Usage
hcmm_hyperpar(hcmmdat = NULL, q = ncol(hcmmdat$Y), cx = hcmmdat$cx, alpha_a = 0.5, alpha_b = 0.5, beta_x_a = 0.5, beta_x_b = 0.5, beta_y_a = 0.5, beta_y_b = 0.5, tau_a = 0.5, tau_b = 0.5, v = q + 1, w = q + 2, Sigma0 = diag(1, q)/v, gamma = 1/cx, sigma2_0beta = 10)
Arguments
hcmmdat
An hcmm_data object
q
The number of continuous variables
cx
A length p vector (where p is the number of categorical variables). cx[j]
is the number of distinct values taken by X[,j]
alpha_a,alpha_b
Gamma prior on top-level concentration parameter,
where E(alpha) = alpha_a/alpha_b
beta_x_a,beta_x_b
Gamma prior on X model concentration parameter
beta_y_a,beta_y_b
Gamma prior on Y model concentration parameter
tau_a,tau_b
Gamma prior on coefficient precision parameters
v,w
Degree of freedom parameters in the hierarchical inverse-Wishart/Wishart prior
Sigma0
Centering matrix in the hierarchical inverse-Wishart/Wishart prior
gamma
Parameter of the symmetric Dirichlet priors in the product multinomial kernel.
(Should be a length p vector.)
sigma2_0beta
Variance of the prior on B0