Hello,

I’m trying to fit a Stan model shown here:

```
data {
int<lower=0> n_person;
int<lower=0> n_item;
int<lower = 1> D;
int<lower=0, upper = 1> Y[n_person,n_item];
real<lower=0> t[n_person,n_item];
vector[D] Zero;
}
parameters {
vector[n_person] theta;
vector[n_person] tau;
corr_matrix[2] correlP;
vector<lower=0>[D] sigmaP;
vector[n_item] b;
vector[n_item] beta;
real mu_b;
real mu_beta;
real<lower=0> sigma_b;
real<lower=0> sigma_beta;
}
transformed parameters{
matrix[D, n_person] PersPar[n_person];
cov_matrix[D] SigmaP;
SigmaP=quad_form_diag(correlP, sigmaP);
PersPar[1] = theta;
PersPar[2] = tau;
}
model {
PersPar~ multi_normal(Zero,SigmaP);
sigmaP ~ cauchy(0,5);
correlP ~ lkj_corr(1);
b ~ normal(mu_b,sigma_b);
beta ~ normal(mu_beta,sigma_beta);
sigma_b ~ cauchy(0,5);
sigma_beta ~ cauchy(0,5);
for(i in 1:n_person){
for (j in 1:n_item){
Y[i,j] ~ bernoulli(inv_logit(theta[i] - b[j]));
t[i,j] ~ lognormal(beta[j] - tau[i], sigma_beta);
}}
}
```

However, I’m getting a mismatch between the matrix and the vector in rows 27 and 28. What is the proper way to write this code?

The data which is used here can be generated with the R code:

```
library(tidyverse)
library(magrittr)
Y <- list()
t <- list()
p <- rep(seq(.2, .8, .2),5)
for(i in 1:20){
Y[[i]] <- rbinom(500, 1, p[i]) %>% as.numeric()
t [[i]] <- rlnorm(n = 500, meanlog = log(4), sdlog = log(1.5)) %>% as.numeric()
}
Y %<>% transpose() %>%
map(unlist)
t %<>% transpose() %>%
map(unlist)
dfs <- list("Y" = Y,
"t" = t)
data <- list("n_person" = length(Y),
"n_item" = length(Y[[1]]),
"D" = length(dfs),
"Y" = dfs$Y,
"t" = dfs$t,
"Zero" = rep(0, length(dfs)))
```