference | 0.06 | [-0.12, 0.24] | 73.38% | 62.20% | beta
## leadershipSum | generalRiskPreference | 0.12 | [-0.05, 0.31] | 89.48% | 42.48% | beta
## UMSSum | UMSIntimacySum | 0.76 | [ 0.67, 0.83] | 100%*** | 0% | beta
## UMSSum | UMSAffiliationSum | 0.95 | [ 0.93, 0.96] | 100%*** | 0% | beta
## UMSSum | riskSum | 0.23 | [ 0.05, 0.39] | 99.28%** | 7.65% | beta
## UMSSum | riskPerceptionSum | 0.22 | [ 0.04, 0.38] | 99.15%** | 8.53% | beta
## UMSSum | riskBenefitSum | 0.17 | [-0.01, 0.35] | 95.95% | 23.20% | beta
## UMSSum | ethicalQuestionsRiskSum | 0.24 | [ 0.06, 0.40] | 99.70%** | 5.67% | beta
## UMSSum | financialQuestionsRiskSum | 0.23 | [ 0.05, 0.39] | 99.30%** | 9.35% | beta
## UMSSum | healthAndSafetyQuestionsRiskSum | 0.17 | [ 0.00, 0.35] | 96.47% | 22.95% | beta
## UMSSum | recreationalQuestionsRiskSum | 0.12 | [-0.06, 0.30] | 90.18% | 39.90% | beta
## UMSSum | socialQuestionsRiskSum | 0.18 | [-0.02, 0.35] | 96.92% | 18.43% | beta
## UMSSum | ethicalQuestionsBenefitSum | 0.09 | [-0.09, 0.27] | 84.70% | 50.52% | beta
## UMSSum | financialQuestionsBenefitSum | 0.20 | [ 0.03, 0.37] | 98.45%* | 13.95% | beta
## UMSSum | healthAndSafetyQuestionsBenefitSum | 0.17 | [-0.03, 0.33] | 95.93% | 23.90% | beta
## UMSSum | recreationalQuestionsBenefitSum | 0.07 | [-0.12, 0.24] | 76.70% | 58.53% | beta
## UMSSum | socialQuestionsBenefitSum | 0.18 | [ 0.01, 0.35] | 97.50%* | 19.02% | beta
## UMSSum | ethicalQuestionsPerceptionSum | 0.26 | [ 0.08, 0.42] | 99.90%** | 3.80% | beta
## UMSSum | financialQuestionsPerceptionSum | 0.22 | [ 0.05, 0.39] | 98.75%* | 9.75% | beta
## UMSSum | healthAndSafetyQuestionsPerceptionSum | 0.18 | [-0.02, 0.34] | 97.05%* | 20.47% | beta
## UMSSum | recreationalQuestionsPerceptionSum | 0.21 | [ 0.02, 0.37] | 99.22%** | 11.43% | beta
## UMSSum | socialQuestionsPerceptionSum | 0.10 | [-0.07, 0.29] | 86.55% | 47.62% | beta
## UMSSum | ethicalPreference | 0.05 | [-0.12, 0.23] | 71.67% | 65.38% | beta
## UMSSum | financialPreference | 0.22 | [ 0.04, 0.40] | 98.90%* | 9.80% | beta
## UMSSum | socialPreference | 0.17 | [ 0.00, 0.34] | 96.65% | 22.88% | beta
## UMSSum | healthAndSafetyPreference | 0.14 | [-0.03, 0.33] | 93.55% | 33.52% | beta
## UMSSum | recreationalPreference | -5.81e-04 | [-0.20, 0.17] | 50.30% | 71.20% | beta
## UMSSum | generalRiskPreference | 0.15 | [-0.04, 0.32] | 95.08% | 27.47% | beta
## UMSIntimacySum | UMSAffiliationSum | 0.53 | [ 0.38, 0.65] | 100%*** | 0% | beta
## UMSIntimacySum | riskSum | 0.07 | [-0.12, 0.25] | 76.53% | 60.22% | beta
## UMSIntimacySum | riskPerceptionSum | 0.25 | [ 0.07, 0.41] | 99.60%** | 5.75% | beta
## UMSIntimacySum | riskBenefitSum | 0.07 | [-0.11, 0.25] | 77.95% | 58.27% | beta
## UMSIntimacySum | ethicalQuestionsRiskSum | 0.07 | [-0.12, 0.25] | 76.68% | 59.23% | beta
## UMSIntimacySum | financialQuestionsRiskSum | 0.07 | [-0.10, 0.26] | 78.33% | 58.77% | beta
## UMSIntimacySum | healthAndSafetyQuestionsRiskSum | 0.07 | [-0.12, 0.24] | 75.67% | 59.30% | beta
## UMSIntimacySum | recreationalQuestionsRiskSum | -0.07 | [-0.25, 0.11] | 77.88% | 58.70% | beta
## UMSIntimacySum | socialQuestionsRiskSum | 0.11 | [-0.07, 0.29] | 88.22% | 44.35% | beta
## UMSIntimacySum | ethicalQuestionsBenefitSum | 8.16e-03 | [-0.17, 0.19] | 53.87% | 72.00% | beta
## UMSIntimacySum | financialQuestionsBenefitSum | 0.09 | [-0.09, 0.27] | 81.97% | 53.37% | beta
## UMSIntimacySum | healthAndSafetyQuestionsBenefitSum | 0.08 | [-0.09, 0.28] | 81.65% | 54.50% | beta
## UMSIntimacySum | recreationalQuestionsBenefitSum | 0.02 | [-0.15, 0.21] | 57.88% | 71.50% | beta
## UMSIntimacySum | socialQuestionsBenefitSum | 0.11 | [-0.06, 0.29] | 87.83% | 46.30% | beta
## UMSIntimacySum | ethicalQuestionsPerceptionSum | 0.31 | [ 0.14, 0.47] | 99.95%*** | 0.90% | beta
## UMSIntimacySum | financialQuestionsPerceptionSum | 0.22 | [ 0.04, 0.38] | 99.12%** | 9.12% | beta
## UMSIntimacySum | healthAndSafetyQuestionsPerceptionSum | 0.16 | [-0.02, 0.34] | 95.25% | 25.40% | beta
## UMSIntimacySum | recreationalQuestionsPerceptionSum | 0.27 | [ 0.08, 0.43] | 99.95%*** | 3.50% | beta
## UMSIntimacySum | socialQuestionsPerceptionSum | 0.12 | [-0.06, 0.30] | 88.92% | 41.62% | beta
## UMSIntimacySum | ethicalPreference | -0.04 | [-0.23, 0.14] | 66.72% | 67.05% | beta
## UMSIntimacySum | financialPreference | 0.11 | [-0.07, 0.29] | 86.90% | 45.65% | beta
## UMSIntimacySum | socialPreference | 0.09 | [-0.09, 0.27] | 84.67% | 50.25% | beta
## UMSIntimacySum | healthAndSafetyPreference | 0.06 | [-0.13, 0.24] | 74.55% | 61.70% | beta
## UMSIntimacySum | recreationalPreference | -0.07 | [-0.25, 0.11] | 76.05% | 60.80% | beta
## UMSIntimacySum | generalRiskPreference | 0.06 | [-0.12, 0.25] | 74.78% | 60.90% | beta
## UMSAffiliationSum | riskSum | 0.28 | [ 0.11, 0.45] | 99.88%** | 2.73% | beta
## UMSAffiliationSum | riskPerceptionSum | 0.18 | [ 0.00, 0.35] | 96.85% | 20.00% | beta
## UMSAffiliationSum | riskBenefitSum | 0.18 | [ 0.01, 0.36] | 97.42%* | 18.75% | beta
## UMSAffiliationSum | ethicalQuestionsRiskSum | 0.28 | [ 0.12, 0.45] | 99.92%*** | 1.93% | beta
## UMSAffiliationSum | financialQuestionsRiskSum | 0.26 | [ 0.07, 0.42] | 99.85%** | 3.95% | beta
## UMSAffiliationSum | healthAndSafetyQuestionsRiskSum | 0.20 | [ 0.02, 0.37] | 98.15%* | 15.05% | beta
## UMSAffiliationSum | recreationalQuestionsRiskSum | 0.20 | [ 0.02, 0.37] | 98.40%* | 14.17% | beta
## UMSAffiliationSum | socialQuestionsRiskSum | 0.19 | [ 0.01, 0.37] | 97.55%* | 17.70% | beta
## UMSAffiliationSum | ethicalQuestionsBenefitSum | 0.12 | [-0.05, 0.30] | 90.12% | 40.45% | beta
## UMSAffiliationSum | financialQuestionsBenefitSum | 0.22 | [ 0.05, 0.39] | 99.22%** | 9.20% | beta
## UMSAffiliationSum | healthAndSafetyQuestionsBenefitSum | 0.18 | [ 0.00, 0.34] | 97.38%* | 19.05% | beta
## UMSAffiliationSum | recreationalQuestionsBenefitSum | 0.08 | [-0.10, 0.26] | 80.20% | 55.80% | beta
## UMSAffiliationSum | socialQuestionsBenefitSum | 0.19 | [ 0.00, 0.35] | 97.47%* | 17.20% | beta
## UMSAffiliationSum | ethicalQuestionsPerceptionSum | 0.20 | [ 0.02, 0.37] | 98.55%* | 15.00% | beta
## UMSAffiliationSum | financialQuestionsPerceptionSum | 0.18 | [ 0.00, 0.35] | 97.32%* | 18.18% | beta
## UMSAffiliationSum | healthAndSafetyQuestionsPerceptionSum | 0.16 | [-0.03, 0.33] | 94.97% | 28.23% | beta
## UMSAffiliationSum | recreationalQuestionsPerceptionSum | 0.15 | [-0.02, 0.34] | 94.92% | 27.90% | beta
## UMSAffiliationSum | socialQuestionsPerceptionSum | 0.08 | [-0.10, 0.26] | 79.20% | 55.25% | beta
## UMSAffiliationSum | ethicalPreference | 0.09 | [-0.08, 0.28] | 82.53% | 51.10% | beta
## UMSAffiliationSum | financialPreference | 0.23 | [ 0.06, 0.41] | 99.42%** | 6.73% | beta
## UMSAffiliationSum | socialPreference | 0.18 | [ 0.01, 0.36] | 97.00% | 20.08% | beta
## UMSAffiliationSum | healthAndSafetyPreference | 0.15 | [-0.03, 0.32] | 94.70% | 28.60% | beta
## UMSAffiliationSum | recreationalPreference | 0.03 | [-0.14, 0.23] | 61.68% | 68.45% | beta
## UMSAffiliationSum | generalRiskPreference | 0.18 | [ 0.00, 0.35] | 97.15%* | 20.10% | beta
## riskSum | riskPerceptionSum | -0.18 | [-0.35, -0.01] | 97.32%* | 19.32% | beta
## riskSum | riskBenefitSum | 0.57 | [ 0.44, 0.69] | 100%*** | 0% | beta
## riskSum | ethicalQuestionsRiskSum | 0.83 | [ 0.77, 0.89] | 100%*** | 0% | beta
## riskSum | financialQuestionsRiskSum | 0.79 | [ 0.71, 0.85] | 100%*** | 0% | beta
## riskSum | healthAndSafetyQuestionsRiskSum | 0.80 | [ 0.73, 0.86] | 100%*** | 0% | beta
## riskSum | recreationalQuestionsRiskSum | 0.80 | [ 0.72, 0.86] | 100%*** | 0% | beta
## riskSum | socialQuestionsRiskSum | 0.53 | [ 0.39, 0.66] | 100%*** | 0% | beta
## riskSum | ethicalQuestionsBenefitSum | 0.53 | [ 0.40, 0.65] | 100%*** | 0% | beta
## riskSum | financialQuestionsBenefitSum | 0.45 | [ 0.31, 0.59] | 100%*** | 0% | beta
## riskSum | healthAndSafetyQuestionsBenefitSum | 0.52 | [ 0.36, 0.64] | 100%*** | 0% | beta
## riskSum | recreationalQuestionsBenefitSum | 0.48 | [ 0.33, 0.62] | 100%*** | 0% | beta
## riskSum | socialQuestionsBenefitSum | 0.53 | [ 0.40, 0.67] | 100%*** | 0% | beta
## riskSum | ethicalQuestionsPerceptionSum | -0.16 | [-0.33, 0.02] | 95.45% | 25.37% | beta
## riskSum | financialQuestionsPerceptionSum | -0.08 | [-0.25, 0.10] | 79.05% | 57.73% | beta
## riskSum | healthAndSafetyQuestionsPerceptionSum | -0.13 | [-0.31, 0.05] | 91.88% | 35.15% | beta
## riskSum | recreationalQuestionsPerceptionSum | -0.18 | [-0.35, 0.01] | 96.70% | 19.30% | beta
## riskSum | socialQuestionsPerceptionSum | -0.23 | [-0.39, -0.06] | 99.75%** | 7.45% | beta
## riskSum | ethicalPreference | 0.54 | [ 0.41, 0.67] | 100%*** | 0% | beta
## riskSum | financialPreference | 0.45 | [ 0.29, 0.58] | 100%*** | 0% | beta
## riskSum | socialPreference | 0.55 | [ 0.41, 0.66] | 100%*** | 0% | beta
## riskSum | healthAndSafetyPreference | 0.53 | [ 0.39, 0.65] | 100%*** | 0% | beta
## riskSum | recreationalPreference | 0.49 | [ 0.34, 0.62] | 100%*** | 0% | beta
## riskSum | generalRiskPreference | 0.57 | [ 0.46, 0.70] | 100%*** | 0% | beta
## riskPerceptionSum | riskBenefitSum | -0.05 | [-0.23, 0.13] | 69.97% | 64.83% | beta
## riskPerceptionSum | ethicalQuestionsRiskSum | -0.09 | [-0.27, 0.09] | 83.05% | 50.85% | beta
## riskPerceptionSum | financialQuestionsRiskSum | -0.07 | [-0.25, 0.12] | 78.53% | 57.98% | beta
## riskPerceptionSum | healthAndSafetyQuestionsRiskSum | -0.23 | [-0.40, -0.06] | 99.58%** | 7.67% | beta
## riskPerceptionSum | recreationalQuestionsRiskSum | -0.19 | [-0.35, 0.00] | 97.97%* | 17.60% | beta
## riskPerceptionSum | socialQuestionsRiskSum | 0.05 | [-0.14, 0.23] | 71.08% | 64.03% | beta
## riskPerceptionSum | ethicalQuestionsBenefitSum | -0.03 | [-0.22, 0.14] | 63.90% | 69.50% | beta
## riskPerceptionSum | financialQuestionsBenefitSum | -0.07 | [-0.24, 0.11] | 78.50% | 58.73% | beta
## riskPerceptionSum | healthAndSafetyQuestionsBenefitSum | -0.07 | [-0.26, 0.11] | 77.48% | 60.17% | beta
## riskPerceptionSum | recreationalQuestionsBenefitSum | -0.09 | [-0.29, 0.08] | 84.60% | 50.60% | beta
## riskPerceptionSum | socialQuestionsBenefitSum | 0.05 | [-0.12, 0.23] | 71.83% | 64.28% | beta
## riskPerceptionSum | ethicalQuestionsPerceptionSum | 0.88 | [ 0.83, 0.92] | 100%*** | 0% | beta
## riskPerceptionSum | financialQuestionsPerceptionSum | 0.83 | [ 0.76, 0.88] | 100%*** | 0% | beta
## riskPerceptionSum | healthAndSafetyQuestionsPerceptionSum | 0.88 | [ 0.84, 0.92] | 100%*** | 0% | beta
## riskPerceptionSum | recreationalQuestionsPerceptionSum | 0.86 | [ 0.81, 0.90] | 100%*** | 0% | beta
## riskPerceptionSum | socialQuestionsPerceptionSum | 0.81 | [ 0.74, 0.86] | 100%*** | 0% | beta
## riskPerceptionSum | ethicalPreference | -0.16 | [-0.33, 0.01] | 96.23% | 23.77% | beta
## riskPerceptionSum | financialPreference | -1.48e-03 | [-0.18, 0.19] | 50.50% | 70.30% | beta
## riskPerceptionSum | socialPreference | -0.02 | [-0.21, 0.16] | 59.48% | 69.55% | beta
## riskPerceptionSum | healthAndSafetyPreference | -0.19 | [-0.37, -0.01] | 97.45%* | 15.70% | beta
## riskPerceptionSum | recreationalPreference | -0.34 | [-0.50, -0.18] | 100%*** | 0.15% | beta
## riskPerceptionSum | generalRiskPreference | -0.10 | [-0.26, 0.09] | 84.03% | 50.20% | beta
## riskBenefitSum | ethicalQuestionsRiskSum | 0.46 | [ 0.31, 0.59] | 100%*** | 0% | beta
## riskBenefitSum | financialQuestionsRiskSum | 0.58 | [ 0.45, 0.69] | 100%*** | 0% | beta
## riskBenefitSum | healthAndSafetyQuestionsRiskSum | 0.50 | [ 0.36, 0.64] | 100%*** | 0% | beta
## riskBenefitSum | recreationalQuestionsRiskSum | 0.44 | [ 0.28, 0.57] | 100%*** | 0% | beta
## riskBenefitSum | socialQuestionsRiskSum | 0.84 | [ 0.78, 0.89] | 100%*** | 0% | beta
## riskBenefitSum | ethicalQuestionsBenefitSum | 0.89 | [ 0.84, 0.92] | 100%*** | 0% | beta
## riskBenefitSum | financialQuestionsBenefitSum | 0.88 | [ 0.83, 0.92] | 100%*** | 0% | beta
## riskBenefitSum | healthAndSafetyQuestionsBenefitSum | 0.87 | [ 0.83, 0.92] | 100%*** | 0% | beta
## riskBenefitSum | recreationalQuestionsBenefitSum | 0.88 | [ 0.83, 0.92] | 100%*** | 0% | beta
## riskBenefitSum | socialQuestionsBenefitSum | 0.84 | [ 0.78, 0.89] | 100%*** | 0% | beta
## riskBenefitSum | ethicalQuestionsPerceptionSum | -0.02 | [-0.21, 0.16] | 57.77% | 70.53% | beta
## riskBenefitSum | financialQuestionsPerceptionSum | 0.08 | [-0.10, 0.26] | 80.83% | 54.62% | beta
## riskBenefitSum | healthAndSafetyQuestionsPerceptionSum | -0.07 | [-0.24, 0.12] | 76.25% | 60.22% | beta
## riskBenefitSum | recreationalQuestionsPerceptionSum | -0.08 | [-0.26, 0.09] | 82.03% | 53.90% | beta
## riskBenefitSum | socialQuestionsPerceptionSum | -0.12 | [-0.29, 0.07] | 89.53% | 42.38% | beta
## riskBenefitSum | ethicalPreference | 0.87 | [ 0.82, 0.91] | 100%*** | 0% | beta
## riskBenefitSum | financialPreference | 0.88 | [ 0.83, 0.91] | 100%*** | 0% | beta
## riskBenefitSum | socialPreference | 0.84 | [ 0.78, 0.90] | 100%*** | 0% | beta
## riskBenefitSum | healthAndSafetyPreference | 0.86 | [ 0.80, 0.90] | 100%*** | 0% | beta
## riskBenefitSum | recreationalPreference | 0.81 | [ 0.75, 0.87] | 100%*** | 0% | beta
## riskBenefitSum | generalRiskPreference | 1.00 | [ 1.00, 1.00] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | financialQuestionsRiskSum | 0.60 | [ 0.48, 0.72] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | healthAndSafetyQuestionsRiskSum | 0.65 | [ 0.53, 0.75] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | recreationalQuestionsRiskSum | 0.61 | [ 0.49, 0.72] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | socialQuestionsRiskSum | 0.39 | [ 0.23, 0.54] | 100%*** | 0.05% | beta
## ethicalQuestionsRiskSum | ethicalQuestionsBenefitSum | 0.47 | [ 0.32, 0.61] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | financialQuestionsBenefitSum | 0.38 | [ 0.23, 0.53] | 100%*** | 0.07% | beta
## ethicalQuestionsRiskSum | healthAndSafetyQuestionsBenefitSum | 0.40 | [ 0.23, 0.54] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | recreationalQuestionsBenefitSum | 0.40 | [ 0.23, 0.54] | 100%*** | 0.10% | beta
## ethicalQuestionsRiskSum | socialQuestionsBenefitSum | 0.39 | [ 0.24, 0.55] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | ethicalQuestionsPerceptionSum | -0.14 | [-0.31, 0.04] | 92.70% | 34.02% | beta
## ethicalQuestionsRiskSum | financialQuestionsPerceptionSum | 0.01 | [-0.17, 0.19] | 55.17% | 70.95% | beta
## ethicalQuestionsRiskSum | healthAndSafetyQuestionsPerceptionSum | -0.05 | [-0.24, 0.13] | 72.47% | 63.40% | beta
## ethicalQuestionsRiskSum | recreationalQuestionsPerceptionSum | -0.12 | [-0.30, 0.06] | 89.58% | 40.62% | beta
## ethicalQuestionsRiskSum | socialQuestionsPerceptionSum | -0.07 | [-0.24, 0.11] | 78.92% | 57.93% | beta
## ethicalQuestionsRiskSum | ethicalPreference | 0.48 | [ 0.32, 0.61] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | financialPreference | 0.38 | [ 0.22, 0.52] | 100%*** | 0.07% | beta
## ethicalQuestionsRiskSum | socialPreference | 0.39 | [ 0.24, 0.53] | 100%*** | 0.03% | beta
## ethicalQuestionsRiskSum | healthAndSafetyPreference | 0.40 | [ 0.24, 0.54] | 100%*** | 0.03% | beta
## ethicalQuestionsRiskSum | recreationalPreference | 0.39 | [ 0.24, 0.55] | 100%*** | 0% | beta
## ethicalQuestionsRiskSum | generalRiskPreference | 0.46 | [ 0.31, 0.60] | 100%*** | 0% | beta
## financialQuestionsRiskSum | healthAndSafetyQuestionsRiskSum | 0.54 | [ 0.41, 0.67] | 100%*** | 0% | beta
## financialQuestionsRiskSum | recreationalQuestionsRiskSum | 0.56 | [ 0.43, 0.68] | 100%*** | 0% | beta
## financialQuestionsRiskSum | socialQuestionsRiskSum | 0.54 | [ 0.40, 0.66] | 100%*** | 0% | beta
## financialQuestionsRiskSum | ethicalQuestionsBenefitSum | 0.57 | [ 0.44, 0.69] | 100%*** | 0% | beta
## financialQuestionsRiskSum | financialQuestionsBenefitSum | 0.50 | [ 0.36, 0.64] | 100%*** | 0% | beta
## financialQuestionsRiskSum | healthAndSafetyQuestionsBenefitSum | 0.49 | [ 0.34, 0.62] | 100%*** | 0% | beta
## financialQuestionsRiskSum | recreationalQuestionsBenefitSum | 0.47 | [ 0.31, 0.60] | 100%*** | 0% | beta
## financialQuestionsRiskSum | socialQuestionsBenefitSum | 0.54 | [ 0.41, 0.67] | 100%*** | 0% | beta
## financialQuestionsRiskSum | ethicalQuestionsPerceptionSum | -0.05 | [-0.24, 0.12] | 72.40% | 64.35% | beta
## financialQuestionsRiskSum | financialQuestionsPerceptionSum | -0.09 | [-0.27, 0.09] | 83.45% | 50.25% | beta
## financialQuestionsRiskSum | healthAndSafetyQuestionsPerceptionSum | -0.01 | [-0.20, 0.17] | 56.03% | 71.62% | beta
## financialQuestionsRiskSum | recreationalQuestionsPerceptionSum | -0.08 | [-0.26, 0.11] | 78.03% | 56.12% | beta
## financialQuestionsRiskSum | socialQuestionsPerceptionSum | -0.10 | [-0.28, 0.09] | 85.72% | 46.60% | beta
## financialQuestionsRiskSum | ethicalPreference | 0.56 | [ 0.43, 0.68] | 100%*** | 0% | beta
## financialQuestionsRiskSum | financialPreference | 0.49 | [ 0.34, 0.61] | 100%*** | 0% | beta
## financialQuestionsRiskSum | socialPreference | 0.54 | [ 0.39, 0.66] | 100%*** | 0% | beta
## financialQuestionsRiskSum | healthAndSafetyPreference | 0.47 | [ 0.33, 0.61] | 100%*** | 0% | beta
## financialQuestionsRiskSum | recreationalPreference | 0.44 | [ 0.28, 0.58] | 100%*** | 0% | beta
## financialQuestionsRiskSum | generalRiskPreference | 0.58 | [ 0.44, 0.70] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | recreationalQuestionsRiskSum | 0.57 | [ 0.44, 0.69] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | socialQuestionsRiskSum | 0.40 | [ 0.25, 0.55] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | ethicalQuestionsBenefitSum | 0.44 | [ 0.29, 0.59] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | financialQuestionsBenefitSum | 0.44 | [ 0.28, 0.58] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | healthAndSafetyQuestionsBenefitSum | 0.55 | [ 0.42, 0.68] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | recreationalQuestionsBenefitSum | 0.40 | [ 0.24, 0.54] | 100%*** | 0.05% | beta
## healthAndSafetyQuestionsRiskSum | socialQuestionsBenefitSum | 0.40 | [ 0.25, 0.55] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | ethicalQuestionsPerceptionSum | -0.21 | [-0.38, -0.03] | 98.92%* | 11.43% | beta
## healthAndSafetyQuestionsRiskSum | financialQuestionsPerceptionSum | -0.08 | [-0.26, 0.10] | 79.97% | 56.35% | beta
## healthAndSafetyQuestionsRiskSum | healthAndSafetyQuestionsPerceptionSum | -0.25 | [-0.41, -0.07] | 99.42%** | 5.62% | beta
## healthAndSafetyQuestionsRiskSum | recreationalQuestionsPerceptionSum | -0.16 | [-0.34, 0.01] | 95.93% | 24.60% | beta
## healthAndSafetyQuestionsRiskSum | socialQuestionsPerceptionSum | -0.27 | [-0.42, -0.10] | 99.83%** | 2.38% | beta
## healthAndSafetyQuestionsRiskSum | ethicalPreference | 0.47 | [ 0.32, 0.61] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | financialPreference | 0.42 | [ 0.27, 0.57] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | socialPreference | 0.42 | [ 0.26, 0.56] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | healthAndSafetyPreference | 0.57 | [ 0.44, 0.70] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | recreationalPreference | 0.41 | [ 0.26, 0.55] | 100%*** | 0% | beta
## healthAndSafetyQuestionsRiskSum | generalRiskPreference | 0.51 | [ 0.37, 0.63] | 100%*** | 0% | beta
## recreationalQuestionsRiskSum | socialQuestionsRiskSum | 0.37 | [ 0.21, 0.54] | 100%*** | 0.25% | beta
## recreationalQuestionsRiskSum | ethicalQuestionsBenefitSum | 0.41 | [ 0.24, 0.55] | 100%*** | 0% | beta
## recreationalQuestionsRiskSum | financialQuestionsBenefitSum | 0.31 | [ 0.16, 0.48] | 100%*** | 0.65% | beta
## recreationalQuestionsRiskSum | healthAndSafetyQuestionsBenefitSum | 0.42 | [ 0.26, 0.56] | 100%*** | 0.03% | beta
## recreationalQuestionsRiskSum | recreationalQuestionsBenefitSum | 0.45 | [ 0.28, 0.58] | 100%*** | 0% | beta
## recreationalQuestionsRiskSum | socialQuestionsBenefitSum | 0.37 | [ 0.20, 0.52] | 100%*** | 0.18% | beta
## recreationalQuestionsRiskSum | ethicalQuestionsPerceptionSum | -0.17 | [-0.35, 0.01] | 96.58% | 22.85% | beta
## recreationalQuestionsRiskSum | financialQuestionsPerceptionSum | -0.14 | [-0.32, 0.04] | 93.27% | 30.88% | beta
## recreationalQuestionsRiskSum | healthAndSafetyQuestionsPerceptionSum | -0.12 | [-0.29, 0.06] | 89.60% | 40.38% | beta
## recreationalQuestionsRiskSum | recreationalQuestionsPerceptionSum | -0.26 | [-0.43, -0.10] | 99.80%** | 3.02% | beta
## recreationalQuestionsRiskSum | socialQuestionsPerceptionSum | -0.11 | [-0.30, 0.06] | 87.62% | 43.48% | beta
## recreationalQuestionsRiskSum | ethicalPreference | 0.42 | [ 0.27, 0.57] | 100%*** | 0% | beta
## recreationalQuestionsRiskSum | financialPreference | 0.30 | [ 0.13, 0.46] | 100%*** | 1.77% | beta
## recreationalQuestionsRiskSum | socialPreference | 0.37 | [ 0.22, 0.52] | 100%*** | 0.10% | beta
## recreationalQuestionsRiskSum | healthAndSafetyPreference | 0.42 | [ 0.27, 0.56] | 100%*** | 0% | beta
## recreationalQuestionsRiskSum | recreationalPreference | 0.49 | [ 0.34, 0.62] | 100%*** | 0% | beta
## recreationalQuestionsRiskSum | generalRiskPreference | 0.45 | [ 0.30, 0.59] | 100%*** | 0% | beta
## socialQuestionsRiskSum | ethicalQuestionsBenefitSum | 0.68 | [ 0.57, 0.77] | 100%*** | 0% | beta
## socialQuestionsRiskSum | financialQuestionsBenefitSum | 0.66 | [ 0.54, 0.75] | 100%*** | 0% | beta
## socialQuestionsRiskSum | healthAndSafetyQuestionsBenefitSum | 0.70 | [ 0.60, 0.78] | 100%*** | 0% | beta
## socialQuestionsRiskSum | recreationalQuestionsBenefitSum | 0.65 | [ 0.55, 0.76] | 100%*** | 0% | beta
## socialQuestionsRiskSum | socialQuestionsBenefitSum | 1.00 | [ 1.00, 1.00] | 100%*** | 0% | beta
## socialQuestionsRiskSum | ethicalQuestionsPerceptionSum | 0.09 | [-0.08, 0.28] | 84.08% | 51.10% | beta
## socialQuestionsRiskSum | financialQuestionsPerceptionSum | 0.17 | [-0.01, 0.34] | 96.90% | 22.07% | beta
## socialQuestionsRiskSum | healthAndSafetyQuestionsPerceptionSum | 0.04 | [-0.14, 0.21] | 64.85% | 68.45% | beta
## socialQuestionsRiskSum | recreationalQuestionsPerceptionSum | 0.05 | [-0.13, 0.23] | 69.12% | 67.25% | beta
## socialQuestionsRiskSum | socialQuestionsPerceptionSum | -0.11 | [-0.29, 0.07] | 87.95% | 43.65% | beta
## socialQuestionsRiskSum | ethicalPreference | 0.65 | [ 0.53, 0.75] | 100%*** | 0% | beta
## socialQuestionsRiskSum | financialPreference | 0.67 | [ 0.56, 0.77] | 100%*** | 0% | beta
## socialQuestionsRiskSum | socialPreference | 1.00 | [ 0.99, 1.00] | 100%*** | 0% | beta
## socialQuestionsRiskSum | healthAndSafetyPreference | 0.67 | [ 0.56, 0.77] | 100%*** | 0% | beta
## socialQuestionsRiskSum | recreationalPreference | 0.58 | [ 0.44, 0.69] | 100%*** | 0% | beta
## socialQuestionsRiskSum | generalRiskPreference | 0.84 | [ 0.78, 0.89] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | financialQuestionsBenefitSum | 0.71 | [ 0.61, 0.79] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | healthAndSafetyQuestionsBenefitSum | 0.77 | [ 0.69, 0.84] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | recreationalQuestionsBenefitSum | 0.77 | [ 0.69, 0.84] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | socialQuestionsBenefitSum | 0.68 | [ 0.58, 0.77] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | ethicalQuestionsPerceptionSum | -0.08 | [-0.27, 0.09] | 81.42% | 54.33% | beta
## ethicalQuestionsBenefitSum | financialQuestionsPerceptionSum | 0.09 | [-0.09, 0.27] | 82.47% | 53.47% | beta
## ethicalQuestionsBenefitSum | healthAndSafetyQuestionsPerceptionSum | -0.03 | [-0.21, 0.15] | 62.88% | 68.80% | beta
## ethicalQuestionsBenefitSum | recreationalQuestionsPerceptionSum | -0.08 | [-0.26, 0.10] | 80.88% | 53.42% | beta
## ethicalQuestionsBenefitSum | socialQuestionsPerceptionSum | -0.03 | [-0.22, 0.15] | 62.95% | 67.58% | beta
## ethicalQuestionsBenefitSum | ethicalPreference | 0.99 | [ 0.98, 0.99] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | financialPreference | 0.71 | [ 0.62, 0.80] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | socialPreference | 0.67 | [ 0.57, 0.77] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | healthAndSafetyPreference | 0.75 | [ 0.66, 0.83] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | recreationalPreference | 0.72 | [ 0.63, 0.80] | 100%*** | 0% | beta
## ethicalQuestionsBenefitSum | generalRiskPreference | 0.89 | [ 0.85, 0.93] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | healthAndSafetyQuestionsBenefitSum | 0.70 | [ 0.60, 0.78] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | recreationalQuestionsBenefitSum | 0.75 | [ 0.67, 0.83] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | socialQuestionsBenefitSum | 0.66 | [ 0.55, 0.76] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | ethicalQuestionsPerceptionSum | 6.94e-03 | [-0.18, 0.19] | 53.35% | 70.40% | beta
## financialQuestionsBenefitSum | financialQuestionsPerceptionSum | 0.04 | [-0.16, 0.22] | 65.35% | 66.20% | beta
## financialQuestionsBenefitSum | healthAndSafetyQuestionsPerceptionSum | -0.12 | [-0.29, 0.06] | 89.72% | 41.45% | beta
## financialQuestionsBenefitSum | recreationalQuestionsPerceptionSum | -0.08 | [-0.27, 0.10] | 81.17% | 54.67% | beta
## financialQuestionsBenefitSum | socialQuestionsPerceptionSum | -0.15 | [-0.31, 0.04] | 94.27% | 31.85% | beta
## financialQuestionsBenefitSum | ethicalPreference | 0.69 | [ 0.58, 0.78] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | financialPreference | 1.00 | [ 0.99, 1.00] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | socialPreference | 0.66 | [ 0.54, 0.76] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | healthAndSafetyPreference | 0.69 | [ 0.59, 0.78] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | recreationalPreference | 0.70 | [ 0.60, 0.79] | 100%*** | 0% | beta
## financialQuestionsBenefitSum | generalRiskPreference | 0.88 | [ 0.83, 0.92] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | recreationalQuestionsBenefitSum | 0.69 | [ 0.59, 0.78] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | socialQuestionsBenefitSum | 0.70 | [ 0.61, 0.78] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | ethicalQuestionsPerceptionSum | -0.04 | [-0.22, 0.15] | 64.10% | 67.03% | beta
## healthAndSafetyQuestionsBenefitSum | financialQuestionsPerceptionSum | 0.06 | [-0.11, 0.25] | 75.17% | 60.48% | beta
## healthAndSafetyQuestionsBenefitSum | healthAndSafetyQuestionsPerceptionSum | -0.09 | [-0.27, 0.09] | 83.60% | 51.45% | beta
## healthAndSafetyQuestionsBenefitSum | recreationalQuestionsPerceptionSum | -0.11 | [-0.28, 0.09] | 87.70% | 45.40% | beta
## healthAndSafetyQuestionsBenefitSum | socialQuestionsPerceptionSum | -0.12 | [-0.30, 0.06] | 90.18% | 40.23% | beta
## healthAndSafetyQuestionsBenefitSum | ethicalPreference | 0.75 | [ 0.67, 0.83] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | financialPreference | 0.70 | [ 0.60, 0.79] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | socialPreference | 0.70 | [ 0.59, 0.80] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | healthAndSafetyPreference | 0.99 | [ 0.98, 0.99] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | recreationalPreference | 0.65 | [ 0.54, 0.75] | 100%*** | 0% | beta
## healthAndSafetyQuestionsBenefitSum | generalRiskPreference | 0.87 | [ 0.82, 0.92] | 100%*** | 0% | beta
## recreationalQuestionsBenefitSum | socialQuestionsBenefitSum | 0.66 | [ 0.54, 0.75] | 100%*** | 0% | beta
## recreationalQuestionsBenefitSum | ethicalQuestionsPerceptionSum | -0.08 | [-0.25, 0.11] | 79.10% | 58.63% | beta
## recreationalQuestionsBenefitSum | financialQuestionsPerceptionSum | 3.96e-03 | [-0.18, 0.19] | 51.95% | 71.50% | beta
## recreationalQuestionsBenefitSum | healthAndSafetyQuestionsPerceptionSum | -0.09 | [-0.26, 0.10] | 82.47% | 53.33% | beta
## recreationalQuestionsBenefitSum | recreationalQuestionsPerceptionSum | -0.15 | [-0.32, 0.02] | 95.80% | 26.30% | beta
## recreationalQuestionsBenefitSum | socialQuestionsPerceptionSum | -0.10 | [-0.27, 0.09] | 86.08% | 48.15% | beta
## recreationalQuestionsBenefitSum | ethicalPreference | 0.76 | [ 0.67, 0.83] | 100%*** | 0% | beta
## recreationalQuestionsBenefitSum | financialPreference | 0.74 | [ 0.66, 0.82] | 100%*** | 0% | beta
## recreationalQuestionsBenefitSum | socialPreference | 0.66 | [ 0.54, 0.76] | 100%*** | 0% | beta
## recreationalQuestionsBenefitSum | healthAndSafetyPreference | 0.69 | [ 0.59, 0.78] | 100%*** | 0% | beta
## recreationalQuestionsBenefitSum | recreationalPreference | 0.95 | [ 0.93, 0.97] | 100%*** | 0% | beta
## recreationalQuestionsBenefitSum | generalRiskPreference | 0.88 | [ 0.84, 0.92] | 100%*** | 0% | beta
## socialQuestionsBenefitSum | ethicalQuestionsPerceptionSum | 0.09 | [-0.10, 0.26] | 84.00% | 50.55% | beta
## socialQuestionsBenefitSum | financialQuestionsPerceptionSum | 0.17 | [ 0.00, 0.37] | 96.75% | 20.32% | beta
## socialQuestionsBenefitSum | healthAndSafetyQuestionsPerceptionSum | 0.04 | [-0.15, 0.21] | 65.22% | 68.58% | beta
## socialQuestionsBenefitSum | recreationalQuestionsPerceptionSum | 0.04 | [-0.13, 0.23] | 67.95% | 66.35% | beta
## socialQuestionsBenefitSum | socialQuestionsPerceptionSum | -0.11 | [-0.29, 0.07] | 88.02% | 43.62% | beta
## socialQuestionsBenefitSum | ethicalPreference | 0.65 | [ 0.54, 0.74] | 100%*** | 0% | beta
## socialQuestionsBenefitSum | financialPreference | 0.67 | [ 0.55, 0.77] | 100%*** | 0% | beta
## socialQuestionsBenefitSum | socialPreference | 1.00 | [ 0.99, 1.00] | 100%*** | 0% | beta
## socialQuestionsBenefitSum | healthAndSafetyPreference | 0.67 | [ 0.57, 0.77] | 100%*** | 0% | beta
## socialQuestionsBenefitSum | recreationalPreference | 0.57 | [ 0.44, 0.69] | 100%*** | 0% | beta
## socialQuestionsBenefitSum | generalRiskPreference | 0.84 | [ 0.77, 0.88] | 100%*** | 0% | beta
## ethicalQuestionsPerceptionSum | financialQuestionsPerceptionSum | 0.67 | [ 0.56, 0.76] | 100%*** | 0% | beta
## ethicalQuestionsPerceptionSum | healthAndSafetyQuestionsPerceptionSum | 0.73 | [ 0.64, 0.81] | 100%*** | 0% | beta
## ethicalQuestionsPerceptionSum | recreationalQuestionsPerceptionSum | 0.74 | [ 0.64, 0.81] | 100%*** | 0% | beta
## ethicalQuestionsPerceptionSum | socialQuestionsPerceptionSum | 0.62 | [ 0.49, 0.72] | 100%*** | 0% | beta
## ethicalQuestionsPerceptionSum | ethicalPreference | -0.23 | [-0.40, -0.05] | 98.98%* | 8.35% | beta
## ethicalQuestionsPerceptionSum | financialPreference | 0.06 | [-0.11, 0.25] | 75.62% | 61.22% | beta
## ethicalQuestionsPerceptionSum | socialPreference | 0.04 | [-0.13, 0.23] | 65.03% | 67.83% | beta
## ethicalQuestionsPerceptionSum | healthAndSafetyPreference | -0.14 | [-0.32, 0.03] | 93.92% | 34.20% | beta
## ethicalQuestionsPerceptionSum | recreationalPreference | -0.29 | [-0.46, -0.12] | 99.83%** | 1.73% | beta
## ethicalQuestionsPerceptionSum | generalRiskPreference | -0.06 | [-0.24, 0.12] | 73.70% | 63.73% | beta
## financialQuestionsPerceptionSum | healthAndSafetyQuestionsPerceptionSum | 0.69 | [ 0.58, 0.77] | 100%*** | 0% | beta
## financialQuestionsPerceptionSum | recreationalQuestionsPerceptionSum | 0.66 | [ 0.55, 0.76] | 100%*** | 0% | beta
## financialQuestionsPerceptionSum | socialQuestionsPerceptionSum | 0.57 | [ 0.43, 0.69] | 100%*** | 0% | beta
## financialQuestionsPerceptionSum | ethicalPreference | -0.01 | [-0.18, 0.18] | 55.20% | 71.28% | beta
## financialQuestionsPerceptionSum | financialPreference | 0.12 | [-0.07, 0.29] | 89.45% | 40.85% | beta
## financialQuestionsPerceptionSum | socialPreference | 0.12 | [-0.04, 0.31] | 90.65% | 39.12% | beta
## financialQuestionsPerceptionSum | healthAndSafetyPreference | -0.04 | [-0.22, 0.15] | 66.00% | 67.60% | beta
## financialQuestionsPerceptionSum | recreationalPreference | -0.20 | [-0.38, -0.02] | 98.42%* | 14.03% | beta
## financialQuestionsPerceptionSum | generalRiskPreference | 0.04 | [-0.14, 0.22] | 69.45% | 65.80% | beta
## healthAndSafetyQuestionsPerceptionSum | recreationalQuestionsPerceptionSum | 0.68 | [ 0.56, 0.76] | 100%*** | 0% | beta
## healthAndSafetyQuestionsPerceptionSum | socialQuestionsPerceptionSum | 0.69 | [ 0.59, 0.78] | 100%*** | 0% | beta
## healthAndSafetyQuestionsPerceptionSum | ethicalPreference | -0.14 | [-0.31, 0.04] | 93.33% | 33.05% | beta
## healthAndSafetyQuestionsPerceptionSum | financialPreference | -0.06 | [-0.23, 0.13] | 72.40% | 62.75% | beta
## healthAndSafetyQuestionsPerceptionSum | socialPreference | -0.02 | [-0.22, 0.15] | 59.05% | 70.88% | beta
## healthAndSafetyQuestionsPerceptionSum | healthAndSafetyPreference | -0.23 | [-0.42, -0.07] | 99.50%** | 7.85% | beta
## healthAndSafetyQuestionsPerceptionSum | recreationalPreference | -0.28 | [-0.44, -0.12] | 100%*** | 1.88% | beta
## healthAndSafetyQuestionsPerceptionSum | generalRiskPreference | -0.10 | [-0.29, 0.07] | 87.17% | 46.83% | beta
## recreationalQuestionsPerceptionSum | socialQuestionsPerceptionSum | 0.59 | [ 0.46, 0.69] | 100%*** | 0% | beta
## recreationalQuestionsPerceptionSum | ethicalPreference | -0.19 | [-0.37, -0.02] | 98.15%* | 15.62% | beta
## recreationalQuestionsPerceptionSum | financialPreference | -0.03 | [-0.22, 0.15] | 60.77% | 69.38% | beta
## recreationalQuestionsPerceptionSum | socialPreference | -7.90e-03 | [-0.20, 0.16] | 53.08% | 70.83% | beta
## recreationalQuestionsPerceptionSum | healthAndSafetyPreference | -0.20 | [-0.38, -0.03] | 98.52%* | 13.63% | beta
## recreationalQuestionsPerceptionSum | recreationalPreference | -0.43 | [-0.57, -0.29] | 100%*** | 0% | beta
## recreationalQuestionsPerceptionSum | generalRiskPreference | -0.12 | [-0.29, 0.06] | 91.22% | 39.12% | beta
## socialQuestionsPerceptionSum | ethicalPreference | -0.12 | [-0.30, 0.06] | 90.80% | 38.42% | beta
## socialQuestionsPerceptionSum | financialPreference | -0.09 | [-0.27, 0.08] | 84.50% | 52.05% | beta
## socialQuestionsPerceptionSum | socialPreference | -0.20 | [-0.36, -0.01] | 98.20%* | 14.65% | beta
## socialQuestionsPerceptionSum | healthAndSafetyPreference | -0.21 | [-0.37, -0.02] | 99.02%** | 11.43% | beta
## socialQuestionsPerceptionSum | recreationalPreference | -0.27 | [-0.43, -0.08] | 99.80%** | 3.52% | beta
## socialQuestionsPerceptionSum | generalRiskPreference | -0.15 | [-0.32, 0.04] | 94.47% | 29.12% | beta
## ethicalPreference | financialPreference | 0.68 | [ 0.58, 0.77] | 100%*** | 0% | beta
## ethicalPreference | socialPreference | 0.65 | [ 0.53, 0.74] | 100%*** | 0% | beta
## ethicalPreference | healthAndSafetyPreference | 0.76 | [ 0.67, 0.83] | 100%*** | 0% | beta
## ethicalPreference | recreationalPreference | 0.75 | [ 0.66, 0.82] | 100%*** | 0% | beta
## ethicalPreference | generalRiskPreference | 0.87 | [ 0.83, 0.92] | 100%*** | 0% | beta
## financialPreference | socialPreference | 0.67 | [ 0.55, 0.75] | 100%*** | 0% | beta
## financialPreference | healthAndSafetyPreference | 0.69 | [ 0.59, 0.78] | 100%*** | 0% | beta
## financialPreference | recreationalPreference | 0.68 | [ 0.57, 0.77] | 100%*** | 0% | beta
## financialPreference | generalRiskPreference | 0.87 | [ 0.83, 0.91] | 100%*** | 0% | beta
## socialPreference | healthAndSafetyPreference | 0.68 | [ 0.58, 0.77] | 100%*** | 0% | beta
## socialPreference | recreationalPreference | 0.59 | [ 0.47, 0.71] | 100%*** | 0% | beta
## socialPreference | generalRiskPreference | 0.84 | [ 0.78, 0.89] | 100%*** | 0% | beta
## healthAndSafetyPreference | recreationalPreference | 0.68 | [ 0.58, 0.78] | 100%*** | 0% | beta
## healthAndSafetyPreference | generalRiskPreference | 0.87 | [ 0.82, 0.91] | 100%*** | 0% | beta
## recreationalPreference | generalRiskPreference | 0.83 | [ 0.77, 0.88] | 100%*** | 0% | betam1 <- brm(generalRiskPreference ~ dominanceSum + prestigeSum + leadershipSum + Gender + Age,
data = experiment_dataset_analysis_scaled, backend = "cmdstanr", cores = parallel::detectCores(), warmup = 500, iter = 40000,
prior = c(
prior(normal(0, 1), class = "Intercept"),
prior(normal(3, 1), class = "b", coef = "dominanceSum"),
prior(normal(0, 1), class = "b", coef = "prestigeSum"),
prior(normal(-2, 1), class = "b", coef = "leadershipSum"),
prior(normal(-3, 1), class = "b", coef = "Gender1"),
prior(normal(-3, 1), class = "b", coef = "Age")
),
save_pars = save_pars(all = T)
)
summary(m1)
# write.csv(round(fixef(m1), 2), "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/DoPL/Experiments/DoPL_Experiment_Two/Priors/fixef_m1.csv")summary(m1)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: generalRiskPreference ~ dominanceSum + prestigeSum + leadershipSum + Gender + Age
## Data: experiment_dataset_analysis_scaled (Number of observations: 107)
## Draws: 4 chains, each with iter = 40000; warmup = 500; thin = 1;
## total post-warmup draws = 158000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 0.28 0.13 0.02 0.54 1.00 234785 121646
## dominanceSum 0.22 0.10 0.02 0.43 1.00 220215 127350
## prestigeSum 0.14 0.10 -0.07 0.34 1.00 204482 127295
## leadershipSum -0.02 0.10 -0.22 0.18 1.00 235088 130581
## Gender1 -0.58 0.19 -0.96 -0.21 1.00 233182 123674
## Age 0.08 0.10 -0.11 0.27 1.00 229878 125014
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.96 0.07 0.84 1.11 1.00 231138 123050
##
## 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 + interaction between gender and each of the DoPL motives
m1_int <- brm(generalRiskPreference ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age,
data = experiment_dataset_analysis_scaled, backend = "cmdstanr", cores = parallel::detectCores(), warmup = 500, iter = 8000,
prior = c(
prior(normal(0, 1), class = "Intercept"),
prior(normal(3, 1), class = "b", coef = "dominanceSum"),
prior(normal(0, 1), class = "b", coef = "prestigeSum"),
prior(normal(-2, 1), class = "b", coef = "leadershipSum"),
prior(normal(-3, 1), class = "b", coef = "Gender1"),
prior(normal(-3, 1), class = "b", coef = "Age"),
prior(normal(0, 1), class = "b", coef = "dominanceSum:Gender1"),
prior(normal(0, 1), class = "b", coef = "Gender1:leadershipSum"),
prior(normal(0, 1), class = "b", coef = "Gender1:prestigeSum")
),
save_pars = save_pars(all = T)
)
# write.csv(round(fixef(m1_int), 2), "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/DoPL/Experiments/DoPL_Experiment_Two/Priors/fixef_m1_int.csv")summary(m1_int)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: generalRiskPreference ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## Data: experiment_dataset_analysis_scaled (Number of observations: 107)
## Draws: 4 chains, each with iter = 8000; warmup = 500; thin = 1;
## total post-warmup draws = 30000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 0.26 0.13 0.00 0.52 1.00 31271
## dominanceSum 0.37 0.16 0.05 0.69 1.00 20706
## Gender1 -0.58 0.19 -0.96 -0.20 1.00 32425
## prestigeSum 0.01 0.15 -0.29 0.31 1.00 21486
## leadershipSum 0.01 0.15 -0.29 0.29 1.00 22893
## Age 0.09 0.10 -0.10 0.28 1.00 37083
## dominanceSum:Gender1 -0.24 0.21 -0.65 0.17 1.00 20953
## Gender1:prestigeSum 0.22 0.21 -0.18 0.63 1.00 21756
## Gender1:leadershipSum -0.05 0.20 -0.44 0.35 1.00 23251
## Tail_ESS
## Intercept 21678
## dominanceSum 21985
## Gender1 22302
## prestigeSum 21890
## leadershipSum 22361
## Age 24758
## dominanceSum:Gender1 21076
## Gender1:prestigeSum 22396
## Gender1:leadershipSum 23307
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.97 0.07 0.84 1.12 1.00 29394 23156
##
## 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).model comparisons
rstan::loo(m1, m1_int) # equal
bfs_gen <- bayesfactor_models(m1, m1_int, denominator = 2) # equalbfs_gen## Bayes Factors for Model Comparison
##
## Model BF
## [1] dominanceSum + prestigeSum + leadershipSum + Gender + Age 51.12
##
## * Against Denominator: [2] dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## * Bayes Factor Type: marginal likelihoods (bridgesampling)# HDI m1 + DOPL:gender interactions
m1_hdi <- hdi(m1, effects = "fixed", component = "conditional", ci = .95)## Warning: Identical densities found along different segments of the distribution,
## choosing rightmost.m1_hdi[
sign(m1_hdi$CI_low) == sign(m1_hdi$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
]## Highest Density Interval
##
## Parameter | 95% HDI
## -------------------------------
## b_Intercept | [ 0.02, 0.54]
## b_dominanceSum | [ 0.02, 0.43]
## b_Gender1 | [-0.95, -0.21]general mediation of components of DOSPERT
experiment_dataset_analysis_scaled_complete <- experiment_dataset_analysis_scaled[complete.cases(experiment_dataset_analysis_scaled), ]
m1mod <- bf(riskPerceptionSum ~ dominanceSum + prestigeSum + leadershipSum)
m2mod <- bf(riskBenefitSum ~ dominanceSum + prestigeSum + leadershipSum)
ymod <- bf(riskSum ~ riskBenefitSum + riskPerceptionSum + dominanceSum +
prestigeSum + leadershipSum + Gender + Age)
med_model <- brm(ymod + m1mod + m2mod + set_rescor(FALSE),
backend = "cmdstanr", cores = parallel::detectCores(),
warmup = 1000,
iter = 8000, data = experiment_dataset_analysis_scaled_complete, save_pars = save_pars(all = TRUE)
)
m1mod.2 <- bf(riskPerceptionSum ~ dominanceSum * Gender + prestigeSum * Gender +
leadershipSum * Gender)
m2mod.2 <- bf(riskBenefitSum ~ dominanceSum * Gender + prestigeSum * Gender +
leadershipSum * Gender)
ymod.2 <- bf(riskSum ~ riskBenefitSum + riskPerceptionSum + dominanceSum * Gender +
prestigeSum * Gender + leadershipSum * Gender + Age)
med_model.2 <- brm(ymod.2 + m1mod.2 + m2mod.2 + set_rescor(FALSE),
backend = "cmdstanr", cores = parallel::detectCores(),
warmup = 1000, iter = 8000,
data = experiment_dataset_analysis_scaled_complete, save_pars = save_pars(all = TRUE)
)
m1mod.3 <- bf(riskPerceptionSum ~ dominanceSum + prestigeSum + leadershipSum
* Gender)
m2mod.3 <- bf(riskBenefitSum ~ dominanceSum + prestigeSum + leadershipSum
+ Gender)
ymod.3 <- bf(riskSum ~ riskBenefitSum + riskPerceptionSum + dominanceSum * Gender
+ Age)
med_model.3 <- brm(ymod.3 + m1mod.3 + m2mod.3 + set_rescor(FALSE),
backend = "cmdstanr", cores = parallel::detectCores(),
warmup = 1000, iter = 8000,
data = experiment_dataset_analysis_scaled_complete, save_pars = save_pars(all = TRUE)
)
# No mediation for above specifications with riskPerception.
m2mod.4 <- bf(riskBenefitSum ~ dominanceSum + prestigeSum + leadershipSum)
ymod.4 <- bf(riskSum ~ riskBenefitSum + dominanceSum + prestigeSum +
leadershipSum + Gender + Age)
med_model.4 <- brm(ymod.4 + m2mod.4 + set_rescor(FALSE),
backend = "cmdstanr", cores = parallel::detectCores(),
warmup = 1000, iter = 8000,
data = experiment_dataset_analysis_scaled_complete, save_pars = save_pars(all = TRUE)
)
m2mod.5 <- bf(riskBenefitSum ~ dominanceSum + prestigeSum + leadershipSum
+ Gender + Age)
ymod.5 <- bf(riskSum ~ riskBenefitSum + dominanceSum + prestigeSum +
leadershipSum + Gender + Age)
med_model.5 <- brm(ymod.5 + m2mod.5 + set_rescor(FALSE),
backend = "cmdstanr", cores = parallel::detectCores(),
warmup = 1000, iter = 8000,
data = experiment_dataset_analysis_scaled_complete, save_pars = save_pars(all = TRUE)
)
m2mod.6 <- bf(riskBenefitSum ~ dominanceSum * Gender + prestigeSum + leadershipSum
+ Age)
ymod.6 <- bf(riskSum ~ riskBenefitSum + dominanceSum * Gender + prestigeSum +
leadershipSum + Age)
med_model.6 <- brm(ymod.6 + m2mod.6 + set_rescor(FALSE),
backend = "cmdstanr", cores = parallel::detectCores(),
warmup = 1000, iter = 8000,
data = experiment_dataset_analysis_scaled_complete, save_pars = save_pars(all = TRUE)
)
m2mod.7 <- bf(riskBenefitSum ~ dominanceSum * Gender + prestigeSum * Gender +
leadershipSum * Gender + Age)
ymod.7 <- bf(riskSum ~ riskBenefitSum + dominanceSum * Gender + prestigeSum *
Gender + leadershipSum * Gender + Age)
med_model.7 <- brm(ymod.7 + m2mod.7 + set_rescor(FALSE),
backend = "cmdstanr", cores = parallel::detectCores(),
warmup = 1000, iter = 8000,
data = experiment_dataset_analysis_scaled_complete, save_pars = save_pars(all = TRUE)
)
# No mediation in above models but possible to condition
# on joint distributions?# compare possible mediation models
med_loo <- rstan::loo(med_model, med_model.2, med_model.3, med_model.4) # m3
# 5 (though too close)
med_comp <- bayesfactor_models(med_model, med_model.2, med_model.3, med_model.4, denominator = 4) # m3
med_loo2 <- rstan::loo(med_model.4, med_model.5, med_model.6, med_model.7)
med_comp2 <- bayesfactor_models(med_model.4, med_model.5, med_model.6, med_model.7, denominator = med_model.4)m6
bayes_R2(med_model.3) # riskSum = .49, percept = .14, benefit = .16## Estimate Est.Error Q2.5 Q97.5
## R2riskSum 0.4872155 0.04918886 0.37888630 0.5698785
## R2riskPerceptionSum 0.1409219 0.05151985 0.04777861 0.2461936
## R2riskBenefitSum 0.1617239 0.05501047 0.05957481 0.2719514hdi.3 <- hdi(med_model.3, effects = "fixed", component = "conditional", ci = .95)
hdi.3[
sign(hdi.3$CI_low) == sign(hdi.3$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
]## Highest Density Interval
##
## Parameter | 95% HDI
## ----------------------------------------------------------
## b_riskSum_riskBenefitSum | [ 0.34, 0.65]
## b_riskSum_dominanceSum | [ 0.24, 0.69]
## b_riskSum_dominanceSum:Gender1 | [-0.65, -0.06]
## b_riskPerceptionSum_prestigeSum | [ 0.01, 0.42]
## b_riskPerceptionSum_leadershipSum:Gender1 | [ 0.02, 0.76]
## b_riskBenefitSum_Gender1 | [-0.83, -0.09]# plot R^2 values for best mediation model
bayes_R2(med_model.3, summary = F) %>%
data.frame() %>%
pivot_longer(everything()) %>%
mutate(name = str_remove(name, "R2")) %>%
ggplot(aes(x = value, color = name, fill = name)) +
geom_density(alpha = .5) +
scale_color_ptol() +
scale_fill_ptol() +
scale_y_continuous(NULL, breaks = NULL) +
coord_cartesian(xlim = 0:1) +
labs(title <- expression(paste("Our ", italic(R)^{
2
}, "distributions")), x = NULL) +
theme_minimal() +
theme(legend.title <- element_blank())
# extract posterior info for computing mediation for Dominance
# notation, c' = direct effect; c = total effect.
post <- posterior_samples(med_model.3)## Warning: Method 'posterior_samples' is deprecated. Please see ?as_draws for
## recommended alternatives.post <- post %>%
mutate(
a1 = b_riskPerceptionSum_dominanceSum,
a2 = b_riskBenefitSum_dominanceSum,
b1 = b_riskSum_riskPerceptionSum,
b2 = b_riskSum_riskBenefitSum,
c_prime = b_riskSum_dominanceSum
) %>%
mutate(
a1b1 = a1 * b1,
a2b2 = a2 * b2
) %>%
mutate(c = c_prime + a1b1 + a2b2) %>%
mutate(total_indirect_effect = a1b1 + a2b2) %>%
mutate(c_minus_c_prime = c - c_prime)
post %>%
pivot_longer(a1:c_minus_c_prime) %>%
group_by(name) %>%
median_qi(value) %>%
mutate_if(is_double, round, digits = 3)## # A tibble: 10 × 7
## name value .lower .upper .width .point .interval
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 a1 -0.198 -0.404 0.007 0.95 median qi
## 2 a1b1 0.02 -0.007 0.075 0.95 median qi
## 3 a2 0.174 -0.029 0.379 0.95 median qi
## 4 a2b2 0.084 -0.014 0.2 0.95 median qi
## 5 b1 -0.12 -0.27 0.028 0.95 median qi
## 6 b2 0.498 0.344 0.65 0.95 median qi
## 7 c 0.58 0.33 0.832 0.95 median qi
## 8 c_minus_c_prime 0.108 0.002 0.232 0.95 median qi
## 9 c_prime 0.47 0.239 0.698 0.95 median qi
## 10 total_indirect_effect 0.108 0.002 0.232 0.95 median qi# dom -> benefit -> riskSum (marginal indirect effect)
# visualise results from tibble
post %>%
pivot_longer(c(c, c_prime)) %>%
ggplot(aes(x = value, fill = name, color = name)) +
geom_vline(xintercept = 0, color = "black") +
geom_density(alpha = .5) +
scale_color_ptol(NULL) +
scale_fill_ptol(NULL) +
scale_y_continuous(NULL, breaks = NULL) +
# labs +
coord_cartesian(xlim = c(-1.5, 1.5)) +
theme_minimal()
post %>%
pivot_longer(c(a1b1, a2b2)) %>%
ggplot(aes(x = value, fill = name, color = name)) +
geom_vline(xintercept = 0, color = "black") +
geom_density(alpha = .5) +
scale_color_ptol(NULL) +
scale_fill_ptol(NULL) +
scale_y_continuous(NULL, breaks = NULL) +
# labs +
coord_cartesian(xlim = c(-1.5, 1.5)) +
theme_minimal()
# extract posterior info for computing mediation for Prestige
# notation, c' = direct effect; c = total effect.
# post <- post %>%
# mutate(
# a1 = b_riskPerceptionSum_prestigeSum,
# a2 = b_riskBenefitSum_prestigeSum,
# b1 = b_riskSum_riskPerceptionSum,
# b2 = b_riskSum_riskBenefitSum,
# c_prime = b_riskSum_prestigeSum
# ) %>%
# mutate(
# a1b1 = a1 * b1,
# a2b2 = a2 * b2
# ) %>%
# mutate(c = c_prime + a1b1 + a2b2) %>%
# mutate(total_indirect_effect = a1b1 + a2b2) %>%
# mutate(c_minus_c_prime = c - c_prime)
post %>%
pivot_longer(a1:c_minus_c_prime) %>%
group_by(name) %>%
median_qi(value) %>%
mutate_if(is_double, round, digits = 3)## # A tibble: 10 × 7
## name value .lower .upper .width .point .interval
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 a1 -0.198 -0.404 0.007 0.95 median qi
## 2 a1b1 0.02 -0.007 0.075 0.95 median qi
## 3 a2 0.174 -0.029 0.379 0.95 median qi
## 4 a2b2 0.084 -0.014 0.2 0.95 median qi
## 5 b1 -0.12 -0.27 0.028 0.95 median qi
## 6 b2 0.498 0.344 0.65 0.95 median qi
## 7 c 0.58 0.33 0.832 0.95 median qi
## 8 c_minus_c_prime 0.108 0.002 0.232 0.95 median qi
## 9 c_prime 0.47 0.239 0.698 0.95 median qi
## 10 total_indirect_effect 0.108 0.002 0.232 0.95 median qi# No mediation
# extract posterior info for computing mediation
# notation, c' = direct effect; c = total effect.
# post <- post %>%
# mutate(
# a1 = b_riskPerceptionSum_dominanceSum,
# a2 = b_riskBenefitSum_leadershipSum,
# b1 = b_riskSum_riskPerceptionSum,
# b2 = b_riskSum_riskBenefitSum,
# c_prime = b_riskSum_leadershipSum
# ) %>%
# mutate(
# a1b1 = a1 * b1,
# a2b2 = a2 * b2
# ) %>%
# mutate(c = c_prime + a1b1 + a2b2) %>%
# mutate(total_indirect_effect = a1b1 + a2b2) %>%
# mutate(c_minus_c_prime = c - c_prime)
post %>%
pivot_longer(a1:c_minus_c_prime) %>%
group_by(name) %>%
median_qi(value) %>%
mutate_if(is_double, round, digits = 3)## # A tibble: 10 × 7
## name value .lower .upper .width .point .interval
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 a1 -0.198 -0.404 0.007 0.95 median qi
## 2 a1b1 0.02 -0.007 0.075 0.95 median qi
## 3 a2 0.174 -0.029 0.379 0.95 median qi
## 4 a2b2 0.084 -0.014 0.2 0.95 median qi
## 5 b1 -0.12 -0.27 0.028 0.95 median qi
## 6 b2 0.498 0.344 0.65 0.95 median qi
## 7 c 0.58 0.33 0.832 0.95 median qi
## 8 c_minus_c_prime 0.108 0.002 0.232 0.95 median qi
## 9 c_prime 0.47 0.239 0.698 0.95 median qi
## 10 total_indirect_effect 0.108 0.002 0.232 0.95 median qi# No mediation
# plot R^2 values for best mediation model
bayes_R2(med_model.6, summary = F) %>%
data.frame() %>%
pivot_longer(everything()) %>%
mutate(name = str_remove(name, "R2")) %>%
ggplot(aes(x = value, color = name, fill = name)) +
geom_density(alpha = .5) +
scale_color_ptol() +
scale_fill_ptol() +
scale_y_continuous(NULL, breaks = NULL) +
coord_cartesian(xlim = 0:1) +
labs(title = expression(paste("Our ", italic(R)^{
2
}, "distributions")), x = NULL) +
theme_minimal() +
theme(legend.title = element_blank())
# extract posterior info for computing mediation for Dominance
# notation, c' = direct effect; c = total effect.
post2 <- posterior_samples(med_model.6)## Warning: Method 'posterior_samples' is deprecated. Please see ?as_draws for
## recommended alternatives.post2 <- post2 %>%
mutate(
a1 = b_riskBenefitSum_dominanceSum,
b1 = b_riskSum_riskBenefitSum,
c_prime = b_riskSum_dominanceSum
) %>%
mutate(a1b1 = a1 * b1) %>%
mutate(c = c_prime + a1b1) %>%
mutate(total_indirect_effect = a1b1) %>%
mutate(c_minus_c_prime = c - c_prime)
post2 %>%
pivot_longer(a1:c_minus_c_prime) %>%
group_by(name) %>%
median_qi(value) %>%
mutate_if(is_double, round, digits = 3)## # A tibble: 7 × 7
## name value .lower .upper .width .point .interval
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 a1 0.258 -0.047 0.561 0.95 median qi
## 2 a1b1 0.12 -0.022 0.288 0.95 median qi
## 3 b1 0.48 0.324 0.635 0.95 median qi
## 4 c 0.558 0.279 0.834 0.95 median qi
## 5 c_minus_c_prime 0.12 -0.022 0.288 0.95 median qi
## 6 c_prime 0.434 0.195 0.672 0.95 median qi
## 7 total_indirect_effect 0.12 -0.022 0.288 0.95 median qi# dom -> benefit -> riskSum (marginal indirect effect)
# visualise results from tibble
post %>%
pivot_longer(c(c, c_prime)) %>%
ggplot(aes(x = value, fill = name, color = name)) +
geom_vline(xintercept = 0, color = "black") +
geom_density(alpha = .5) +
scale_color_ptol(NULL) +
scale_fill_ptol(NULL) +
scale_y_continuous(NULL, breaks = NULL) +
# labs +
coord_cartesian(xlim = c(-1.5, 1.5)) +
theme_minimal()
post %>%
pivot_longer(c(a1b1, a2b2)) %>%
ggplot(aes(x = value, fill = name, color = name)) +
geom_vline(xintercept = 0, color = "black") +
geom_density(alpha = .5) +
scale_color_ptol(NULL) +
scale_fill_ptol(NULL) +
scale_y_continuous(NULL, breaks = NULL) +
# labs +
coord_cartesian(xlim = c(-1.5, 1.5)) +
theme_minimal()
Regression m2-m3: Domain specific risk preference across DOPL motives, gender and age —-
# Additive model (m2)
m2 <- brm(mvbind(ethicalPreference, financialPreference, socialPreference, healthAndSafetyPreference, recreationalPreference) ~ dominanceSum + prestigeSum + leadershipSum + Gender + Age,
data = experiment_dataset_analysis_scaled, backend = "cmdstanr", cores = parallel::detectCores(), save_pars = save_pars(all = TRUE), iter = 10000,
prior = c(
prior(normal(0, 1), coef = "Age", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "ethicalPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "ethicalPreference"),
prior(normal(0, 1), class = "Intercept", resp = "ethicalPreference"),
prior(normal(0, 1), class = "sigma", resp = "ethicalPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "financialPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "financialPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "financialPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "financialPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "financialPreference"),
prior(normal(0, 1), class = "Intercept", resp = "financialPreference"),
prior(normal(0, 1), class = "sigma", resp = "financialPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "healthAndSafetyPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), class = "Intercept", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), class = "sigma", resp = "healthAndSafetyPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "recreationalPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "recreationalPreference"),
prior(normal(0, 1), class = "Intercept", resp = "recreationalPreference"),
prior(normal(0, 1), class = "sigma", resp = "recreationalPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "socialPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "socialPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "socialPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "socialPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "socialPreference"),
prior(normal(0, 1), class = "Intercept", resp = "socialPreference"),
prior(normal(0, 1), class = "sigma", resp = "socialPreference")
)
)
summary(m2)
# write.csv(round(fixef(m2), 2), "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/DoPL/Experiments/DoPL_Experiment_Two/Priors/fixef_m2.csv")HDI m2
m2_hdi <- hdi(m2, effects = "fixed", component = "conditional", ci = .95)
m2_hdi[
sign(m2_hdi$CI_low) == sign(m2_hdi$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
]## Highest Density Interval
##
## Parameter | 95% HDI
## ---------------------------------------------------------
## b_ethicalPreference_dominanceSum | [ 0.15, 0.55]
## b_socialPreference_Gender1 | [-0.76, -0.04]
## b_healthAndSafetyPreference_dominanceSum | [ 0.08, 0.49]
## b_recreationalPreference_Intercept | [ 0.11, 0.56]
## b_recreationalPreference_dominanceSum | [ 0.22, 0.60]
## b_recreationalPreference_Gender1 | [-1.04, -0.38]
## b_recreationalPreference_Age | [ 0.05, 0.39]m2 + Gender-DOPL interactions
m3 <- brm(mvbind(ethicalPreference, financialPreference, socialPreference, healthAndSafetyPreference, recreationalPreference) ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age,
data = experiment_dataset_analysis_scaled, backend = "cmdstanr", cores = parallel::detectCores(), save_pars = save_pars(all = TRUE), iter = 10000,
prior = c(
prior(normal(0, 1), coef = "Age", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "ethicalPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "ethicalPreference"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "ethicalPreference"),
prior(normal(0, 1), class = "Intercept", resp = "ethicalPreference"),
prior(normal(0, 1), class = "sigma", resp = "ethicalPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "financialPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "financialPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "financialPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "financialPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "financialPreference"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "financialPreference"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "financialPreference"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "financialPreference"),
prior(normal(0, 1), class = "Intercept", resp = "financialPreference"),
prior(normal(0, 1), class = "sigma", resp = "financialPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "healthAndSafetyPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), class = "Intercept", resp = "healthAndSafetyPreference"),
prior(normal(0, 1), class = "sigma", resp = "healthAndSafetyPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "recreationalPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "recreationalPreference"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "recreationalPreference"),
prior(normal(0, 1), class = "Intercept", resp = "recreationalPreference"),
prior(normal(0, 1), class = "sigma", resp = "recreationalPreference"),
#
prior(normal(0, 1), coef = "Age", resp = "socialPreference"),
prior(normal(0, 1), coef = "Gender1", resp = "socialPreference"),
prior(normal(2, 1), coef = "dominanceSum", resp = "socialPreference"),
prior(normal(0, 1), coef = "leadershipSum", resp = "socialPreference"),
prior(normal(0, 1), coef = "prestigeSum", resp = "socialPreference"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "socialPreference"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "socialPreference"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "socialPreference"),
prior(normal(0, 1), class = "Intercept", resp = "socialPreference"),
prior(normal(0, 1), class = "sigma", resp = "socialPreference")
)
)
# write.csv(round(fixef(m3), 2), "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/DoPL/Experiments/DoPL_Experiment_Two/Priors/fixef_m3.csv")
# saveRDS(m3, "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/Dissertation/RDS_Files/m3_exp_1.rds")summary(m3)## 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 ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## financialPreference ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## socialPreference ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## healthAndSafetyPreference ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## recreationalPreference ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## Data: experiment_dataset_analysis_scaled (Number of observations: 107)
## Draws: 4 chains, each with iter = 10000; warmup = 5000; thin = 1;
## total post-warmup draws = 20000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## ethicalPreference_Intercept 0.11 0.13 -0.14
## financialPreference_Intercept 0.14 0.13 -0.12
## socialPreference_Intercept 0.17 0.13 -0.09
## healthAndSafetyPreference_Intercept 0.09 0.13 -0.17
## recreationalPreference_Intercept 0.33 0.12 0.10
## ethicalPreference_dominanceSum 0.51 0.15 0.21
## ethicalPreference_Gender1 -0.28 0.18 -0.62
## ethicalPreference_prestigeSum -0.12 0.14 -0.40
## ethicalPreference_leadershipSum 0.05 0.14 -0.22
## ethicalPreference_Age 0.16 0.09 -0.03
## ethicalPreference_dominanceSum:Gender1 -0.27 0.19 -0.65
## ethicalPreference_Gender1:prestigeSum 0.30 0.19 -0.07
## ethicalPreference_Gender1:leadershipSum -0.03 0.19 -0.40
## financialPreference_dominanceSum 0.27 0.16 -0.04
## financialPreference_Gender1 -0.31 0.19 -0.67
## financialPreference_prestigeSum -0.00 0.15 -0.29
## financialPreference_leadershipSum 0.05 0.14 -0.24
## financialPreference_Age 0.01 0.10 -0.18
## financialPreference_dominanceSum:Gender1 -0.23 0.20 -0.63
## financialPreference_Gender1:prestigeSum 0.30 0.20 -0.09
## financialPreference_Gender1:leadershipSum -0.11 0.19 -0.48
## socialPreference_dominanceSum 0.36 0.15 0.06
## socialPreference_Gender1 -0.40 0.18 -0.75
## socialPreference_prestigeSum 0.12 0.14 -0.16
## socialPreference_leadershipSum 0.06 0.14 -0.21
## socialPreference_Age 0.01 0.10 -0.18
## socialPreference_dominanceSum:Gender1 -0.37 0.20 -0.76
## socialPreference_Gender1:prestigeSum 0.10 0.20 -0.29
## socialPreference_Gender1:leadershipSum -0.03 0.19 -0.40
## healthAndSafetyPreference_dominanceSum 0.38 0.16 0.08
## healthAndSafetyPreference_Gender1 -0.24 0.18 -0.60
## healthAndSafetyPreference_prestigeSum 0.03 0.15 -0.25
## healthAndSafetyPreference_leadershipSum 0.05 0.14 -0.23
## healthAndSafetyPreference_Age 0.15 0.10 -0.04
## healthAndSafetyPreference_dominanceSum:Gender1 -0.17 0.20 -0.56
## healthAndSafetyPreference_Gender1:prestigeSum 0.07 0.20 -0.32
## healthAndSafetyPreference_Gender1:leadershipSum -0.12 0.19 -0.49
## recreationalPreference_dominanceSum 0.50 0.14 0.22
## recreationalPreference_Gender1 -0.71 0.17 -1.03
## recreationalPreference_prestigeSum -0.24 0.13 -0.51
## recreationalPreference_leadershipSum 0.05 0.13 -0.20
## recreationalPreference_Age 0.23 0.09 0.06
## recreationalPreference_dominanceSum:Gender1 -0.15 0.18 -0.50
## recreationalPreference_Gender1:prestigeSum 0.34 0.18 -0.01
## recreationalPreference_Gender1:leadershipSum -0.16 0.17 -0.50
## u-95% CI Rhat Bulk_ESS Tail_ESS
## ethicalPreference_Intercept 0.36 1.00 13119 13823
## financialPreference_Intercept 0.39 1.00 13669 14869
## socialPreference_Intercept 0.42 1.00 14769 14565
## healthAndSafetyPreference_Intercept 0.35 1.00 13028 14506
## recreationalPreference_Intercept 0.56 1.00 13895 14744
## ethicalPreference_dominanceSum 0.81 1.00 9310 12765
## ethicalPreference_Gender1 0.08 1.00 12844 14312
## ethicalPreference_prestigeSum 0.16 1.00 9430 12835
## ethicalPreference_leadershipSum 0.33 1.00 10424 13593
## ethicalPreference_Age 0.34 1.00 12481 15151
## ethicalPreference_dominanceSum:Gender1 0.10 1.00 10037 13177
## ethicalPreference_Gender1:prestigeSum 0.69 1.00 9895 13942
## ethicalPreference_Gender1:leadershipSum 0.34 1.00 10644 13270
## financialPreference_dominanceSum 0.58 1.00 9774 13110
## financialPreference_Gender1 0.06 1.00 13381 14116
## financialPreference_prestigeSum 0.29 1.00 9343 14333
## financialPreference_leadershipSum 0.32 1.00 11166 14421
## financialPreference_Age 0.21 1.00 12991 15113
## financialPreference_dominanceSum:Gender1 0.15 1.00 10484 14167
## financialPreference_Gender1:prestigeSum 0.69 1.00 10273 14050
## financialPreference_Gender1:leadershipSum 0.28 1.00 10908 14124
## socialPreference_dominanceSum 0.66 1.00 10972 13551
## socialPreference_Gender1 -0.03 1.00 14525 15184
## socialPreference_prestigeSum 0.40 1.00 10613 14651
## socialPreference_leadershipSum 0.33 1.00 11346 14865
## socialPreference_Age 0.20 1.00 13844 15791
## socialPreference_dominanceSum:Gender1 0.01 1.00 11370 14420
## socialPreference_Gender1:prestigeSum 0.48 1.00 11163 13155
## socialPreference_Gender1:leadershipSum 0.35 1.00 11231 14402
## healthAndSafetyPreference_dominanceSum 0.69 1.00 9477 13082
## healthAndSafetyPreference_Gender1 0.13 1.00 13227 15007
## healthAndSafetyPreference_prestigeSum 0.32 1.00 9490 12465
## healthAndSafetyPreference_leadershipSum 0.32 1.00 10555 13162
## healthAndSafetyPreference_Age 0.34 1.00 12681 14726
## healthAndSafetyPreference_dominanceSum:Gender1 0.21 1.00 9663 13493
## healthAndSafetyPreference_Gender1:prestigeSum 0.45 1.00 9748 13792
## healthAndSafetyPreference_Gender1:leadershipSum 0.26 1.00 10209 13538
## recreationalPreference_dominanceSum 0.78 1.00 9821 13243
## recreationalPreference_Gender1 -0.38 1.00 13636 15059
## recreationalPreference_prestigeSum 0.01 1.00 9349 13503
## recreationalPreference_leadershipSum 0.30 1.00 10864 13120
## recreationalPreference_Age 0.40 1.00 13083 15523
## recreationalPreference_dominanceSum:Gender1 0.20 1.00 10395 13583
## recreationalPreference_Gender1:prestigeSum 0.69 1.00 10008 13482
## recreationalPreference_Gender1:leadershipSum 0.18 1.00 10756 13437
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## sigma_ethicalPreference 0.94 0.06 0.82 1.07 1.00
## sigma_financialPreference 0.97 0.07 0.85 1.11 1.00
## sigma_socialPreference 0.94 0.06 0.83 1.08 1.00
## sigma_healthAndSafetyPreference 0.96 0.07 0.84 1.10 1.00
## sigma_recreationalPreference 0.87 0.06 0.76 0.99 1.00
## Bulk_ESS Tail_ESS
## sigma_ethicalPreference 13094 14888
## sigma_financialPreference 15692 16496
## sigma_socialPreference 16920 16050
## sigma_healthAndSafetyPreference 13991 15953
## sigma_recreationalPreference 14104 15421
##
## Residual Correlations:
## Estimate Est.Error
## rescor(ethicalPreference,financialPreference) 0.65 0.06
## rescor(ethicalPreference,socialPreference) 0.60 0.06
## rescor(financialPreference,socialPreference) 0.61 0.06
## rescor(ethicalPreference,healthAndSafetyPreference) 0.73 0.05
## rescor(financialPreference,healthAndSafetyPreference) 0.65 0.06
## rescor(socialPreference,healthAndSafetyPreference) 0.65 0.06
## rescor(ethicalPreference,recreationalPreference) 0.71 0.05
## rescor(financialPreference,recreationalPreference) 0.68 0.05
## rescor(socialPreference,recreationalPreference) 0.56 0.07
## rescor(healthAndSafetyPreference,recreationalPreference) 0.66 0.06
## l-95% CI u-95% CI Rhat
## rescor(ethicalPreference,financialPreference) 0.53 0.75 1.00
## rescor(ethicalPreference,socialPreference) 0.47 0.71 1.00
## rescor(financialPreference,socialPreference) 0.48 0.72 1.00
## rescor(ethicalPreference,healthAndSafetyPreference) 0.63 0.81 1.00
## rescor(financialPreference,healthAndSafetyPreference) 0.53 0.75 1.00
## rescor(socialPreference,healthAndSafetyPreference) 0.53 0.75 1.00
## rescor(ethicalPreference,recreationalPreference) 0.60 0.79 1.00
## rescor(financialPreference,recreationalPreference) 0.56 0.77 1.00
## rescor(socialPreference,recreationalPreference) 0.42 0.68 1.00
## rescor(healthAndSafetyPreference,recreationalPreference) 0.54 0.75 1.00
## Bulk_ESS Tail_ESS
## rescor(ethicalPreference,financialPreference) 14045 15133
## rescor(ethicalPreference,socialPreference) 16020 16715
## rescor(financialPreference,socialPreference) 17174 16545
## rescor(ethicalPreference,healthAndSafetyPreference) 14631 15071
## rescor(financialPreference,healthAndSafetyPreference) 16068 15469
## rescor(socialPreference,healthAndSafetyPreference) 17212 15235
## rescor(ethicalPreference,recreationalPreference) 14924 15769
## rescor(financialPreference,recreationalPreference) 16687 16224
## rescor(socialPreference,recreationalPreference) 16642 16175
## rescor(healthAndSafetyPreference,recreationalPreference) 15830 15901
##
## 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).HDI m3
m3_hdi <- hdi(m3, effects = "fixed", component = "conditional", ci = .95)
m3_hdi[
sign(m3_hdi$CI_low) == sign(m3_hdi$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
]## Highest Density Interval
##
## Parameter | 95% HDI
## ---------------------------------------------------------
## b_ethicalPreference_dominanceSum | [ 0.22, 0.81]
## b_socialPreference_dominanceSum | [ 0.05, 0.65]
## b_socialPreference_Gender1 | [-0.76, -0.05]
## b_healthAndSafetyPreference_dominanceSum | [ 0.08, 0.69]
## b_recreationalPreference_Intercept | [ 0.10, 0.56]
## b_recreationalPreference_dominanceSum | [ 0.23, 0.78]
## b_recreationalPreference_Gender1 | [-1.03, -0.38]
## b_recreationalPreference_Age | [ 0.06, 0.40]# Model Comparison (m2 and m3)
rstan::loo(m2, m3) # m2 better
co <- rstan::loo(m2, m3)
looic_ <- c(co$loos$m2$looic, co$loos$m3$looic)
looic_se <- c(co$loos$m2$se_looic, co$loos$m3$se_looic)
looic_elpd <- c(co$loos$m2$elpd_loo, co$loos$m3$elpd_loo)
looic_elpd_se <- c(co$loos$m2$se_elpd_loo, co$loos$m3$se_elpd_loo)
loo_table <- data.frame(looic_, looic_se, looic_elpd, looic_elpd_se, row.names = c("m2", "m3"))loo_table## looic_ looic_se looic_elpd looic_elpd_se
## m2 1153.223 45.73624 -576.6114 22.86812
## m3 1175.348 45.15830 -587.6742 22.57915bayes_R2(m2)## Estimate Est.Error Q2.5 Q97.5
## R2ethicalPreference 0.1818593 0.05277993 0.08018318 0.2853971
## R2financialPreference 0.1168314 0.04597399 0.03637310 0.2128145
## R2socialPreference 0.1671061 0.05285828 0.06751315 0.2726679
## R2healthAndSafetyPreference 0.1414594 0.04916081 0.05141047 0.2425328
## R2recreationalPreference 0.2859518 0.05557302 0.17206987 0.3900298bayes_R2(m3)## Estimate Est.Error Q2.5 Q97.5
## R2ethicalPreference 0.2105040 0.05244274 0.10642322 0.3118639
## R2financialPreference 0.1497163 0.04760039 0.06257252 0.2472180
## R2socialPreference 0.2010671 0.05227597 0.10008772 0.3034595
## R2healthAndSafetyPreference 0.1621954 0.04896555 0.07063896 0.2609062
## R2recreationalPreference 0.3104758 0.05393463 0.19968937 0.4086270# Regression m4-m5: Benefit, perception and risk taking across subdomains for DOPL motives, age and sex ----Additive model
m4 <- brm(mvbind(riskSum, riskPerceptionSum, riskBenefitSum) ~ dominanceSum + prestigeSum + leadershipSum + Age + Gender,
data = experiment_dataset_analysis_scaled, backend = "cmdstanr", cores = parallel::detectCores(), save_pars = save_pars(all = TRUE), iter = 5000,
prior = c(
prior(normal(0, 1), class = "Intercept", resp = "riskSum"),
prior(normal(0, 1), class = "sigma", resp = "riskSum"),
prior(normal(-3, 1), coef = "Age", resp = "riskSum"),
prior(normal(-3, 1), coef = "Gender1", resp = "riskSum"),
prior(normal(3, 1), coef = "dominanceSum", resp = "riskSum"),
prior(normal(-2, 1), coef = "leadershipSum", resp = "riskSum"),
prior(normal(0, 1), coef = "prestigeSum", resp = "riskSum"),
#
prior(normal(0, 1), class = "Intercept", resp = "riskPerceptionSum"),
prior(normal(0, 1), class = "sigma", resp = "riskPerceptionSum"),
prior(normal(3, 1), coef = "Age", resp = "riskPerceptionSum"),
prior(normal(3, 1), coef = "Gender1", resp = "riskPerceptionSum"),
prior(normal(-3, 1), coef = "dominanceSum", resp = "riskPerceptionSum"),
prior(normal(2, 1), coef = "leadershipSum", resp = "riskPerceptionSum"),
prior(normal(0, 1), coef = "prestigeSum", resp = "riskPerceptionSum"),
#
prior(normal(0, 1), class = "Intercept", resp = "riskBenefitSum"),
prior(normal(0, 1), class = "sigma", resp = "riskBenefitSum"),
prior(normal(-3, 1), coef = "Age", resp = "riskBenefitSum"),
prior(normal(-3, 1), coef = "Gender1", resp = "riskBenefitSum"),
prior(normal(3, 1), coef = "dominanceSum", resp = "riskBenefitSum"),
prior(normal(-2, 1), coef = "leadershipSum", resp = "riskBenefitSum"),
prior(normal(0, 1), coef = "prestigeSum", resp = "riskBenefitSum")
)
)
# write.csv(round(fixef(m4), 2), "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/DoPL/Experiments/DoPL_Experiment_Two/Priors/fixef_m4.csv")summary(m4)## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: riskSum ~ dominanceSum + prestigeSum + leadershipSum + Age + Gender
## riskPerceptionSum ~ dominanceSum + prestigeSum + leadershipSum + Age + Gender
## riskBenefitSum ~ dominanceSum + prestigeSum + leadershipSum + Age + Gender
## Data: experiment_dataset_analysis_scaled (Number of observations: 107)
## Draws: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
## total post-warmup draws = 10000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## riskSum_Intercept 0.25 0.12 0.01 0.49 1.00
## riskPerceptionSum_Intercept -0.24 0.13 -0.51 0.02 1.00
## riskBenefitSum_Intercept 0.27 0.13 0.02 0.52 1.00
## riskSum_dominanceSum 0.37 0.10 0.18 0.56 1.00
## riskSum_prestigeSum 0.17 0.10 -0.02 0.36 1.00
## riskSum_leadershipSum -0.01 0.10 -0.20 0.17 1.00
## riskSum_Age 0.00 0.09 -0.17 0.18 1.00
## riskSum_Gender1 -0.51 0.18 -0.86 -0.16 1.00
## riskPerceptionSum_dominanceSum -0.25 0.11 -0.46 -0.04 1.00
## riskPerceptionSum_prestigeSum 0.21 0.11 -0.00 0.42 1.00
## riskPerceptionSum_leadershipSum 0.04 0.11 -0.17 0.24 1.00
## riskPerceptionSum_Age -0.16 0.10 -0.36 0.04 1.00
## riskPerceptionSum_Gender1 0.43 0.19 0.06 0.82 1.00
## riskBenefitSum_dominanceSum 0.22 0.10 0.02 0.43 1.00
## riskBenefitSum_prestigeSum 0.15 0.11 -0.06 0.35 1.00
## riskBenefitSum_leadershipSum -0.03 0.10 -0.23 0.16 1.00
## riskBenefitSum_Age 0.06 0.10 -0.13 0.26 1.00
## riskBenefitSum_Gender1 -0.60 0.19 -0.98 -0.24 1.00
## Bulk_ESS Tail_ESS
## riskSum_Intercept 10131 7820
## riskPerceptionSum_Intercept 12180 7590
## riskBenefitSum_Intercept 10945 8487
## riskSum_dominanceSum 9777 7771
## riskSum_prestigeSum 9897 8546
## riskSum_leadershipSum 9723 7090
## riskSum_Age 10191 7705
## riskSum_Gender1 10156 7865
## riskPerceptionSum_dominanceSum 11725 8381
## riskPerceptionSum_prestigeSum 12653 8114
## riskPerceptionSum_leadershipSum 11137 7424
## riskPerceptionSum_Age 12729 7785
## riskPerceptionSum_Gender1 12138 8084
## riskBenefitSum_dominanceSum 9152 7889
## riskBenefitSum_prestigeSum 10219 8089
## riskBenefitSum_leadershipSum 9441 8253
## riskBenefitSum_Age 10501 7724
## riskBenefitSum_Gender1 10198 8386
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_riskSum 0.90 0.06 0.79 1.04 1.00 10889
## sigma_riskPerceptionSum 0.99 0.07 0.86 1.14 1.00 11333
## sigma_riskBenefitSum 0.97 0.07 0.84 1.11 1.00 10477
## Tail_ESS
## sigma_riskSum 7825
## sigma_riskPerceptionSum 7283
## sigma_riskBenefitSum 8282
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(riskSum,riskPerceptionSum) -0.15 0.10 -0.33 0.04
## rescor(riskSum,riskBenefitSum) 0.51 0.07 0.36 0.65
## rescor(riskPerceptionSum,riskBenefitSum) -0.01 0.10 -0.20 0.18
## Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 1.00 11105 7404
## rescor(riskSum,riskBenefitSum) 1.00 9881 7566
## rescor(riskPerceptionSum,riskBenefitSum) 1.00 11002 7565
##
## 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).HDI m4
m4_hdi <- hdi(m4, effects = "fixed", component = "conditional", ci = .95)
m4_hdi[
sign(m4_hdi$CI_low) == sign(m4_hdi$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
]## Highest Density Interval
##
## Parameter | 95% HDI
## -------------------------------------------------
## b_riskSum_Intercept | [ 0.01, 0.49]
## b_riskSum_dominanceSum | [ 0.18, 0.56]
## b_riskSum_Gender1 | [-0.85, -0.16]
## b_riskPerceptionSum_dominanceSum | [-0.46, -0.04]
## b_riskPerceptionSum_Gender1 | [ 0.06, 0.81]
## b_riskBenefitSum_Intercept | [ 0.02, 0.52]
## b_riskBenefitSum_dominanceSum | [ 0.03, 0.43]
## b_riskBenefitSum_Gender1 | [-0.97, -0.23]m4 + DOPL motives:gender interactions
m5 <- brm(mvbind(riskSum, riskPerceptionSum, riskBenefitSum) ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age,
data = experiment_dataset_analysis_scaled, backend = "cmdstanr", cores = parallel::detectCores(), iter = 6000, save_pars = save_pars(all = TRUE),
prior = c(
prior(normal(0, 1), class = "Intercept", resp = "riskSum"),
prior(normal(0, 1), class = "sigma", resp = "riskSum"),
prior(normal(-3, 1), coef = "Age", resp = "riskSum"),
prior(normal(-3, 1), coef = "Gender1", resp = "riskSum"),
prior(normal(3, 1), coef = "dominanceSum", resp = "riskSum"),
prior(normal(-2, 1), coef = "leadershipSum", resp = "riskSum"),
prior(normal(0, 1), coef = "prestigeSum", resp = "riskSum"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "riskSum"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "riskSum"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "riskSum"),
#
prior(normal(0, 1), class = "Intercept", resp = "riskPerceptionSum"),
prior(normal(0, 1), class = "sigma", resp = "riskPerceptionSum"),
prior(normal(3, 1), coef = "Age", resp = "riskPerceptionSum"),
prior(normal(3, 1), coef = "Gender1", resp = "riskPerceptionSum"),
prior(normal(-3, 1), coef = "dominanceSum", resp = "riskPerceptionSum"),
prior(normal(2, 1), coef = "leadershipSum", resp = "riskPerceptionSum"),
prior(normal(0, 1), coef = "prestigeSum", resp = "riskPerceptionSum"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "riskPerceptionSum"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "riskPerceptionSum"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "riskPerceptionSum"),
#
prior(normal(0, 1), class = "Intercept", resp = "riskBenefitSum"),
prior(normal(0, 1), class = "sigma", resp = "riskBenefitSum"),
prior(normal(0, 1), resp = "riskBenefitSum"),
prior(normal(-3, 1), coef = "Age", resp = "riskBenefitSum"),
prior(normal(-3, 1), coef = "Gender1", resp = "riskBenefitSum"),
prior(normal(3, 1), coef = "dominanceSum", resp = "riskBenefitSum"),
prior(normal(-2, 1), coef = "leadershipSum", resp = "riskBenefitSum"),
prior(normal(0, 1), coef = "prestigeSum", resp = "riskBenefitSum"),
prior(normal(0, 1), coef = "dominanceSum:Gender1", resp = "riskBenefitSum"),
prior(normal(0, 1), coef = "Gender1:prestigeSum", resp = "riskBenefitSum"),
prior(normal(0, 1), coef = "Gender1:leadershipSum", resp = "riskBenefitSum")
)
)
# write.csv(round(fixef(m5), 2), "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/DoPL/Experiments/DoPL_Experiment_Two/Priors/fixef_m5.csv")
# saveRDS(m5, "/Users/andrew/Library/CloudStorage/OneDrive-Personal/Documents/1_UoE/Research/PhD/Dissertation/RDS_Files/m5_exp_1.rds")summary(m5)## Family: MV(gaussian, gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: riskSum ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## riskPerceptionSum ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## riskBenefitSum ~ dominanceSum * Gender + prestigeSum * Gender + leadershipSum * Gender + Age
## Data: experiment_dataset_analysis_scaled (Number of observations: 107)
## Draws: 4 chains, each with iter = 6000; warmup = 3000; thin = 1;
## total post-warmup draws = 12000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI
## riskSum_Intercept 0.21 0.12 -0.03 0.45
## riskPerceptionSum_Intercept -0.23 0.13 -0.50 0.03
## riskBenefitSum_Intercept 0.25 0.13 -0.02 0.51
## riskSum_dominanceSum 0.66 0.15 0.37 0.94
## riskSum_Gender1 -0.50 0.18 -0.85 -0.15
## riskSum_prestigeSum 0.10 0.14 -0.18 0.36
## riskSum_leadershipSum -0.03 0.14 -0.30 0.24
## riskSum_Age 0.02 0.09 -0.16 0.19
## riskSum_dominanceSum:Gender1 -0.48 0.19 -0.85 -0.11
## riskSum_Gender1:prestigeSum 0.10 0.19 -0.26 0.47
## riskSum_Gender1:leadershipSum 0.03 0.18 -0.33 0.39
## riskPerceptionSum_dominanceSum -0.23 0.16 -0.55 0.09
## riskPerceptionSum_Gender1 0.43 0.19 0.06 0.80
## riskPerceptionSum_prestigeSum 0.31 0.15 0.01 0.61
## riskPerceptionSum_leadershipSum -0.20 0.15 -0.48 0.10
## riskPerceptionSum_Age -0.17 0.10 -0.37 0.02
## riskPerceptionSum_dominanceSum:Gender1 -0.06 0.20 -0.46 0.35
## riskPerceptionSum_Gender1:prestigeSum -0.20 0.21 -0.60 0.21
## riskPerceptionSum_Gender1:leadershipSum 0.44 0.20 0.04 0.83
## riskBenefitSum_dominanceSum 0.39 0.16 0.07 0.71
## riskBenefitSum_Gender1 -0.60 0.19 -0.97 -0.22
## riskBenefitSum_prestigeSum 0.02 0.15 -0.28 0.31
## riskBenefitSum_leadershipSum -0.03 0.15 -0.32 0.26
## riskBenefitSum_Age 0.07 0.10 -0.12 0.27
## riskBenefitSum_dominanceSum:Gender1 -0.27 0.21 -0.68 0.14
## riskBenefitSum_Gender1:prestigeSum 0.22 0.21 -0.18 0.62
## riskBenefitSum_Gender1:leadershipSum -0.00 0.20 -0.39 0.40
## Rhat Bulk_ESS Tail_ESS
## riskSum_Intercept 1.00 11689 9491
## riskPerceptionSum_Intercept 1.00 14647 9197
## riskBenefitSum_Intercept 1.00 11758 9629
## riskSum_dominanceSum 1.00 6862 8616
## riskSum_Gender1 1.00 11082 9179
## riskSum_prestigeSum 1.00 6867 8768
## riskSum_leadershipSum 1.00 7725 8617
## riskSum_Age 1.00 11683 9430
## riskSum_dominanceSum:Gender1 1.00 7054 8329
## riskSum_Gender1:prestigeSum 1.00 6976 8639
## riskSum_Gender1:leadershipSum 1.00 7754 8706
## riskPerceptionSum_dominanceSum 1.00 8049 8432
## riskPerceptionSum_Gender1 1.00 13880 9290
## riskPerceptionSum_prestigeSum 1.00 8068 8659
## riskPerceptionSum_leadershipSum 1.00 8806 8802
## riskPerceptionSum_Age 1.00 13916 8965
## riskPerceptionSum_dominanceSum:Gender1 1.00 8251 8659
## riskPerceptionSum_Gender1:prestigeSum 1.00 8390 9066
## riskPerceptionSum_Gender1:leadershipSum 1.00 8865 9086
## riskBenefitSum_dominanceSum 1.00 7373 8347
## riskBenefitSum_Gender1 1.00 11026 9617
## riskBenefitSum_prestigeSum 1.00 7355 8570
## riskBenefitSum_leadershipSum 1.00 7904 8782
## riskBenefitSum_Age 1.00 11912 9862
## riskBenefitSum_dominanceSum:Gender1 1.00 7641 8907
## riskBenefitSum_Gender1:prestigeSum 1.00 7485 8284
## riskBenefitSum_Gender1:leadershipSum 1.00 7648 8959
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_riskSum 0.89 0.06 0.78 1.03 1.00 10548
## sigma_riskPerceptionSum 0.97 0.07 0.84 1.12 1.00 12927
## sigma_riskBenefitSum 0.97 0.07 0.85 1.12 1.00 10847
## Tail_ESS
## sigma_riskSum 8696
## sigma_riskPerceptionSum 9530
## sigma_riskBenefitSum 9033
##
## Residual Correlations:
## Estimate Est.Error l-95% CI u-95% CI
## rescor(riskSum,riskPerceptionSum) -0.16 0.10 -0.35 0.03
## rescor(riskSum,riskBenefitSum) 0.51 0.07 0.36 0.65
## rescor(riskPerceptionSum,riskBenefitSum) 0.00 0.10 -0.19 0.19
## Rhat Bulk_ESS Tail_ESS
## rescor(riskSum,riskPerceptionSum) 1.00 12579 9450
## rescor(riskSum,riskBenefitSum) 1.00 10992 8853
## rescor(riskPerceptionSum,riskBenefitSum) 1.00 13732 9142
##
## 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).HDI m5
m5_hdi <- hdi(m5, effects = "fixed", component = "conditional", ci = .95)
m5_hdi[
sign(m5_hdi$CI_low) == sign(m5_hdi$CI_high),
c("Parameter", "CI", "CI_low", "CI_high")
]## Highest Density Interval
##
## Parameter | 95% HDI
## ----------------------------------------------------------
## b_riskSum_dominanceSum | [ 0.36, 0.93]
## b_riskSum_Gender1 | [-0.84, -0.15]
## b_riskSum_dominanceSum:Gender1 | [-0.85, -0.11]
## b_riskPerceptionSum_Gender1 | [ 0.06, 0.80]
## b_riskPerceptionSum_prestigeSum | [ 0.01, 0.60]
## b_riskPerceptionSum_Gender1:leadershipSum | [ 0.05, 0.83]
## b_riskBenefitSum_dominanceSum | [ 0.07, 0.71]
## b_riskBenefitSum_Gender1 | [-0.97, -0.23]# conditional effects plot
plot(conditional_effects(m5, resp = "riskPerceptionSum"), points = T)
# Model comparison
co2 <- rstan::loo(m4, m5)
looic_2 <- c(co2$loos$m4$looic, co2$loos$m5$looic)## Warning: Accessing looic using '$' is deprecated and will be removed in a
## future release. Please extract the looic estimate from the 'estimates' component
## instead.
## Warning: Accessing looic using '$' is deprecated and will be removed in a
## future release. Please extract the looic estimate from the 'estimates' component
## instead.looic_2_se <- c(co2$loos$m4$se_looic, co2$loos$m5$se_looic)## Warning: Accessing se_looic using '$' is deprecated and will be removed in
## a future release. Please extract the se_looic estimate from the 'estimates'
## component instead.## Warning: Accessing se_looic using '$' is deprecated and will be removed in
## a future release. Please extract the se_looic estimate from the 'estimates'
## component instead.looic_elpd_2 <- c(co2$loos$m4$elpd_loo, co2$loos$m5$elpd_loo)## Warning: Accessing elpd_loo using '$' is deprecated and will be removed in
## a future release. Please extract the elpd_loo estimate from the 'estimates'
## component instead.## Warning: Accessing elpd_loo using '$' is deprecated and will be removed in
## a future release. Please extract the elpd_loo estimate from the 'estimates'
## component instead.looic_elpd_2_se <- c(co2$loos$m4$se_elpd_loo, co2$loos$m5$se_elpd_loo)## Warning: Accessing se_elpd_loo using '$' is deprecated and will be removed in
## a future release. Please extract the se_elpd_loo estimate from the 'estimates'
## component instead.## Warning: Accessing se_elpd_loo using '$' is deprecated and will be removed in
## a future release. Please extract the se_elpd_loo estimate from the 'estimates'
## component instead.loo_table2 <- data.frame(looic_2, looic_2_se, looic_elpd_2, looic_elpd_2_se, row.names = c("m4", "m5"))loo_table2## looic_2 looic_2_se looic_elpd_2 looic_elpd_2_se
## m4 862.6091 32.31804 -431.3046 16.15902
## m5 867.4774 31.26629 -433.7387 15.63314bayes_R2(m4)## Estimate Est.Error Q2.5 Q97.5
## R2riskSum 0.2992012 0.05797203 0.18180270 0.4045294
## R2riskPerceptionSum 0.1503215 0.05213345 0.05651509 0.2566861
## R2riskBenefitSum 0.2016543 0.05547717 0.09403851 0.3077344bayes_R2(m5)## Estimate Est.Error Q2.5 Q97.5
## R2riskSum 0.3414139 0.05405201 0.22879562 0.4405954
## R2riskPerceptionSum 0.2003191 0.05350717 0.09811141 0.3057656
## R2riskBenefitSum 0.2272865 0.05433600 0.12036754 0.3313134pp_check(m5, resp = "riskSum", ndraws = 1000)
pp_check(m5, resp = "riskPerceptionSum", ndraws = 1000)
pp_check(m5, resp = "riskBenefitSum", ndraws = 1000)