ProfileGLMM (version 1.1.0)

prior_init: Initialize the prior hyperparameters for the Profile GLMM

Description

This function establishes the prior distributions for all parameters in the Profile GLMM. It sets up vague, non-informative priors (often using small precision/large variance or conjugate forms like Wishart/Dirichlet) for the fixed effects (\(beta_{FE}\)), residual variance (\(\sigma^2\)), random effects covariance (\(\Sigma_{RE}\)), latent effects covariance (\(\Sigma_{Lat}\)), cluster parameters (means and covariances), and the Dirichlet Process parameters (\(\alpha\)).

Usage

prior_init(params)

Value

A list (prior) containing the hyperparameter values structured by the parameter block they govern:

FE:

Priors for fixed effects and residual variance (e.g., lambda, a, b for conjugate Normal-Gamma).

RE:

Inverse-Wishart priors for random effects covariance (\(\Sigma_{RE}\)) (e.g., Phi, eta).

assign:

Priors for the cluster assignment parameters, nested under Cont (Normal-Inverse-Wishart for continuous) and Cat (Dirichlet for categorical).

Lat:

Inverse-Wishart prior for the latent effects covariance (\(\Sigma_{Lat}\)) (e.g., Phi, eta).

DP:

Parameters for the Dirichlet Process prior (e.g., scale, shape).

Arguments

params

A list containing dimensional parameters of the model (often the output of process_Data_outcome). Important fields used for prior setup include:

qFE:

Number of fixed effects coefficients.

qRE:

Dimension of the random effects vector.

qLat:

Dimension of the latent effects vector.

qUCont:

Number of continuous profile variables.

qUCat:

Number of categorical profile variables.

Examples

# Load dataProfile, the result of profileGLMM_preProcess()
data("examp")
dataProfile = examp$dataProfile
prior_config <- prior_init(dataProfile$params)