beta_priors {estimateW}

Set prior specifications for the slope parameters

Description

This function allows the user to specify custom values for Gaussian priors on the slope parameters.

Usage

beta_priors(
  k,
  beta_mean_prior = matrix(0, k, 1),
  beta_var_prior = diag(k) * 100
)

Arguments

k

The total number of slope parameters in the model.

beta_mean_prior

numeric k by 1 matrix of prior means \underline{\mu}_\beta.

beta_var_prior

A k by k matrix of prior variances \underline{V}_\beta. Defaults to a diagonal matrix with 100 on the main diagonal.

Details

For the slope parameters \beta the package uses common Normal prior specifications. Specifically, p(\beta)\sim\mathcal{N}(\underline{\mu}_\beta,\underline{V}_\beta).

This function allows the user to specify custom values for the prior hyperparameters \underline{\mu}_\beta and \underline{V}_\beta. The default values correspond to weakly informative Gaussian priors with mean zero and a diagonal prior variance-covariance matrix with 100 on the main diagonal.

Value

A list with the prior mean vector (beta_mean_prior), the prior variance matrix (beta_var_prior) and the inverse of the prior variance matrix (beta_var_prior_inv).