Dear all,

I’ve been trying to track down the following error message:

“Exception: bernoulli_logit_lpmf: Logit transformed probability parameter[2815] is nan, but must not be nan! for a model whose code is shown in the end of the post (because maybe not relevant to the question).”

To do this, I put a print function in the code that is executed once per stan iteration through the model. For the parameter that I’m monitoring (rewMagDist with 5 entries), I’ve put a prior of normal(1,0.7) and lower=0. From the print command I get for the first few steps leading up to output-1.csv: Iteration: 1 / 20 [ 5%] (Warmup):

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [48.5049,0,inf,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [34.2568,0,inf,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [0.978113,0,inf,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [0.662151,0,inf,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [0.439123,0,inf,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [0.366358,0,inf,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [0.383564,0,inf,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [0.388283,0,8.15364e+162,0,0]

output-1.csv: rewMagDist [0.391818,6.87112,1.86618,4.96533,5.44635]

output-1.csv: rewMagDist [0.390574,0,8.43455e+40,0,0]

…

I’ve noticed that some of the monitored values are inf or very large (8.43455e+40). Is this normal given that I’ve put a prior of normal(1,0.7)? Or does it suggest that something is wrong with my model?

[When I put in my model to only print if the evaluating to nan occurs (i.e. if invTxUtil, see model code is nan), it was always when parameters were very large.]

Many thanks

Jacquie

In case this is useful, here is the model code for a prospect theory model with ~70 human participants with ~1000 trials per participant. I did not manage to get this to finish sampling (I gave up after 10h and sampling still being at 20%); one way I found to make this run faster is to impose hard boundaries on rewMagDist and lossMagDist of <lower=0,upper=2>, which improves the problem I think because these parameters are in an exponent.

```
data{
int ntr; // number of total data points
int nsub; // total number of participants
int subInd[ntr];
int rewM[ntr,2]; // reward magnitude
int lossM[ntr,2]; // loss magnitude
real prob[ntr,2]; // reward probability
int choice[ntr]; // choice on each trial (1 for left, 0 for right)
}
parameters{
// individual subject level
real<lower=0> invTemp[nsub]; // inverse temperature
real<lower=0> probDist[nsub]; // probability distortion
real<lower=0> rewMagDist[nsub]; // reward magnitude distortion
real<lower=0> lossMagDist[nsub]; // loss magnitude distortion
}
model{
// transformed reward and loss probabilities and magnitudes
real rewProbTr[ntr,2];
real lossProbTr[ntr,2];
real rewMagTr[ntr,2];
real lossMagTr[ntr,2];
real utilities[ntr,2];
// some short-cuts
real g;
real invTxUtil[ntr];
// Priors
invTemp ~ normal(0,1); // it's a very small number
// if there is no bias: value=1, therefore centre prior on 1.
probDist ~ normal(1,0.7);
rewMagDist ~ normal(1,0.7); // values get crazy quickly, so only allow small range
lossMagDist ~ normal(1,0.7);
// Going through each trial (because Stan can only do exponentiation on single numbers)
for (it in 1:ntr){
for (io in 1:2){
// Transform probabilities
g = probDist[subInd[it]];
rewProbTr[it,io] = prob[it,io]^g/((prob[it,io]^g+(1-prob[it,io])^g)^(1/g));
lossProbTr[it,io] = 1- rewProbTr[it,io];
// Transform reward magnitudes
rewMagTr[it,io] = rewM[it,io]^rewMagDist[subInd[it]];
lossMagTr[it,io] = lossM[it,io]^lossMagDist[subInd[it]];
// Compute utility
utilities[it,io] = rewProbTr[it,io]*rewMagTr[it,io] - lossProbTr[it,io]*lossMagTr[it,io];
}
// combine utilities and choice stochasticity
invTxUtil[it] = invTemp[subInd[it]]*(utilities[it,1]-utilities[it,2]);
}
print("rewMagDist ", rewMagDist);
// link utilities to actual choices
choice ~ bernoulli_logit(invTxUtil);
}
~~~~
```