STAN in R: dimensions error in linear regression - r

Question

Below you can find a simple example of linear regression with 2 predictors. It works well. However, when having only 1 predictor (see second script), the following error message appears:
Exception: mismatch in number dimensions declared and found in context; processing stage=data initialization; variable name=x; dims declared=(20,1); dims found=(20)
The problem is that a matrix with 1 row is automatically transformed in a vector and therefore, doesn't match with the declared dimensions. One solution would be to declare xas a vector, but the problem is that I'm running the same scripts with different number of predictors (could be 1 or more).
STAN script:
write("// Stan model for simple linear regression
data {
int<lower=0> N; // number of data items
int<lower=0> K;// number of predictors
matrix[N, K] x;// predictor matrix
vector[N] y;// outcome vector
}
parameters {
real alpha; // intercept
vector[K] beta; // coefficients for predictors
real<lower=0> sigma; // error scale
}
model {
y ~ normal(x * beta + alpha, sigma); // likelihood
}", "ex_dimension.stan")
R script with 2 predictors (working):
N=20
K=2
x1=1:N+rnorm(N,0,0.5)
x2=rnorm(N,2,1)
x=cbind(x1,x2)
a=2
b=3
y=a*x1+b*x2+rnorm(N,0,1)
stan_data=list(N=N,
K=K,
x=x,
y=y)
fit <- stan(file = "ex_dimension.stan",
data = stan_data,
warmup = 500,
iter = 2000,
chains = 4,
cores = 4,
thin = 1,
control=list(adapt_delta=0.8))
fit
Script with 1 predictor (not working):
stan_data=list(N=N,
K=1,
x=x[,1],
y=y)
fit <- stan(file = "ex_dimension.stan",
data = stan_data,
warmup = 500,
iter = 2000,
chains = 4,
cores = 4,
thin = 1,
control=list(adapt_delta=0.8))
fit

Answer 1

Subset the matrix with drop = FALSE to avoid collapsing it to a vector and thereby passing the wrong input to the Stan model (see also e.g. Advanced R - Subsetting Chapter).
library(rstan)
stan_data <- list(N = N, K = 1, x = x[, 1, drop = FALSE], y = y)
fit <- stan(
model_code = "// Stan model for simple linear regression
data {
int<lower=0> N; // number of data items
int<lower=0> K; // number of predictors
matrix[N, K] x; // predictor matrix
vector[N] y; // outcome vector
}
parameters {
real alpha; // intercept
vector[K] beta; // coefficients for predictors
real<lower=0> sigma; // error scale
}
model {
y ~ normal(x * beta + alpha, sigma); // likelihood
}",
data = stan_data,
chains = 1
)
fit
#> Inference for Stan model: 4f8ba0f0c644593f519910e9d2741995.
#> 1 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=1000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> alpha 6.26 0.06 1.20 3.93 5.49 6.25 7.04 8.68 470 1
#> beta[1] 2.00 0.00 0.10 1.81 1.94 2.00 2.06 2.19 453 1
#> sigma 2.70 0.02 0.50 1.87 2.35 2.62 2.97 3.88 458 1
#> lp__ -28.15 0.06 1.21 -31.12 -28.80 -27.84 -27.23 -26.74 366 1
#>
#> Samples were drawn using NUTS(diag_e) at Thu Aug 15 12:41:19 2019.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).