hcmm_hyperpar {MixedDataImpute} | R Documentation |
Generates a list of hyperparameters for use in hcmm_impute
. Specifying
only hcmmdat
or q
AND cx
will generate default values
(see citation).
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)
hcmmdat |
An |
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 |
A list of hyperparameters