Warning: 253 divergent transitions after warmup

Modeling
1

Hi everyone,
After running the following code, I get the following warning messages, which I do not know if can cause a major problem for my model? Does this mean that the model did not reach convergence?
fit_modellong ← sampling(modellong,
data = datalist,
init = function(){
list(pAB0= -2, uAB = 0.053, uASC0 = -2, sigma = 0.01, sigmapAB = 0.01, sigmauASC = 0.01, rpAB = rnorm(indivs, 0.001, 0.01), ruASC = rnorm(indivs, 0.001, 0.01), z_init = as.array(c(1766, 654), dim = 2)) }
,iter = 2000,
chains = 1,
seed = 0, control = list (stepsize = 0.1))
Warning message:

samplingwarning
samplingwarning1038×332 19.7 KB 

functions {
  vector model3(real t, vector y, real pAB, real uAB, real uASC) {
    vector[2] dydt;  
    dydt[1] = pAB*y[2]-uAB*y[1];
    dydt[2] = -uASC*y[2];
    
   return dydt;
  }
}
data {
  int <lower=1> nobs;
  real t0;
  vector[2] y0;
  real ts[nobs];
  int <lower=1> indivs;
  real <lower=0> antib[nobs];
  //real <lower=1, upper=indivs> subj[nobs];
}
parameters {
  real pAB0;
  real <lower=0> uAB;
  real  uASC0;
  real <lower=0> sigma;
  real <lower=0> sigmapAB;
  real <lower=0> sigmauASC;
  real rpAB[indivs];
  real ruASC[indivs];
  vector<lower=0>[2] z_init;
}
model {
  vector[2] yhat[nobs];
  //prior distributions
  pAB0 ~ normal(-2, 0.5);
  uASC0 ~ normal(-2, 0.5);
  uAB ~ normal(0, 0.001);
  sigmapAB ~ inv_gamma(0.01, 0.01);
  sigmauASC ~ inv_gamma(0.01, 0.01);
  sigma ~ inv_gamma(0.01, 0.01);
  z_init[1] ~ normal(1764, 0.001);
  z_init[2] ~ normal(654, 0.001);
  for (j in 1:indivs) {
    real pAB = exp(pAB0)*exp(rpAB[j]);
    real uABt = uAB;
    real uASC = exp(uASC0)*exp(ruASC[j]);
    
    yhat = ode_rk45(model3, z_init, t0, ts, pAB, uABt, uASC);
  //likelihood
  for (i in 1:nobs) {
    antib[i] ~ normal(yhat[i,2], sigma); 
    // where mu of the r.e. is 0 or -sigmapAB/2
    rpAB[j] ~ normal(0.01, sigmapAB);
    ruASC[j] ~ normal(0.01, sigmauASC);
  }
}
}

generated quantities {
  real z_pred[nobs];
  for (t in 1:nobs){
    z_pred[t] = normal_rng(antib[t], sigma);
  }
}
2

When compiling this same code with cmdstan_model, and then using the model$sample(), I get the following warning saying that my initial state is inf.
Where can I see that this initial state can take an infinite value, if I am already specifying an initial value?

samplewarning
samplewarning1107×133 9.85 KB

3

Did you have a look at the resource mentioned in the warning message (Runtime warnings and convergence problems) and the resources linked there?

These diagnostics are definitely telling you that you haven’t reached convergence, and are also telling you that even by running for longer you are unlikely to reach convergence (divergences in particular are indicative of this).