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 |
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 |
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).
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 lm_imp(y ~ C1 + C2 + B1, data = wideDF, hyperpars = hyp, mess = FALSE): object 'hyp' not found