I’m trying to pass a matrix as data to a stan model (via cmdstanr), but somehow the data gets read as a vector,

generating the following error message:

mismatch in number dimensions declared and found in context; processing stage=data initialization; variable name=k; dims declared=(3,9); dims found=(27)

The reproducible example is below. I’ve seen several posts with the same error message, where e.g. a vector was passed as a 1-column matrix, but none of the solutions there (specify as.vector, which in my case’d become as.matrix()) seems to work.

Some context: the model is based on the memory exponential decay example in Lee&Wagenmakers Bayesian Cognitive Modeling. There is a slightly different implementation of the same model in stan by Smira here, which gives the exact same issue: example-models/Retention_1_Stan.R at master · stan-dev/example-models · GitHub

```
library(cmdstanr)
t <- c(1, 2, 4, 7, 12, 21, 35, 59, 99, 200)
nt <- length(t)
slist <- 1:4
ns <- length(slist)
k1 <- matrix(c(18, 18, 16, 13, 9, 6, 4, 4, 4, NA,
17, 13, 9, 6, 4, 4, 4, 4, 4, NA,
14, 10, 6, 4, 4, 4, 4, 4, 4, NA,
NA, NA, NA, NA,NA,NA,NA,NA,NA, NA), nrow=ns, ncol=nt, byrow=T)
k <- k1[1:(ns - 1), 1:(nt - 1)] # Excluding NAs (for Stan solution)
n <- 18
data <- list(k=k, n=n, t=t, ns=ns, nt=nt) # To be passed on to Stan
myinits <- list(
list(alpha=.5, beta=.1))
stan_file <- write_stan_file("
// Retention With No Individual Differences
data {
int<lower=0> ns; // n of participants
int<lower=0> nt; // n of temporal points
int<lower=0> n; // n of stimuli to remember
int<lower=0> k[ns - 1,nt - 1]; // successes
int<lower=0> t[nt]; // time from training
}
// The parameters accepted by the model.
parameters {
real<lower=0,upper=1> alpha;
real<lower=0,upper=1> beta;
}
transformed parameters {
matrix<lower=0,upper=1>[ns,nt] theta;
// Retention Rate At Each Lag For Each Subject Decays Exponentially
for (i in 1:ns) {
for (j in 1:nt) {
theta[i,j] = fmin(1, exp(-alpha * t[j]) + beta);
}
}
}
// The model to be estimated.
model {
// Priors
alpha ~ beta(1, 1); // can be removed
beta ~ beta(1, 1); // can be removed
// Observed Data
for (i in 1:(ns - 1))
for (j in 1:(nt - 1))
k[i,j] ~ binomial(n, theta[i,j]);
}
generated quantities {
int<lower=0,upper=n> predk[ns,nt];
// Predicted Data
for (i in 1:ns)
for (j in 1:nt)
predk[i,j] = binomial_rng(n , theta[i,j]);
}")
mod <- cmdstan_model(stan_file, cpp_options = list(stan_threads = TRUE), pedantic = TRUE)
samples <- mod$sample(
data = data,
seed = 123,
chains = 2,
parallel_chains = 2,
threads_per_chain = 2,
iter_warmup = 2000,
iter_sampling = 2000,
refresh = 500,
#init = myinits,
max_treedepth = 20,
adapt_delta = 0.99,
)
****
```