simple Gamma GLM in STAN

Question

I'm trying a simple Gamma GLM in STAN and R, but it crashes immediately

generate data:

set.seed(1)
library(rstan)
N<-500 #sample size
dat<-data.frame(x1=runif(N,-1,1),x2=runif(N,-1,1))
#the model
X<-model.matrix(~.,dat)
K<-dim(X)[2] #number of regression params
#the regression slopes
betas<-runif(K,-1,1)
shape <- 10
#simulate gamma data
mus<-exp(X%*%betas)
y<-rgamma(500,shape=shape,rate=shape/mus)

this is my STAN model:

model_string <- "
data {
  int<lower=0> N; //the number of observations
  int<lower=0> K; //the number of columns in the model matrix
  matrix[N,K] X; //the model matrix
  vector[N] y; //the response
}
parameters {
  vector[K] betas; //the regression parameters
  real<lower=0, upper=1000> shape; //the shape parameter
}
model {  
  y ~ gamma(shape, (shape/exp(X * betas)));
}"

when I run this model, R immediately crashes:

m <- stan(model_code = model_string, data = list(X=X, K=3, N=500, y=y), chains = 1, cores=1)

update : I think the problem is somewhere in the vectorization as I can get a running model where I pass every column of X as a vector.

update2: this also works

  for(i in 1:N)
    y[i] ~ gamma(shape, (shape / exp(X[i,] * betas)));

Answer 1

The problem with the original code is that there is no operator currently defined in Stan for a scalar divided by a vector. In this case, shape / exp(X * betas) You might be able to do shape[1:N] ./ exp(X * betas) or failing that, (shape * ones_vector) ./ exp(X * betas)