Generated Quantities returns error "Mismatch between model and fitted_parameters csv file"

Interfaces CmdStan
1

I’m attempting to use the generate_quantities function to predict on some new data. I’ve fit my initial model using cmdstanr

fit <- model$sample(model_data)

and have written a new model to perform the generated quantities, as in the documentation for the function here.

pred_model$generate_quantities(fit, data = pred_data)

When I do this, my chains finish unexpectedly with the error “Mismatch between model and fitted_parameters csv file”.

What does this mean? Where does this mismatch typically occur?

Here are the associated stan models

First, the model I use to fit my data

data{
  int n; //Total number of observations
  int subjectids[n]; //Subject idendification number as an integer.  Mine go from 1 - 36
  int n_subjectids; //How many unique subjects do I have?
  vector[n] time; //time at which subjects were observed?  Length N
  real yobs[n]; //Observed concentraitons
  
  //Covars
  vector[n] sex;
  vector[n] weight;
  vector[n] creatinine;
  vector[n] age;
  vector[n] D;


}
parameters{
  
  real<lower=0>  mu_cl;
  real<lower=0> s_cl;                                                   
  vector[n_subjectids] z_cl;
  
  real<lower=0> mu_tmax;
  real<lower=0> s_t;
  vector[n_subjectids] z_t;

  real<lower=0, upper=1> phi;
  real<lower=0, upper=1> kappa;
  vector<lower=0, upper=1>[n_subjectids] delays;
  
  real<lower=0> sigma;
  
  real mu_alpha;
  real<lower=0> s_alpha;
  vector[n_subjectids] z_alpha;
  
  real beta_cl_sex;
  real beta_cl_weight;
  real beta_cl_creatinine;
  real beta_cl_age;
  
  
}
transformed parameters{
  vector<lower=0>[n] Cl = exp(mu_cl + z_cl[subjectids]*s_cl + beta_cl_sex*sex + beta_cl_weight*weight + beta_cl_creatinine*creatinine + beta_cl_age*age);
  vector<lower=0>[n] t = exp(mu_tmax + z_t[subjectids]*s_t);
  vector<lower=0, upper=1>[n] alpha = inv_logit(mu_alpha + z_alpha[subjectids]*s_alpha);
  vector<lower=0>[n]ka = log(alpha)./(t .* (alpha-1));
  vector<lower=0>[n] ke = alpha .* log(alpha)./(t .* (alpha-1));
  vector<lower=0>[n] delayed_time = time - 0.5*delays[subjectids];
   
  vector<lower=0>[n] C = (0.5*D ./ Cl) .* (ke .* ka) ./ (ke - ka) .* (exp(-ka .* delayed_time) -exp(-ke .* delayed_time));
}
model{
  mu_tmax ~ normal(log(3.3), 0.25);
  s_t ~ gamma(10, 100);
  z_t ~ normal(0,1);
  
  mu_cl ~ normal(log(3.3),0.15);
  s_cl ~ gamma(15,100);
  z_cl ~ normal(0,1);
  
  
  mu_alpha ~ normal(0,1);
  s_alpha ~ gamma(10, 100);
  z_alpha ~ normal(0,1);
  
  
  phi ~ beta(20,20);
  kappa ~ beta(20,20);
  delays ~ beta(phi/kappa, (1-phi)/kappa);
  
  beta_cl_sex ~ student_t(3,0,2.5);
  beta_cl_weight ~ student_t(3,0,2.5);
  beta_cl_creatinine ~ student_t(3,0,2.5);
  
  sigma ~ lognormal(log(0.1), 0.2);
  yobs ~ lognormal(log(C), sigma);
}

And now, the model I use for generated quantities


data{
  int n; //Total number of observations
  int subjectids[n]; //Subject idendification number as an integer.  Mine go from 1 - 36
  int n_subjectids; //How many unique subjects do I have?
  vector[n] time; //time at which subjects were observed?  Length N
  real yobs[n]; //Observed concentraitons
  
  //Covars
  vector[n] sex;
  vector[n] weight;
  vector[n] creatinine;
  vector[n] age;
  vector[n] D;


}
parameters{
  
  real<lower=0>  mu_cl;
  real<lower=0> s_cl;                                                   
  vector[n_subjectids] z_cl;
  
  real<lower=0> mu_tmax;
  real<lower=0> s_t;
  vector[n_subjectids] z_t;

  real<lower=0, upper=1> phi;
  real<lower=0, upper=1> kappa;
  vector<lower=0, upper=1>[n_subjectids] delays;
  
  real<lower=0> sigma;
  
  real mu_alpha;
  real<lower=0> s_alpha;
  vector[n_subjectids] z_alpha;
  
  real beta_cl_sex;
  real beta_cl_weight;
  real beta_cl_creatinine;
  real beta_cl_age;
  
  
}

generated quantities{
  vector<lower=0>[n] Cl = exp(mu_cl + beta_cl_sex*sex + beta_cl_weight*weight + beta_cl_creatinine*creatinine + beta_cl_age*age);
  real<lower=0> t = exp(mu_tmax);
  real<lower=0> alpha = inv_logit(mu_alpha);
  real<lower=0> ka = log(alpha)/(t * (alpha-1));
  real<lower=0> ke = alpha * ka;
  vector<lower=0>[n] delayed_time = time - 0.5*phi;
   
  vector<lower=0>[n] C = (0.5*D ./ Cl) * (ke * ka) / (ke - ka) .* (exp(-ka * delayed_time) -exp(-ke * delayed_time));
}