# test new identification options
# March 19, 2019
require(idealstan)
require(dplyr)
require(ggplot2)
bin_irt_2pl_sim <- id_sim_gen(num_person=20,num_bills=200,ordinal=F,inflate=F,
diff_sd=1,
reg_discrim_sd = 1,
absence_discrim_sd = 1)
print(bin_irt_2pl_sim@simul_data$true_person)
bin_irt_2pl_est <- id_estimate(idealdata=bin_irt_2pl_sim,
model_type=1,
fixtype='prefix',
person_sd = 3,
nchains=3,ncores=3,
id_refresh=100)
model_code <- 'functions {
real id_params(vector p, int high, int low,
real fix_high,
real fix_low,
real sd_fix,
real mean_val,
real sd_val) {
int N = num_elements(p);
real prob_dens; // hold the calculated probability density
prob_dens = 0;
// use different types of indexing depending on placement of
// values to fix in the vector
if(high>low) {
if(low>1) {
prob_dens += normal_lpdf(p[1:(low-1)]|mean_val,sd_val);
}
prob_dens += normal_lpdf(p[low]|fix_low,sd_fix);
if(high>(low+1)) {
prob_dens += normal_lpdf(p[(low+1):(high-1)]|mean_val,
sd_val);
}
prob_dens += normal_lpdf(p[high]|fix_high,
sd_fix);
if(high<N) {
prob_dens += normal_lpdf(p[(high+1):N]|mean_val,sd_val);
}
} else {
if(high>1) {
prob_dens += normal_lpdf(p[1:(high-1)]|mean_val,sd_val);
}
prob_dens += normal_lpdf(p[high]|fix_high,sd_fix);
if(low>(high+1)) {
prob_dens += normal_lpdf(p[(high+1):(low-1)]|mean_val,
sd_val);
}
prob_dens += normal_lpdf(p[low]|fix_low,
sd_fix);
if(low<N) {
prob_dens += normal_lpdf(p[(low+1):N]|mean_val,sd_val);
}
}
// return accumulated log probability
return prob_dens;
}
}
data {
}
parameters {
}
model {
}
'
test_mod <- rstan::stan_model(model_code=model_code)
rstan::expose_stan_functions(test_mod)
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