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));
}
```