I would like to get the estimated coefficients of a model using rstan
in an rnotebook
I have the following stan
chunk:
```{stan output.var="rats"}
data {
int<lower=0> N;
int<lower=0> T;
real x[T];
real y[N,T];
real xbar;
}
parameters {
real alpha[N];
real beta[N];
real mu_alpha;
real mu_beta; // beta.c in original bugs model
real<lower=0> sigmasq_y;
real<lower=0> sigmasq_alpha;
real<lower=0> sigmasq_beta;
}
transformed parameters {
real<lower=0> sigma_y; // sigma in original bugs model
real<lower=0> sigma_alpha;
real<lower=0> sigma_beta;
sigma_y = sqrt(sigmasq_y);
sigma_alpha = sqrt(sigmasq_alpha);
sigma_beta = sqrt(sigmasq_beta);
}
model {
mu_alpha ~ normal(0, 100);
mu_beta ~ normal(0, 100);
sigmasq_y ~ inv_gamma(0.001, 0.001);
sigmasq_alpha ~ inv_gamma(0.001, 0.001);
sigmasq_beta ~ inv_gamma(0.001, 0.001);
alpha ~ normal(mu_alpha, sigma_alpha); // vectorized
beta ~ normal(mu_beta, sigma_beta); // vectorized
for (n in 1:N)
for (t in 1:T)
y[n,t] ~ normal(alpha[n] + beta[n] * (x[t] - xbar), sigma_y);
}
generated quantities {
real alpha0;
alpha0 = mu_alpha - xbar * mu_beta;
}
```
I also have the following data
```{r}
df <- read_delim("https://raw.githubusercontent.com/wiki/stan-dev/rstan/rats.txt",delim = " ")
y <- as.matrix(df)
x <- c(8,15,22,29,36)
xbar <- mean(x)
N <- nrow(y)
T <- ncol(y)
```
The documentation on github shows rats_fit <- stan(file = 'https://raw.githubusercontent.com/stan-dev/example-models/master/bugs_examples/vol1/rats/rats.stan')
, but since I am using a chunk I don't have a file to refer to.
I have tried stan(rats)
, summary(rats)
, print(rats)
, but none of these seem to work.
You can insert an R code chunk either using the RStudio toolbar (the Insert button) or the keyboard shortcut Ctrl + Alt + I ( Cmd + Option + I on macOS).
You use results="hide" to hide the results/output (but here the code would still be displayed). You use include=FALSE to have the chunk evaluated, but neither the code nor its output displayed.
You can preview your file by using the shortcut shift + ctrl + k on rmarkdown::render("file.
The first RMarkdown chunk calls rats <- rstan::stan_model(model_code=the_text)
behind the scenes, so in order to sample from that posterior distribution you need to ultimately do rats_fit <- sampling(rats, data = list())
, whose remaining arguments are pretty much the same as for stan
. But you do have to call library(rstan)
before all that.
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