Hi, I’m new to using map_rect and have been facing issues with trying to code the function to calculate the log-posterior of each shard with censored data (due to the limit of detection). I’m using a simulated dataset of 3 patients’ viral load with 25 data points each while in the process of coding this. So each patient is meant to be a shard. The data vLoad is the viral load on each day (1-25) for patient 1 to 3, appended one after another. The LOD looks similar, except that for each patient’s daily viral load, they have a corresponding LOD value (which is the same throughout the 25 days. E.g. 1500 for patient 1, 600 for patient 2, etc)

This is what I have done so far for the code with map_rect

```
functions {
real[] OneSys(real t, real[] y, real[] params, real[] x_r, int[] x_i) {
real dydt[5];
dydt[1] = -params[1]*y[1]*y[4];
dydt[2] = params[1]*y[1]*y[4] - params[2]*y[2];
dydt[3] = params[2]*y[2] - params[3]*y[3];
dydt[4] = 15*y[3] - 20*y[4] - params[4]*y[5]*y[4];
dydt[5] = params[5]*(y[2]+y[3])*y[5];
return dydt;
}
// function lp calculates log posterior on one shard (patient) at a time
vector lp(vector pred, real par, real[] lod, real[] xr) {
real sigma = par;
real ll;
int N = 25;
real measure = xr[1:25];
real limit = lod[1:25];
real ypred = pred[1:25];
for (i in 1:N) {
if (measure[i] <= log(limit[i])) {
ll = normal_lcdf(log(limit[i]) | ypred[i], sigma);
}
if (measure[i] > log(limit[i])) {
ll = normal_lpdf(measure[i] | ypred[i], sigma);
}
}
return [ll]';
}
}
data {
int<lower=0> L; //rows in dataset
int<lower=0> M; //no. of patients
vector[L] LOD;
real vLoad[L,1];
real y0[5];
real t0;
real ts[L];
int patID[L];
int patCount[M];
int pos_pat[M];
}
transformed data {
real x_r[0];
int x_i[0];
real xr[M,25] = {vLoad[1:25,1], vLoad[26:50,1], vLoad[51:75,1]};
real limit[M,25] = {LOD[1:25], LOD[26:50], LOD[51:75]};
}
parameters {
real<lower = 10^-10, upper = 10^-6> beta_par;
real<lower = 0, upper = 10> k_par;
real<lower = 0, upper = 1> delta_par;
real<lower = 0, upper = 1> gamma_par[M];
real<lower = 0, upper = 1> gamma_mu;
real<lower = 0, upper = 0.1> gamma_sigma;
real<lower = 0, upper = 1> omega_par[M];
real<lower = 0, upper = 1> omega_mu;
real<lower = 0, upper = 0.1> omega_sigma;
real<lower = 0, upper = 20> sigma;
}
transformed parameters {
real predVal[L];
real params[5];
real logpred[M,25];
params[1] = beta_par;
params[2] = k_par;
params[3] = delta_par;
for (m in 1:M) {
real value_m[patCount[m],5];
params[4] = gamma_par[m]; //patient-specific
params[5] = omega_par[m]; //patient-specific
value_m = integrate_ode_bdf(OneSys, y0, t0, segment(ts,pos_pat[m],patCount[m]), params, x_r, x_i);
// store predicted viral load values
for (j in 1:patCount[m]) {
predVal[pos_pat[m] + j - 1] = value_m[j,4];
}
}
logpred = {log(predVal[1:25]), log(predVal[26:50]), log(predVal[51:75])};
}
model {
gamma_par ~ normal(gamma_mu,gamma_sigma);
omega_par ~ normal(omega_mu,omega_sigma);
beta_sigma ~ cauchy(0,10^-7);
gamma_sigma ~ cauchy(0,0.01);
omega_sigma ~ cauchy(0,10^-7);
sigma ~ cauchy(0,10);
inc_per ~ normal(5,2);
target += sum(map_rect(lp, logpred, sigma, limit, xr));
}
```

I get the following error:

```
SYNTAX ERROR, MESSAGE(S) FROM PARSER:
Variable definition base type mismatch, variable declared as base type real variable definition has base type real[ ]Variable definition dimensions mismatch, definition specifies 0, declaration specifies 1 error in 'model310c6c4b6013_20220718_maprect' at line 20, column 27
-------------------------------------------------
18: real ll;
19: int N = 25;
20: real measure = xr[1:25];
^
```

I understand that this is likely a trivial problem but I’m just really stumped – Any help on this would be much appreciated! Thank you!