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_normmean in the priors for regression coefficients
tau_reg_normprecision in the priors for regression coefficients
shape_tau_normshape parameter in Gamma prior for the precision of the (log-)normal distribution
rate_tau_normrate parameter in Gamma prior for the precision of the (log-)normal distribution

gamma: hyper-parameters for Gamma models

mu_reg_gammamean in the priors for regression coefficients
tau_reg_gammaprecision in the priors for regression coefficients
shape_tau_gammashape parameter in Gamma prior for the precision of the Gamma distribution
rate_tau_gammarate parameter in Gamma prior for the precision of the Gamma distribution

beta: hyper-parameters for beta models

mu_reg_betamean in the priors for regression coefficients
tau_reg_betaprecision in the priors for regression coefficients
shape_tau_betashape parameter in Gamma prior for the precision of the beta distribution
rate_tau_betarate parameter in Gamma prior for precision of the of the beta distribution

binom: hyper-parameters for binomial models

mu_reg_binommean in the priors for regression coefficients
tau_reg_binomprecision in the priors for regression coefficients

poisson: hyper-parameters for poisson models

mu_reg_poissonmean in the priors for regression coefficients
tau_reg_poissonprecision in the priors for regression coefficients

multinomial: hyper-parameters for multinomial models

mu_reg_multinomialmean in the priors for regression coefficients
tau_reg_multinomialprecision in the priors for regression coefficients

ordinal: hyper-parameters for ordinal models

mu_reg_ordinalmean in the priors for regression coefficients
tau_reg_ordinalprecision in the priors for regression coefficients
mu_delta_ordinalmean in the prior for the intercepts
tau_delta_ordinalprecision 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_RinvDshape parameter in Gamma prior for the diagonal elements of RinvD
rate_diag_RinvDrate parameter in Gamma prior for the diagonal elements of RinvD
KinvD_expra 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_survmean in the priors for regression coefficients
tau_reg_survprecision 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)
#> Error in get_data_list(Mlist, info_list, hyperpars, append_data_list): object 'hyp' not found