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()

Arguments

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

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

binom: hyper-parameters for binomial models

mu_reg_binom mean in the priors for regression coefficients

poisson: hyper-parameters for poisson models

mu_reg_poisson mean in the priors for regression coefficients

multinomial: hyper-parameters for multinomial models

mu_reg_multinomial mean 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

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`

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

mu_reg_surv mean in the priors for regression coefficients

Examples

Run this code

# NOT RUN {
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)


# }

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