Stan error: require unconstrained variable declaration. found simplex - r

Question

In Stan, I get the following error:
SYNTAX ERROR, MESSAGE(S) FROM PARSER:
require unconstrained variable declaration. found simplex.
ERROR at line 48
46: for (j in 1:records) {
47: real phenology_predictor;
48: simplex[7] pi;
^
I don't quite understand what is the problem. When I used real pi[7] instead of simplex[7] pi, I got different error:
SYNTAX ERROR, MESSAGE(S) FROM PARSER:
no matches for function name="categorical_log"
arg 0 type=int
arg 1 type=real[1]
available function signatures for categorical_log:
0. categorical_log(int, vector) : real
1. categorical_log(int[1], vector) : real
unknown distribution=categorical
ERROR at line 63
62:
63: Y[j] ~ categorical(pi);
^
64:
which I don't understand either... My whole code:
data {
int sites;
int records;
int Y[records];
vector[records] yday;
int site[records];
}
transformed data {
int M[sites];
}
parameters {
real<lower=0,upper=1> psi;
real<lower=0,upper=1000> phi_phen_scale;
real phi_alpha;
real q_date;
real q_date2;
real q_site[sites];
}
model {
real p[records];
real q[records];
// priors
phi_phen_scale ~ normal(0, 10);
phi_alpha ~ normal(0, 10);
q_date ~ normal(0, 10);
q_date2 ~ normal(0, 10);
// vectorized
M ~ bernoulli(psi);
q_site ~ normal(0, 10);
for (j in 1:records) {
real phenology_predictor;
simplex[7] pi;
phenology_predictor <- q_date * yday[j] + q_date2 * yday[j]^2;
p[j] <- M[site[j]] * inv_logit(phi_alpha + phi_phen_scale * phenology_predictor);
q[j] <- inv_logit(q_site[site[j]] + phenology_predictor);
pi[1] <- 1-p[j] + p[j]*(1-q[j])^6;
pi[2] <- p[j]*q[j] ;
pi[3] <- p[j]*(1-q[j])*q[j];
pi[4] <- p[j]*(1-q[j])^2*q[j];
pi[5] <- p[j]*(1-q[j])^3*q[j];
pi[6] <- p[j]*(1-q[j])^4*q[j];
pi[7] <- p[j]*(1-q[j])^5*q[j];
Y[j] ~ categorical(pi);
}
}

Answer 1

Constrained local parameters, such as simplexes, cannot be declared inside the model block because they are not checked. So, you should just declare pi to be a plain vector of length 7, like vector[7] pi;. Nevertheless, pi needs to be on the simplex in order to be an admissible argument to the categorical function.
If it is the case that symbolically pi is non-negative and sums to 1, then it is a question of making sure that numerically they are sufficiently close to non-negative and sum to something that is sufficiently close to 1. I'm not sure what the numerical tolerance is for a simplex in Stan but there is some wiggle room. If numerical error is the problem then doing pi <- pi / sum(pi); before passing pi to the categorical function may help.