micemd: mice.impute.2l.glm.norm – R documentation

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mice.impute.2l.glm.norm

Imputation of univariate missing data using a Bayesian linear mixed model based on non-informative prior distributions

Description

Imputes univariate missing data using a Bayesian linear mixed model based on non-informative prior distributions. The method is dedicated to a continuous outcome stratified in severals clusters. Should be used with few clusters and few individuals per cluster. Can be very slow to perform otherwise.

Usage

mice.impute.2l.glm.norm(y, ry, x, type,...)

Arguments

`y`	Incomplete data vector of length `n`
`ry`	Vector of missing data pattern `(FALSE=missing, TRUE=observed)`
`x`	Matrix `(n x p)` of complete covariates.
`type`	Vector of length `ncol(x)` identifying random and class variables. Random variables are identified by a '2'. The class variable (only one is allowed) is coded as '-2'. Random variables also include the fixed effect.
`...`	Other named arguments.

Details

Imputes univariate two-level continuous variable from a homoscedastic normal model. The variability on the parameters of the imputation is propagated according to an explicit Bayesian modelling. More precisely, improper prior distributions are used for regression coefficients and variances components. The method is recommended for datasets with a small number of clusters and a small number of individuals per cluster. Otherwise, confidence intervals after applying analysis method on the multiply imputed dataset tend to be anti-conservative. In addition, the imputation can be highly time consumming.

Value

A vector of length nmis with imputations.

Author(s)

Vincent Audigier vincent.audigier@cnam.fr from the R code of Shahab Jolani.

References

Jolani, S. (2017) Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations. Biometrical Journal <doi:10.1002/bimj.201600220>

Jolani, S., Debray, T. P. A., Koffijberg, H., van Buuren, S., and Moons, K. G. M. (2015). Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Statistics in Medicine, 34(11):18411863. <doi:10.1002/sim.6451>

Audigier, V., White, I. , Jolani ,S. Debray, T., Quartagno, M., Carpenter, J., van Buuren, S. and Resche-Rigon, M. Multiple imputation for multilevel data with continuous and binary variables (2018). Statistical Science. <doi:10.1214/18-STS646>.