default_hyperpars {JointAI}

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

Note

From the JAGS user manual on the specification of the Wishart distribution:
For KinvD larger than the dimension of the variance-covariance matrix the prior on the correlation between the random effects is concentrated around 0, so that larger values of KinvD indicate stronger prior belief that the elements of the multivariate normal distribution are independent. For KinvD equal to the number of random effects the Wishart prior puts most weight on the extreme values (correlation 1 or -1).

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)