I am trying to run a model in rstan saving only a few parameters, as otherwise the output becomes really big. However, when I use the pars and include arguments I get the following error:

some chains had errors; consider specifying chains = 1 to debughere are whatever error messages > were returned

[[1]]

Stan model ‘83da1bf7b504e96df52c6e00997ecc57’ does not contain samples.[[2]]

Stan model ‘83da1bf7b504e96df52c6e00997ecc57’ does not contain samples.[[3]]

Stan model ‘83da1bf7b504e96df52c6e00997ecc57’ does not contain samples.[[4]]

Stan model ‘83da1bf7b504e96df52c6e00997ecc57’ does not contain samples.

Below is my model:

```
model_code <- '
functions {
real fplBPosRange(real logX, real logBase, real A, real B, real logC, real D) {
return exp(log(A))/(1+logBase^(-B*(logX-logC)))+D;
}
}
data {
int<lower=1> N; // size of dataset
int<lower=1> nSamples; // number of samples
int<lower=1, upper=nSamples> sample[N]; // sample
vector[N] logX; // dose value in logarithmic scale
real y[N]; // Response values
int<lower=1> nBlocks; // number of blocks
int<lower=1,upper=nBlocks> cultureBlock[N]; //block
real targetPotencies[nSamples];
real<lower=0> sigma; // residual
int<lower = 0, upper = 1> run_estimation; // a switch to evaluate the likelihood
}
parameters {
vector[nBlocks] eta[nSamples]; // log(EC50) for each sample in block
real meanLogC[nSamples];
real<lower=0> sigmaLogC; // standard deviation of the logC
}
transformed parameters {
vector[nBlocks] logC[nSamples];
for (i in 1:N) {
logC[sample[i],cultureBlock[i]]=meanLogC[sample[i]]+sigmaLogC*eta[sample[i],cultureBlock[i]];
}
}
model {
vector[N] mu_y;
sigmaLogC ~ normal(0,1);
for (i in 1:nSamples) {
eta[i] ~ normal(0,1);
meanLogC[i] ~ normal(targetPotencies[i],0.1); //mean for each sample
}
if (run_estimation==1) {
for (i in 1:N) {
mu_y[i] = fplBPosRange(logX[i], 2, 1, 1, logC[sample[i],cultureBlock[i]], 0);
};
y ~ normal(mu_y,sigma);
}
}
'
```

And here is my call:

```
stanFit <- rstan::stan(
model_code = model_code,
data = list(N=nrow(data),
logX=data$log2Dose,
y=data$y,
nSamples=length(unique(data$sampleRole)),
sample=data$sample,
nBlocks=nBlocks,
cultureBlock=data$cultureBlock,
targetPotencies=targetPotencies,
sigma=0.1,
run_estimation=1), # named list of data
chains = 4, # number of Markov chains
warmup = 1000, # number of warmup iterations per chain
iter = 10000, # total number of iterations per chain
cores = 4, # number of cores (could use one per chain)
refresh = 0, # no progress shown
seed = 1,
pars=c("meanLogC[1]","meanLogC[2]","meanLogC[3]","meanLogC[4]","meanLogC[5]","meanLogC[6]","sigmaLogC"),
include=TRUE,
save_warmup=FALSE,
control=list(adapt_delta=0.8)
)
```

Any ideas what might be happening? The output fit is getting to be so big that it slows everything down.