I am trying to fit a hierarchical model to (log) count data, modelling the logcounts as gaussians, using MatlabStan. I extended the model from the 8 schools example.

For some runs of the model I get the error “- is either mistyped or misplaced”, apparently after the compilation stage. I’m not sure what this refers to. Weirdly the error seems to be data dependent, it runs fine for some dataset but then crashes for others. Any help would be much appreciated, I’m kind of lost as to how to debug this.

Stan is sampling with 4 chains…

output-1.csv: - is either mistyped or misplaced.

output-1.csv: Failed to parse arguments, terminating Stan

Warning: Stan seems to have exited badly.

In StanFit/process_exit_failure (line 290)

In StanFit/process_exit (line 170)

In StanFit>@(src,evnt)process_exit(self,src,evnt)

In processState/set.exitValue (line 30)

In processManager.pollTimerStop (line 508)

In timer/timercb (line 34)

In timercb (line 24)

In timer/start (line 33)

In processManager/start (line 403)

In StanModel/sampling (line 768)

In stan (line 138)

```
data {
int<lower=0> J; // number of regions
int<lower=0> G; // number of groups
int<lower=0> T; // number of parameters (J x G)
int<lower=0> M; // number of measurements
int<lower=1> regionid[M]; // vector of region indices
int<lower=1> groupid[M]; // vector of group indices
real y[M]; // logcounts for each region
}
parameters {
real<lower=1,upper=3.5> mu; // mean of logcounts across all regions (across groups)
real<lower=0> tau; // spread of mean logcounts across all regions (across groups)
real<lower=1,upper=3.5> mupergroup[J]; // mean of logcounts across groups
real<lower=0> taupergroup[J]; // spread of mean logcounts across groups
real eta[T]; // shifted mean cell counts per region
real<lower=0> sigma; // sampling variability of log counts
}
transformed parameters {
real<lower=-10,upper=10> theta[T];
for (j in 1:J)
for (g in 1:G)
theta[j+(g-1)*J] = mupergroup[j] + taupergroup[j] * eta[j+(g-1)*J];
}
model {
mu ~ normal(2.3,1);
mupergroup ~ normal(2.3,2);
eta ~ normal(0, 1);
sigma ~ cauchy(0,1);
tau ~ cauchy(0,0.5);
taupergroup ~ cauchy(0,0.5);
for (i in 1:M)
y[i] ~ normal(theta[regionid[i] + (groupid[i]-1)*J], sigma); // observed data
}
```