Hi Stan/rstanarm Users,
I am new to the overall Stan environment and still trying to learn it’s many subtleties. One simple problem I am trying to solve is normal linear regression where I have a known diagonal covariance matrix that isn’t the identity matrix. Using the standard
LM template, this would be
model.lm <- lm(formula = Y ~ X, data=d, weights=W)
where the weights
W are the precision estimates of the dependent variable
Y at each
X point. Since the syntax is very similar, the R code below was my guess as to what it should be in rstanarm.
model.rstanarm <- stan_glm( formula = Y ~ X, data=d, weights=W, family = gaussian(), prior=priors.slope, prior_intercept=priors.int )
Is this correct or should I be using the
prior.aux variable in
stan_glm() to specify my knowledge about the covariance matrix rather than weights?