From 38291f45671fa5ed0d2d9890592d1f5d0a5272e8 Mon Sep 17 00:00:00 2001 From: Nathaniel-Haines Date: Sun, 28 May 2023 13:44:19 -0400 Subject: [PATCH 1/2] add new functions for group means and correlations --- .../Code/R/1_Analyses/fitting_orl_joint.Rmd | 2 +- 1_IGT_PP/Code/Stan/igt_orl_joint.stan | 243 ++++++++++-------- 1_IGT_PP/Code/Stan/igt_orl_joint_indMu.stan | 88 ++++--- .../2_Fitted/Joint/orl_pp_joint_blups.rds | Bin 6196 -> 0 bytes .../Joint/orl_pp_joint_model_correlations.rds | Bin 206 -> 0 bytes .../Data/2_Fitted/Joint/orl_pp_joint_mus.rds | Bin 286 -> 0 bytes .../orl_pp_joint_posterior_distributions.tiff | Bin 36602040 -> 0 bytes .../Joint/orl_pp_joint_posterior_means.tiff | Bin 36602040 -> 0 bytes .../Joint/orl_pp_joint_trace_plots.tiff | Bin 36602040 -> 0 bytes 9 files changed, 182 insertions(+), 151 deletions(-) delete mode 100644 1_IGT_PP/Data/2_Fitted/Joint/orl_pp_joint_blups.rds delete mode 100644 1_IGT_PP/Data/2_Fitted/Joint/orl_pp_joint_model_correlations.rds delete mode 100644 1_IGT_PP/Data/2_Fitted/Joint/orl_pp_joint_mus.rds delete mode 100644 1_IGT_PP/Figs_Tables/Joint/orl_pp_joint_posterior_distributions.tiff delete mode 100644 1_IGT_PP/Figs_Tables/Joint/orl_pp_joint_posterior_means.tiff delete mode 100644 1_IGT_PP/Figs_Tables/Joint/orl_pp_joint_trace_plots.tiff diff --git a/1_IGT_PP/Code/R/1_Analyses/fitting_orl_joint.Rmd b/1_IGT_PP/Code/R/1_Analyses/fitting_orl_joint.Rmd index 795474e..c9381b1 100644 --- a/1_IGT_PP/Code/R/1_Analyses/fitting_orl_joint.Rmd +++ b/1_IGT_PP/Code/R/1_Analyses/fitting_orl_joint.Rmd @@ -24,7 +24,7 @@ library(miscTools) ## Load Data ```{r} -stan_data = readRDS(here("1_IGT_PP", "Data", "1_Preprocessed", "List_Sess1&2.rds")) +stan_data = readRDS(here("1_IGT_PP", "Data", "1_Preprocessed", "stan_ready_ORL_joint_retest.rds")) full_data = readRDS(here("1_IGT_PP", "Data", "1_Preprocessed", "Dataframe_Sess1&2.rds")) ``` diff --git a/1_IGT_PP/Code/Stan/igt_orl_joint.stan b/1_IGT_PP/Code/Stan/igt_orl_joint.stan index e3ec798..5fe6cf5 100644 --- a/1_IGT_PP/Code/Stan/igt_orl_joint.stan +++ b/1_IGT_PP/Code/Stan/igt_orl_joint.stan @@ -1,10 +1,54 @@ +functions { + // to compute empirical correlation matrix given input matrix + matrix empirical_correlation(matrix X) { + int N = rows(X); + int M = cols(X); + matrix[N, M] centered; + vector[M] std_devs; + matrix[M, M] Sigma; + matrix[M, M] R; + + // calculate covariance matrix + for (j in 1:M) { + centered[,j] = X[,j] - mean(X[,j]); + } + Sigma = (centered' * centered) / (N - 1); + + // Calculate correlation matrix + std_devs = sqrt(diagonal(Sigma)); + for (i in 1:M) { + for (j in 1:M) { + R[i,j] = Sigma[i,j] / (std_devs[i] * std_devs[j]); + } + } + + return R; + } + + real Phi_approx_group_mean_rng(real mu, real sigma, int n_samples) { + vector[n_samples] par; + for (i in 1:n_samples) { + par[i] = Phi_approx(normal_rng(mu, sigma)); + } + return mean(par); + } + + matrix Phi_approx_corr_rng(vector mu, vector sigma, matrix R, int n_samples) { + int N = rows(R); + int M = cols(R); + matrix[N,M] Sigma = quad_form_diag(R, sigma); + matrix[n_samples, M] X_sim; + for (i in 1:n_samples) { + X_sim[i,] = Phi_approx(multi_normal_rng(mu, Sigma)'); + } + return empirical_correlation(X_sim); + } +} - -// --------------------------------------------------------------------------------------- -data{ - int N; // Number of participants =49 - int S; // Number of sessions = 2 - int T; // Total possile number of trials = 120 +data { + int N; // Number of participants + int S; // Number of sessions + int T; // Total possile number of trials int card[N,T,S]; // Cards presented on each trial int Tsubj[N,S]; // Total number of trials presented to each subject on each session int choice[N,T,S]; // Choices on each trial @@ -13,10 +57,9 @@ data{ } -// --------------------------------------------------------------------------------------- -parameters{ +parameters { // Declare parameters - // Hyper(group)-parameters - these are & sigmas, respectively, for each parameter + // Hyper(group)-parameters matrix[S, 5] mu_p; // S (number of sessions) x 5 (number of parameters) matrix of mus vector[2] sigma_Arew; vector[2] sigma_Apun; @@ -25,10 +68,10 @@ parameters{ vector[2] sigma_betaP; // Subject-level "raw" parameters - i.e., independent/uncorrelated & normally distributed person-level (random-)effects - // Note, these are S (number of sessions) x J (number of subjects) matrices - matrix[S,N] Arew_pr; // Untransformed Arews - matrix[S,N] Apun_pr; // Untransformed Apunss - matrix[S,N] K_pr; // Untransformed Ks + // Note, these are S (number of sessions) x N (number of subjects) matrices + matrix[S,N] Arew_pr; + matrix[S,N] Apun_pr; + matrix[S,N] K_pr; matrix[S,N] betaF_pr; matrix[S,N] betaP_pr; @@ -40,16 +83,13 @@ parameters{ cholesky_factor_corr[2] R_chol_betaP; } - -// --------------------------------------------------------------------------------------- -transformed parameters{ -// ------------------------------- +transformed parameters { // Declare transformed parameters // Parameters to use in the reinforcement-learning algorithm // Note, for each of these, we include the mus and the subject-level parameters (see below), allowing for shrinkage and subject-to-subject variability - matrix[N,S] Arew; // Transformed Arews - matrix[N,S] Apun; // Transformed Apuns - matrix[N,S] K; // Transformed Ks + matrix[N,S] Arew; + matrix[N,S] Apun; + matrix[N,S] K; matrix[N,S] betaF; matrix[N,S] betaP; @@ -60,8 +100,7 @@ transformed parameters{ matrix[S,N] betaF_tilde; matrix[S,N] betaP_tilde; -// ------------------------------- -// Calculate transformed parameters + // Calculate transformed parameters // Untransformed subject-level parameters incorporating correlation between sessions Arew_tilde = diag_pre_multiply(sigma_Arew, R_chol_Arew) * Arew_pr; @@ -87,11 +126,19 @@ transformed parameters{ } } - -// --------------------------------------------------------------------------------------- -model{ -// ------------------------------- -// Priors +model { + // Declare variables to calculate utility after each trial: These 4 (number of cards) x 2 (playing vs. not playing) matrices + matrix[4,2] ef; + matrix[4,2] ev; + matrix[4,2] pers; + matrix[4,2] utility; + + real PEval; + real PEfreq; + real PEfreq_fic; + real K_tr; + + // Priors // Hyperparameters for correlations R_chol_Arew ~ lkj_corr_cholesky(1); R_chol_Apun ~ lkj_corr_cholesky(1); @@ -115,32 +162,20 @@ model{ to_vector(betaF_pr) ~ normal(0, 1.0); to_vector(betaP_pr) ~ normal(0, 1.0); - // Declare variables to calculate utility after each trial: These 4 (number of cards) x 2 (playing vs. not playing) matrices - matrix[4,2] ef; - matrix[4,2] ev; - matrix[4,2] pers; - matrix[4,2] utility; - - real PEval; - real PEfreq; - real PEfreq_fic; - real K_tr; - -// ------------------------------- - for(i in 1:N){ // Loop through individual participants - for(s in 1:S){ // Loop though sessions for participant i - if(Tsubj[i,s] > 0){ // If we have data for participant i on session s, run through RL algorithm + for (i in 1:N) { // Loop through individual participants + for (s in 1:S) { // Loop though sessions for participant i + if (Tsubj[i,s] > 0) { // If we have data for participant i on session s, run through RL algorithm // Initialize starting values K_tr = pow(3, K[i,s]) - 1; - for(pp in 1:2){ // Looping over play/pass columns and assigning each 0 to each card + for (pp in 1:2) { // Looping over play/pass columns and assigning each 0 to each card ev[:,pp] = rep_vector(0,4); ef[:,pp] = rep_vector(0,4); pers[:,pp] = rep_vector(0,4); utility[:,pp] = rep_vector(0,4); } - for(t in 1:Tsubj[i,s]){ // Run through RL algorithm trial-by-trial + for (t in 1:Tsubj[i,s]) { // Run through RL algorithm trial-by-trial // Likelihood - predict choice as a function of utility choice[i,t,s] ~ categorical_logit(to_vector(utility[card[i,t,s], :])); @@ -150,7 +185,7 @@ model{ PEfreq = sign[i,t,s] - ef[card[i,t,s], choice[i,t,s]]; // Win-frequency prediction error PEfreq_fic = -sign[i,t,s]/1 - ef[card[i,t,s], (3-choice[i,t,s])]; // Lose-frequency prediction error? - if(outcome[i,t,s] >= 0){ // If participant DID NOT lose + if (outcome[i,t,s] >= 0) { // If participant DID NOT lose // Update expected win-frequency of what participant DID NOT chose to do ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Apun[i,s] * PEfreq_fic; // Update what participant chose @@ -176,11 +211,18 @@ model{ } } +generated quantities { + // Hyper(group)-parameters - these are 5 (number of parameters) x S (number of sessions) matrix of mus & sigmas, respectively, for each parameter + vector[2] mu_Arew; + vector[2] mu_Apun; + vector[2] mu_K; + vector[2] mu_betaF; + vector[2] mu_betaP; + + // For posterior predictive check + real choice_pred[N,T,S]; -// --------------------------------------------------------------------------------------- -generated quantities { // STILL NEED TO FIGURE OUT HOW EXACTLY THIS BLOCK WORKS, PARTICULARLY WITH THE PPC -// ------------------------------------ -// test-retest correlations + // test-retest correlations corr_matrix[2] R_Arew; corr_matrix[2] R_Apun; corr_matrix[2] R_K; @@ -189,46 +231,31 @@ generated quantities { // STILL NEED TO FIGURE OUT HOW EXACTLY THIS BLOCK WORKS, // Reconstruct correlation matrix from cholesky factor // Note that we're multipling the cholesky factor by its transpose which gives us the correlation matrix - R_Arew = R_chol_Arew * R_chol_Arew'; - R_Apun = R_chol_Apun * R_chol_Apun'; - R_K = R_chol_K * R_chol_K'; + R_Arew = Phi_approx_corr_rng(mu_p[,1], sigma_Arew, R_chol_Arew * R_chol_Arew', 10000); + R_Apun = Phi_approx_corr_rng(mu_p[,2], sigma_Apun, R_chol_Apun * R_chol_Apun', 10000); + R_K = Phi_approx_corr_rng(mu_p[,3], sigma_K, R_chol_K * R_chol_K', 10000); R_betaF = R_chol_betaF * R_chol_betaF'; R_betaP = R_chol_betaP * R_chol_betaP'; -// ------------------------------------ -// For posterior predictive check? - // Hyper(group)-parameters - these are 5 (number of parameters) x S (number of sessions) matrix of mus & sigmas, respectively, for each parameter - vector[2] mu_Arew; - vector[2] mu_Apun; - vector[2] mu_K; - vector[2] mu_betaF; - vector[2] mu_betaP; - - // For posterior predictive check - real choice_pred[N,T,S]; - // Set all posterior predictions to -1 (avoids NULL values) - for(i in 1:N) { - for(s in 1:S){ - for(t in 1:T){ + for (i in 1:N) { + for (s in 1:S) { + for (t in 1:T) { choice_pred[i,t,s] = -1; } } } - // Group-level means - for(s in 1:S){ - mu_Arew[s] = Phi_approx(mu_p[s, 1]); - mu_Apun[s] = Phi_approx(mu_p[s, 2]); - mu_K[s] = Phi_approx(mu_p[s, 3]) * 5; + // Compute group-level means + for (s in 1:S) { + mu_Arew[s] = Phi_approx_group_mean_rng(mu_p[s, 1], sigma_Arew[s], 10000); + mu_Apun[s] = Phi_approx_group_mean_rng(mu_p[s, 2], sigma_Apun[s], 10000); + mu_K[s] = Phi_approx_group_mean_rng(mu_p[s, 3], sigma_K[s], 10000) * 5; mu_betaF[s] = mu_p[s, 4]; mu_betaP[s] = mu_p[s, 5]; } - // ------------------------------------ { // local section, this saves time and space - // ------------------------------- - // Declare variables to calculate utility after each trial: These 4 (number of cards) x 2 (playing vs. not playing) matrices matrix[4,2] ef; matrix[4,2] ev; @@ -240,50 +267,48 @@ generated quantities { // STILL NEED TO FIGURE OUT HOW EXACTLY THIS BLOCK WORKS, real PEfreq_fic; real K_tr; - // ------------------------------- - for(i in 1:N){ // Loop through individual participants - for(s in 1:S){ // Loop though sessions for participant i - if(Tsubj[i,s] > 0){ // If we have data for participant i on session s, run through RL algorithm + for (i in 1:N) { // Loop through individual participants + for (s in 1:S) { // Loop though sessions for participant i + if (Tsubj[i,s] > 0) { // If we have data for participant i on session s, run through RL algorithm // Initialize starting values K_tr = pow(3, K[i,s]) - 1; - for(pp in 1:2){ // Looping over play/pass columns and assigning each 0 to each card + for (pp in 1:2) { // Looping over play/pass columns and assigning each 0 to each card ev[:,pp] = rep_vector(0,4); ef[:,pp] = rep_vector(0,4); pers[:,pp] = rep_vector(0,4); utility[:,pp] = rep_vector(0,4); } - for(t in 1:Tsubj[i,s]){ // Run through RL algorithm trial-by-trial + for (t in 1:Tsubj[i,s]) { // Run through RL algorithm trial-by-trial + // Likelihood - predict choice as a function of utility + choice_pred[i,t,s] = categorical_rng(softmax(to_vector(utility[card[i,t,s], :]))); - // Likelihood - predict choice as a function of utility - choice_pred[i,t,s] = categorical_rng(softmax(to_vector(utility[card[i,t,s], :]))); - - // After choice, calculate prediction error - PEval = outcome[i,t,s] - ev[card[i,t,s], choice[i,t,s]]; // Value prediction error - PEfreq = sign[i,t,s] - ef[card[i,t,s], choice[i,t,s]]; // Win-frequency prediction error - PEfreq_fic = -sign[i,t,s]/1 - ef[card[i,t,s], (3-choice[i,t,s])]; // Lose-frequency prediction error? - - if(outcome[i,t,s] >= 0){ // If participant DID NOT lose - // Update expected win-frequency of what participant DID NOT chose to do - ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Apun[i,s] * PEfreq_fic; - // Update what participant chose - ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEfreq; - ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEval; - } else { // If participant DID lose - // Update expected win-frequency of what participant DID NOT choose to do - ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Arew[i,s] * PEfreq_fic; - // Update what participant chose - ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEfreq; - ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEval; - } - - // Update perseverance - pers[card[i,t,s], choice[i,t,s]] = 1; - pers = pers / (1 + K_tr); - - // Calculate expected value of card - utility = ev + ef * betaF[i,s] + pers * betaP[i,s]; + // After choice, calculate prediction error + PEval = outcome[i,t,s] - ev[card[i,t,s], choice[i,t,s]]; // Value prediction error + PEfreq = sign[i,t,s] - ef[card[i,t,s], choice[i,t,s]]; // Win-frequency prediction error + PEfreq_fic = -sign[i,t,s]/1 - ef[card[i,t,s], (3-choice[i,t,s])]; // Lose-frequency prediction error? + + if (outcome[i,t,s] >= 0) { // If participant DID NOT lose + // Update expected win-frequency of what participant DID NOT chose to do + ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Apun[i,s] * PEfreq_fic; + // Update what participant chose + ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEfreq; + ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEval; + } else { // If participant DID lose + // Update expected win-frequency of what participant DID NOT choose to do + ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Arew[i,s] * PEfreq_fic; + // Update what participant chose + ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEfreq; + ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEval; + } + + // Update perseverance + pers[card[i,t,s], choice[i,t,s]] = 1; + pers = pers / (1 + K_tr); + + // Calculate expected value of card + utility = ev + ef * betaF[i,s] + pers * betaP[i,s]; } } } diff --git a/1_IGT_PP/Code/Stan/igt_orl_joint_indMu.stan b/1_IGT_PP/Code/Stan/igt_orl_joint_indMu.stan index d7ee1cc..96247d8 100644 --- a/1_IGT_PP/Code/Stan/igt_orl_joint_indMu.stan +++ b/1_IGT_PP/Code/Stan/igt_orl_joint_indMu.stan @@ -1,4 +1,14 @@ -data{ +functions { + real Phi_approx_group_mean_rng(real mu, real sigma, int n_samples) { + vector[n_samples] par; + for (i in 1:n_samples) { + par[i] = Phi_approx(normal_rng(mu, sigma)); + } + return mean(par); + } +} + +data { int N; // Number of participants int S; // Number of sessions int T; // Total possile number of trials @@ -10,9 +20,7 @@ data{ int day[N,S]; // num days from day 0 predictor for linear model } - -// --------------------------------------------------------------------------------------- -parameters{ +parameters { // Declare parameters // Hyper(group)-parameters matrix[2,5] mu_pr; // 2 (intercept and slope) x 5 (number of parameters) matrix of mus @@ -39,7 +47,7 @@ parameters{ cholesky_factor_corr[2] R_chol_betaP; } -transformed parameters{ +transformed parameters { // Declare transformed parameters matrix[N,2] Arew; // Transformed Arews matrix[N,2] Apun; // Transformed Apuns @@ -73,7 +81,7 @@ transformed parameters{ } } -model{ +model { // Declare variables to calculate utility after each trial: These 4 (number of cards) x 2 (playing vs. not playing) matrices matrix[4,2] ef; matrix[4,2] ev; @@ -126,34 +134,34 @@ model{ for (t in 1:Tsubj[i,s]) { - // Likelihood - predict choice as a function of utility - choice[i,t,s] ~ categorical_logit(to_vector(utility[card[i,t,s], :])); - - // After choice, calculate prediction error - PEval = outcome[i,t,s] - ev[card[i,t,s], choice[i,t,s]]; // Value prediction error - PEfreq = sign[i,t,s] - ef[card[i,t,s], choice[i,t,s]]; // Win-frequency prediction error - PEfreq_fic = -sign[i,t,s]/1 - ef[card[i,t,s], (3-choice[i,t,s])]; // Lose-frequency prediction error? - - if (outcome[i,t,s] >= 0){ // If participant DID NOT lose - // Update expected win-frequency of what participant DID NOT chose to do - ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Apun[i,s] * PEfreq_fic; - // Update what participant chose - ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEfreq; - ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEval; - } else { // If participant DID lose - // Update expected win-frequency of what participant DID NOT choose to do - ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Arew[i,s] * PEfreq_fic; - // Update what participant chose - ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEfreq; - ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEval; - } - - // Update perseverance - pers[card[i,t,s], choice[i,t,s]] = 1; - pers = pers / (1 + K_tr); - - // Calculate expected value of card - utility = ev + ef * betaF[i,s] + pers * betaP[i,s]; + // Likelihood - predict choice as a function of utility + choice[i,t,s] ~ categorical_logit(to_vector(utility[card[i,t,s], :])); + + // After choice, calculate prediction error + PEval = outcome[i,t,s] - ev[card[i,t,s], choice[i,t,s]]; // Value prediction error + PEfreq = sign[i,t,s] - ef[card[i,t,s], choice[i,t,s]]; // Win-frequency prediction error + PEfreq_fic = -sign[i,t,s]/1 - ef[card[i,t,s], (3-choice[i,t,s])]; // Lose-frequency prediction error? + + if (outcome[i,t,s] >= 0){ // If participant DID NOT lose + // Update expected win-frequency of what participant DID NOT chose to do + ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Apun[i,s] * PEfreq_fic; + // Update what participant chose + ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEfreq; + ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Arew[i,s] * PEval; + } else { // If participant DID lose + // Update expected win-frequency of what participant DID NOT choose to do + ef[card[i,t,s], (3-choice[i,t,s])] = ef[card[i,t,s], (3-choice[i,t,s])] + Arew[i,s] * PEfreq_fic; + // Update what participant chose + ef[card[i,t,s], choice[i,t,s]] = ef[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEfreq; + ev[card[i,t,s], choice[i,t,s]] = ev[card[i,t,s], choice[i,t,s]] + Apun[i,s] * PEval; + } + + // Update perseverance + pers[card[i,t,s], choice[i,t,s]] = 1; + pers = pers / (1 + K_tr); + + // Calculate expected value of card + utility = ev + ef * betaF[i,s] + pers * betaP[i,s]; } } } @@ -187,8 +195,8 @@ generated quantities { // Set all posterior predictions to -1 (avoids NULL values) for (i in 1:N) { - for (s in 1:S){ - for (t in 1:T){ + for (s in 1:S) { + for (t in 1:T) { choice_pred[i,t,s] = -1; } } @@ -196,15 +204,14 @@ generated quantities { // Group-level means for (s in 1:S) { - mu_Arew[s] = Phi_approx(mu_pr[s, 1]); - mu_Apun[s] = Phi_approx(mu_pr[s, 2]); - mu_K[s] = Phi_approx(mu_pr[s, 3]) * 5; + mu_Arew[s] = Phi_approx_group_mean_rng(mu_pr[s, 1], sigma_Arew[s], 10000); + mu_Apun[s] = Phi_approx_group_mean_rng(mu_pr[s, 2], sigma_Apun[s], 10000); + mu_K[s] = Phi_approx_group_mean_rng(mu_pr[s, 3], sigma_K[s], 10000)*5; mu_betaF[s] = mu_pr[s, 4]; mu_betaP[s] = mu_pr[s, 5]; } { // local section, this saves time and space - // Declare variables to calculate utility after each trial: These 4 (number of cards) x 2 (playing vs. not playing) matrices matrix[4,2] ef; matrix[4,2] ev; @@ -216,7 +223,6 @@ generated quantities { real PEfreq_fic; real K_tr; - // ------------------------------- for (i in 1:N) { for (s in 1:S) { if (Tsubj[i,s] > 0) { 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