JointAI (version 1.0.2)

list_models: List model details

Description

This function prints information on all models, those explicitly specified by the user and those specified automatically by JointAI for (incomplete) covariates in a JointAI object.

Usage

list_models(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? (default is TRUE)

regcoef

logical; should information on the regression coefficients be printed? (default is TRUE)

otherpars

logical; should information on other parameters be printed? (default is TRUE)

priors

logical; should information on the priors (and hyper-parameters) be printed? (default is TRUE)

refcat

logical; should information on the reference category be printed? (default is TRUE)

References

Erler, N.S., Rizopoulos, D., Rosmalen, J.V., Jaddoe, V.W., Franco, O.H., & Lesaffre, E.M.E.H. (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.

Erler, N.S., Rizopoulos D. and Lesaffre E.M.E.H. (2019). JointAI: Joint Analysis and Imputation of Incomplete Data in R. arXiv e-prints, arXiv:1907.10867. URL https://arxiv.org/abs/1907.10867.

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, mess = FALSE)

list_models(mod1)

# }