list_models {JointAI} | R Documentation |
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 |
regcoef |
logical; should information on the regression coefficients
be printed? (default is |
otherpars |
logical; should information on other parameters be printed?
(default is |
priors |
logical; should information on the priors
(and hyper-parameters) be printed? (default is |
refcat |
logical; should information on the reference category be
printed? (default is |
Note
The models listed by this function are not the actual imputation models, but the conditional models that are part of the specification of the joint distribution. Briefly, the joint distribution is specified as a sequence of conditional models
\[p(y | x_1, x_2, x_3, ..., \theta) p(x_1|x_2, x_3, ..., \theta) p(x_2|x_3, ..., \theta) ...\]The actual imputation models are the full conditional distributions \(p(x_1 | \cdot)\) derived from this joint distribution. Even though the conditional distributions do not contain the outcome and all other covariates in their linear predictor, outcome and other covariates are taken into account implicitly, since imputations are sampled from the full conditional distributions. For more details, see Erler et al. (2016) and Erler et al. (2019).
The function list_models
prints information on the conditional
distributions of the covariates (since they are what is specified;
the full-conditionals are automatically derived within JAGS). The outcome
is, thus, not part of the printed linear predictor, but is still included
during imputation.
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 NS, Rizopoulos D, Lesaffre EMEH (2021). "JointAI: Joint Analysis and Imputation of Incomplete Data in R." Journal of Statistical Software, 100(20), 1-56. doi:10.18637/jss.v100.i20.
Examples
# (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)