JointAI (version 0.5.1)

list_models: List imputation models

Description

Print information on all models for incomplete covariates used in a JointAI object, including the model type, names of the parameters used and hyperparameters.

Usage

list_models(object, predvars = TRUE, regcoef = TRUE,
  otherpars = TRUE, priors = TRUE, refcat = TRUE)
list_impmodels(object, predvars = TRUE, regcoef = TRUE,
  otherpars = TRUE, priors = TRUE, refcat = TRUE)

Arguments

object

object inheriting from class 'JointAI'

predvars

logical; should information on the predictor variables be printed?

regcoef

logical; should information on the regression coefficients be printed?

otherpars

logical; should information on other parameters be printed?

priors

logical; should information on the priors be printed?

refcat

logical; should information on the reference category be printed?

References

Erler, N. S., Rizopoulos, D., Rosmalen, J. V., Jaddoe, V. W., Franco, O. H., & Lesaffre, E. M. (2016). Dealing with missing covariates in epidemiologic studies: A comparison between multiple imputation and a full Bayesian approach. Statistics in Medicine, 35(17), 2955-2974.

Examples

# NOT RUN {
# (set n.adapt = 0 and n.iter = 0 to prevent MCMC sampling to save time)
mod1 <- lm_imp(y ~ C1 + C2 + M2 + O2 + B2, data = wideDF, n.adapt = 0, n.iter = 0)

list_models(mod1)

# }