StanDDM_NCEN_Pure <- function(simulation, modal, data, control, seed, warmup, num_cores, iter){
model_name <- paste('StanDDM_NCEN_Pure', modal, sep="") #modal assigned in main
dir.create(paste(model_name, sep=''), showWarnings = FALSE)
cat('\n///////////////Computing model: ', model_name, '///////////////\n')
if(simulation){
cat('\nCreating random parameters and simulating Data...\n')
fakeParams <- makeFakeParams(nsub = 10, include = c())
fakedat <- simulDat(fakeParams, n = 50)
fakedat <- fakedat[[1]]
data <- fake_data_processing(fakedat$data)
}
model_definition <-
"
data {
int<lower=1> N; // Number of subjects
int<lower=0> Nu_max; // Max (across subjects) number of upper boundary responses
int<lower=0> Nl_max; // Max (across subjects) number of lower boundary responses
int<lower=0> Nu[N]; // Number of upper boundary responses for each subj
int<lower=0> Nl[N]; // Number of lower boundary responses for each subj
real RTu[N, Nu_max]; // upper boundary response times
real RTl[N, Nl_max]; // lower boundary response times
real minRT[N]; // minimum RT for each subject of the observed data
real RTbound; // lower bound or RT across all subjects (e.g., 0.1 second)
}
parameters {
vector[4] mu_p;
vector<lower=0>[4] sigma;
// Subject-level raw parameters (for Matt trick)
vector[N] alpha_pr;
vector[N] beta_pr;
vector[N] delta_pr;
vector[N] tau_pr;
}
transformed parameters {
// Transform subject-level raw parameters
vector<lower=0>[N] alpha; // boundary separation
vector<lower=0, upper=1>[N] beta; // initial bias
vector<lower=0>[N] delta; // drift rate
vector<lower=RTbound, upper=max(minRT)>[N] tau; // nondecision time
for (i in 1:N) {
beta[i] = Phi_approx(mu_p[2] + sigma[2] * beta_pr[i]); //Phi approx so bounded between 0 and 1
tau[i] = Phi_approx(mu_p[4] + sigma[4] * tau_pr[i]) * (minRT[i]-RTbound) + RTbound; //non decision time //needs to be necessarily smaller than the RT. The bound makes sure that the sampled min non dec time stays //below the smallest RT by the order of the padding, which gets added after the sampling is done(tis sis why //there is a sum at the end. Phi approx = inverse probit.)
}
alpha = exp(mu_p[1] + sigma[1] * alpha_pr); //reparametrization as in Gelman manual second ed pg 313 and Kruschkes manual pg. 281
delta = exp(mu_p[3] + sigma[3] * delta_pr); //exponential link so only positive values, hard limit on 0
}
model {
// Hyperparameters
mu_p ~ normal(0, 1);
sigma ~ cauchy(0, 5);
// Individual parameters for non-centered parameterization
alpha_pr ~ normal(0, 1);
beta_pr ~ normal(0, 1);
delta_pr ~ normal(0, 1);
tau_pr ~ normal(0, 1);
// Begin subject loop
for (i in 1:N) {
// Response time distributed along wiener first passage time distribution
RTu[i, :Nu[i]] ~ wiener(alpha[i], tau[i], beta[i], delta[i]);
RTl[i, :Nl[i]] ~ wiener(alpha[i], tau[i], 1-beta[i], -delta[i]);
} // end of subject loop
}
generated quantities {
// For log likelihood calculation
real log_lik[N];
{ // local section, this saves time and space
// Begin subject loop
for (i in 1:N) {
log_lik[i] = wiener_lpdf(RTu[i, :Nu[i]] | alpha[i], tau[i], beta[i], delta[i]);
log_lik[i] = log_lik[i] + wiener_lpdf(RTl[i, :Nl[i]] | alpha[i], tau[i], 1-beta[i], -delta[i]);
}
}
}
"
inits_fixed <- c(0.5, 0.5, 0.5, 0.15)
init <- lapply(1:num_cores, function(i) {
list(
mu_p = c(log(inits_fixed[1]), qnorm(inits_fixed[2]), log(inits_fixed[3]),
qnorm(inits_fixed[4])),
sigma = c(1.0, 1.0, 1.0, 1.0),
alpha_pr = rep(log(inits_fixed[1]), data$forstan$N),
beta_pr = rep(qnorm(inits_fixed[2]), data$forstan$N),
delta_pr = rep(log(inits_fixed[3]), data$forstan$N),
tau_pr = rep(qnorm(inits_fixed[4]), data$forstan$N)
)
})
pars <- c("alpha", "beta", "delta", "tau", "log_lik")
cat('\nCompiling and fitting the model...\n')
#-----------------------------------------------------------------
fit <- stan(model_code = model_definition,
seed = seed,
data = data$forstan,
pars = pars,
warmup = warmup,
cores = num_cores,
control = control,
init = init,
iter = iter,
chains = num_cores)
cat('\nSaving Stanfit object...\n')
#-----------------------------------------------------------------
saveit(fit = fit,
string = model_name,
file=paste(model_name, '/', model_name, '.RData', sep = ''))
cat('\nExtracting and saving parameters...\n')
#-----------------------------------------------------------------
extracted_params <- parameter_extraction(
fit,
numsub=data$forstan$N,
names=data$forsim$names)
names(extracted_params) <- c('a', 'z', 'v', 't') #dependent on model
write.csv(extracted_params, file = paste(model_name, '/',model_name,".csv", sep = ''),
row.names=FALSE)
if(simulation){ #needs model_name, else that would be in main
write.csv(fakeParams, file = paste(model_name, '/',model_name,"_template_params.csv",
sep = ''), row.names=FALSE)
}
cat(paste('\nSimulating', data$forstan$N, 'subjects with fitted parameters:\n'))
#-----------------------------------------------------------------
sims <- simulDat(extracted_params)
sims <- sims[[1]]
cat(paste('\nSaving Plots...\n'))
#-----------------------------------------------------------------
models_plots(experim_dat = data$forsim$rawdat,
simul_dat = sims,
model_name = model_name)
fit_quality(experim_dat = data$forsim$rawdat,
simul_dat = sims,
model_name = model_name)
if(simulation){
param_recovery(fit, data$forstan$N, fakeParams, model_name = model_name)
}
cat(paste('\nComputing/plotting model diagnostics:\n'))
#-----------------------------------------------------------------
model_diagnostics(fit)
cat(paste('\nFor Reproducibility:\n'))
#-----------------------------------------------------------------
print(devtools::session_info())
cat(paste('Seed:', seed, sep = ' '))
cat('\n///////////////', model_name, 'has been computed.///////////////\n')
}#end function
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