Description Usage Arguments Details Value Author(s) References See Also
View source: R/mice.impute.2l.glm.pois.R
Imputes univariate missing data using a Bayesian mixed model (Poisson regression) based on non-informative prior distributions. The method is dedicated to a count outcome stratified in severals clusters. Should be used with few clusters and few individuals per cluster. Can be very slow to perform otherwise.
1 | mice.impute.2l.glm.pois(y, ry, x, type,...)
|
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern |
x |
Matrix |
type |
Vector of length |
... |
Other named arguments. |
Imputes univariate missing data using a Bayesian mixed model (Poisson regression) based on non-informative prior distributions. 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, the method can be very slow to perform.
A vector of length nmis
with imputations.
Vincent Audigier vincent.audigier@cnam.fr from the R code of Shahab Jolani.
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>
mice,mice.impute.2l.2stage.pois
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