Chains do not contain any samples

Hi everyone, I am new to mathematical modelling as well as Rstan and I’m trying to fit my first, pretty simplistic, compartmental model (for disease transmission using ODEs)

the model seems to compile just fine but when I try to fit the model to the data, I get an error message saying : “Stan model ‘anon_model’ does not contain samples.”

I’ve made a few adjustments to ts and the initial values, which seemed to help, but I continue to get the same error

when I look at the chains specifically, it says :

"Chain 1: Unrecoverable error evaluating the log probability at the initial value.

Chain 1: Exception: Exception: array[uni, …] index: accessing element out of range. index 2 out of range; expecting index to be between 1 and 1 (in ‘string’, line 7, column 4 to column 26) (in ‘string’, line 84, column 4 to column 68)

[1] “Error : Exception: Exception: array[uni, …] index: accessing element out of range. index 2 out of range; expecting index to be between 1 and 1 (in ‘string’, line 7, column 4 to column 26) (in ‘string’, line 84, column 4 to column 68)”

I’ve put my entire code below, if someone has any tips or notices where I might have gone wrong, I’d really appreciate the help!

```
#### MODEL FITTING WITH r STAN ####
cat(
'
functions {
real[] dtb1(real t, real[] y, real[] theta,
real[] x_r, int[] x_i) {
//define interger data//
int N = x_i[1];
int n_latent = x_i[2];
int n_infectious = x_i[3];
int n_recov = x_i[4];
int n_samples = x_i[5];
int time_seed = x_i[6];
//define real data//
real f = x_r[1]; //proportion of fast progressing cases
real r = x_r[2]; //proportion of successfully completed treatment
//define paramaters needed to solve model//
real sigma = theta[1]; //transmission rate
real mu = theta[2]; //all-cause mortality rate, with equal births
real gamma = theta[3]; //disease progression rate latent fast
real epsilon= theta[4]; //disease progression rate latent slow
real delta = theta[5]; //case detection & treatment initiation rate
real omega = theta[6]; //recovery rate (1/mean treatment duration time 2 yrs)
real chi = theta[7]; //TB specific mortality
real pi = theta[8]; //spontaneous cure rate
real rho = theta[9]; //relapse rate
//define compartments & init conditions //
real S = y[1]; //uninfected
real E = y[2]; //latent fast progressing TB
real L = y[3]; //latent slow progressing TB
real I = y[4]; //active & infectious TB
real R = y[5]; //recovered
//define states & ODE//
real dS_dt = -sigma*I*S/N - mu*S + mu*5000 + chi*I;
real dE_dt = f*sigma*I*S/N - gamma*E - mu*E;
real dL_dt = (1-f)*sigma*I*S/N - epsilon*L - mu*L;
real dI_dt = gamma*E + epsilon*L - (mu+chi)*I - pi*I - r*delta*omega*I + rho*R;
real dR_dt = r*delta*omega*I - rho*R - mu*R;
return {dS_dt, dE_dt, dL_dt, dI_dt, dR_dt};
}
}
data {
int<lower = 1> n_ts; // number of days observed
int<lower = 1> n_recov; // recovered
int<lower = 1> n_infectious;
int<lower = 1> n_lf;
int<lower = 1> n_ls;
int<lower = 1> N; // Total population size
int<lower = 1> n_samples; // number of data points to fit to
int<lower = 1> n_comp; // number of compartments
int <lower = 0> time_seed; // index when to fit the model to data
real<lower=0> f; // prop fast progressing cases
real<lower=0> r; // prop success complete Rx
real t0; //initial time point 0
real ts[n_ts];
int y[n_samples, n_comp]; // data, reported prev in ea sample
}
transformed data {
real x_r[2] = {f, r};
int x_i[1] = {N};
}
parameters {
real<lower=0> sigma;
real<lower=0> mu;
real<lower=0> gamma;
real<lower=0> epsilon;
real<lower=0> delta;
real<lower=0> omega;
real<lower=0> chi;
real<lower=0> pi;
real<lower=0> rho;
}
transformed parameters {
real y_hat[n_ts, n_comp]; // Matrix to store the integrated ODE solutions
real theta[9] = {sigma, mu, gamma, epsilon, delta, omega, chi, pi, rho};
real init[n_comp] = {N - n_lf - n_ls - n_infectious - n_recov, n_lf, n_ls, n_infectious, n_recov};
simplex [5] output[n_ts];
// Solve the ODE using integrate_ode_rk45
y_hat = integrate_ode_rk45(dtb1, init, t0, ts, theta, x_r, x_i);
for(i in time_seed+1:n_ts) output[i] = ( to_vector(y_hat[i,1:5]) ) ./ sum( to_vector(y_hat[i,1:5])); // get the proportion in each category
}
model {
// Prior distribution
sigma ~ normal(2, 1);
mu ~ normal(0.0013, 0.05);
gamma ~ normal(0.8, 0.1);
epsilon ~ normal(0.05, 0.1);
delta ~ normal(0.6, 0.1);
chi ~ normal(0.16667, 0.05);
rho ~ normal(0.05, 0.02);
pi ~ normal(0.16667, 0.1);
omega ~ normal(0.5, 0.2);
// Likelihood
for(i in time_seed+1:n_ts) y[i] ~ multinomial(output[i] );
}
',
file = "dtb1_multinom.stan", sep="", fill=T)
#data
N <- 5000
n_lf<-round(5000*0.2606)
n_ls<-round(5000*0.0652)
n_infectious<-round(5000*0.0443)
n_recov<-round(5000*0.1337)
time_seed<-1000
n_ts<-2000
f=0.8
r=0.76 ##(6/41 discontinued rx=35/41*0.9Rx success=0.76)
y <- array(c(S = N - n_lf - n_ls - n_infectious - n_recov,
E = n_lf,
L = n_ls,
I = n_infectious,
R = n_recov), dim = c(1, 5))
ts <- ts$ts
stan_d = list(n_samples = 1,
n_comp = 5,
n_lf = n_lf,
n_ls = n_ls,
n_infectious = n_infectious,
n_recov = n_recov,
n_ts= n_ts,
ts=ts,
time_seed = time_seed,
y=y,
f=f,
r=r,
t0 = 0,
N=N)
#compile stan model
model <- stan_model("dtb1_multinom.stan")
fit <- sampling(model,
data = stan_d,
iter = 2000,
chains = 4,
seed = 0)
```

Thanks again in advance!