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

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()
#> $norm #> mu_reg_norm tau_reg_norm shape_tau_norm rate_tau_norm #> 0e+00 1e-04 1e-02 1e-02 #> #>$gamma
#>    mu_reg_gamma   tau_reg_gamma shape_tau_gamma  rate_tau_gamma
#>           0e+00           1e-04           1e-02           1e-02
#>
#> $beta #> mu_reg_beta tau_reg_beta shape_tau_beta rate_tau_beta #> 0e+00 1e-04 1e-02 1e-02 #> #>$binom
#>  mu_reg_binom tau_reg_binom
#>         0e+00         1e-04
#>
#> $poisson #> mu_reg_poisson tau_reg_poisson #> 0e+00 1e-04 #> #>$multinomial
#>  mu_reg_multinomial tau_reg_multinomial
#>               0e+00               1e-04
#>
#> $ordinal #> mu_reg_ordinal tau_reg_ordinal mu_delta_ordinal tau_delta_ordinal #> 0e+00 1e-04 0e+00 1e-04 #> #>$ranef
#> shape_diag_RinvD  rate_diag_RinvD       KinvD_expr
#>           "0.01"          "0.001"   "nranef + 1.0"
#>
#> $surv #> mu_reg_surv tau_reg_surv #> 0.000 0.001 #> # To change the hyper-parameters: hyp <- default_hyperpars() hyp$norm['rate_tau_norm'] <- 1e-3
mod <- lm_imp(y ~ C1 + C2 + B1, data = wideDF, hyperpars = hyp, mess = FALSE)