JointAI (version 1.0.6)

default_hyperpars: Get the default values for hyper-parameters

Description

This function returns a list of default values for the hyper-parameters.

Usage

default_hyperpars()

Details

norm: hyper-parameters for normal and log-normal models

mu_reg_norm	mean in the priors for regression coefficients
tau_reg_norm	precision in the priors for regression coefficients
shape_tau_norm	shape parameter in Gamma prior for the precision of the (log-)normal distribution
rate_tau_norm	rate parameter in Gamma prior for the precision of the (log-)normal distribution

gamma: hyper-parameters for Gamma models

mu_reg_gamma	mean in the priors for regression coefficients
tau_reg_gamma	precision in the priors for regression coefficients
shape_tau_gamma	shape parameter in Gamma prior for the precision of the Gamma distribution
rate_tau_gamma	rate parameter in Gamma prior for the precision of the Gamma distribution

beta: hyper-parameters for beta models

mu_reg_beta	mean in the priors for regression coefficients
tau_reg_beta	precision in the priors for regression coefficients
shape_tau_beta	shape parameter in Gamma prior for the precision of the beta distribution
rate_tau_beta	rate parameter in Gamma prior for precision of the of the beta distribution

binom: hyper-parameters for binomial models

mu_reg_binom	mean in the priors for regression coefficients
tau_reg_binom	precision in the priors for regression coefficients

poisson: hyper-parameters for poisson models

mu_reg_poisson	mean in the priors for regression coefficients
tau_reg_poisson	precision in the priors for regression coefficients

multinomial: hyper-parameters for multinomial models

mu_reg_multinomial	mean in the priors for regression coefficients
tau_reg_multinomial	precision in the priors for regression coefficients

ordinal: hyper-parameters for ordinal models

mu_reg_ordinal	mean in the priors for regression coefficients
tau_reg_ordinal	precision in the priors for regression coefficients
mu_delta_ordinal	mean in the prior for the intercepts
tau_delta_ordinal	precision in the priors for the intercepts

ranef: hyper-parameters for the random effects variance-covariance matrices (when there is only one random effect a Gamma distribution is used instead of the Wishart distribution)

shape_diag_RinvD	shape parameter in Gamma prior for the diagonal elements of RinvD
rate_diag_RinvD	rate parameter in Gamma prior for the diagonal elements of RinvD
KinvD_expr	a character string that can be evaluated to calculate the number of degrees of freedom in the Wishart distribution used for the inverse of the variance-covariance matrix for random effects, depending on the number of random effects nranef

surv: parameters for survival models (survreg, coxph and JM)

mu_reg_surv	mean in the priors for regression coefficients
tau_reg_surv	precision in the priors for regression coefficients

Examples

default_hyperpars()

# To change the hyper-parameters:
hyp <- default_hyperpars()
hyp$norm['rate_tau_norm'] <- 1e-3
mod <- lm_imp(y ~ C1 + C2 + B1, data = wideDF, hyperpars = hyp, mess = FALSE)