Initialization issues with Rstan 2.21.2 on MacOS Catalina

[TheaT21]

If this is a duplicate, please apologize in advance. But I don’t seem to find other posts with the same problem here, although it seems that others have encountered similar situations.

Summary of the problem After compiling stan models, I get an error message at the beginning of sampling from the sampler.

Description Whenever I run a Stan model, I get the following message when sampling begins: [1] "Error in sampler$call_sampler(args_list[[i]]) : Initialization failed." error occurred during calling the sampler; sampling not done here are whatever error messages were returned [[1]] Stan model 'af543013862bc307cac5e99e31ad53e6' does not contain samples.

[[2]] Stan model 'af543013862bc307cac5e99e31ad53e6' does not contain samples.

[[3]] Stan model 'af543013862bc307cac5e99e31ad53e6' does not contain samples.turned

The sampling process then terminates and Initialization failed. I reinstalled rstan following the directions on the rstan page. I also tried installing them from source and still the sample problem. I also have XCode and R tools installed.

Reproducible Steps

library(plyr) library(StanHeaders) library(ggplot2) library(rstan) library(loo)

abmodelnb_f = " data{ int N; int Nt; int Ns;

int TP[N];
int Dis[N];
int TN[N];
int NDis[N];
int Study[N];
int Test[N];

} parameters{ matrix[2, Nt] logitmu; vector[Ns] nu[2]; matrix[Ns, Nt] delta[2]; vector[Nt] tau[2]; //* vector[2] sigmab; real rho; } transformed parameters{ matrix[Ns, 2] p_i[Nt]; matrix[2, Nt] MU; matrix[2, Nt] RR; matrix[2, Nt] OR; vector[Nt] DOR; vector[Nt] S; matrix[Nt, Nt] A; matrix[Nt, Nt] B; matrix[Nt, Nt] C;

vector<lower=0>[Nt] tausq[2];
vector<lower=0>[2] sigmabsq;

matrix[Nt, Nt] sigmasq[2];
matrix[Nt, Nt] rhow[2];

for (i in 1:Ns){
    for (j in 1:2){
        for (k in 1:Nt)
            p_i[k][i,j] = inv_logit(logitmu[j,k] +  nu[j][i] + delta[j][i,k]);
    }
}

for (j in 1:2){
    for (k in 1:Nt){
        MU[j,k] = mean(col(p_i[k], j));
    }
    tausq[j] = (tau[j]).*(tau[j]);
}

for (j in 1:2){
    for (k in 1:Nt){
        RR[j, k] = MU[j, k]/MU[j, 1]; 
        OR[j, k] = (MU[j, k]*(1 - MU[j, 1]))/(MU[j, 1]*(1 - MU[j, k]));
     }
}

for (l in 1:Nt){
    DOR[l] = (MU[1, l]*MU[2, l])/((1 - MU[1, l])*(1 - MU[2, l]));

    for(m in 1:Nt){
        A[l, m] = if_else((MU[1, l] > MU[1, m]) && (MU[2, l] > MU[2, m]), 1, 0);
        B[l, m] = if_else((MU[1, l] < MU[1, m]) && (MU[2, l] < MU[2, m]), 1, 0);
        C[l, m] = if_else((MU[1, l] == MU[1, m]) && (MU[2, l] == MU[2, m]), 1, 0);
    }

    S[l] = (2*sum(row(A, l)) + sum(row(C, l)))/(2*sum(row(B, l)) + sum(row(C, l)));
}

sigmabsq = (sigmab).*(sigmab);

for (j in 1:2){
    for (k in 1:Nt){
        for (l in 1:Nt){
            sigmasq[j][k,l] = (sigmabsq[j] + tausq[j][k])*((sigmabsq[j] + tausq[j][l]));
            rhow[j][k,l] = sigmabsq[j]/sqrt(sigmasq[j][k,l]);
        }
    }
}

} model{ //Priors for (j in 1:2){ logitmu[j] ~ normal(0, 5); tau[j] ~ cauchy(0, 2.5); }

sigmab ~ cauchy(0, 2.5);
rho ~ uniform(-1, 1);

nu[2] ~ normal(0, sigmab[2]);

for (i in 1:Ns){

    nu[1][i] ~ normal((sigmab[1]/sigmab[2])*rho*nu[2][i], sqrt(sigmabsq[1]*(1 - (rho*rho))));

    for (j in 1:2){
        for (k in 1:Nt)
            delta[j][i,k] ~ normal(0, tau[j][k]);
    }
}

for (n in 1:N){
    TP[n] ~ binomial(Dis[n], p_i[Test[n]][Study[n], 1]);
    TN[n] ~ binomial(NDis[n], p_i[Test[n]][Study[n], 2]);
}

} generated quantities{ vector[2*N] loglik;

for (n in 1:N)
    loglik[n] = binomial_lpmf(TN[n]| NDis[n], p_i[Test[n]][Study[n], 1]);

for (n in (N+1):(2*N))
    loglik[n] = binomial_lpmf(TN[n-N]| NDis[n-N], p_i[Test[n-N]][Study[n-N], 2]);

} " N <- nrow(df1) Ns <- max(df1$Study) Nt <- max(df1$Test)

datalist <- list( N = N, Ns = Ns, Nt = Nt, TP = df1$TP, Dis = df1$Dis, TN = df1$TN, NDis = df1$NDis, Test = df1$Test, Study = df1$Study) model1.0 <- stan(model_code = abmodelnb_f, data=datalist, chains = 3, iter = 4000, warmup = 2000, thin = 5, seed = 1, cores=3, verbose=FALSE)

RStan Version 2.21.2

R Version 4.02

Operating System: MacOS Catalina 10.15.5