This function returns a list of default values for the hyper-parameters.
default_hyperpars()
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 |
# 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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