Imputes univariate missing data using the predictive mean matching (PMM) under the zero-inflated Poisson (ZIP) model.
Usage
mice.impute.2l.zip.pmm(y, ry, x, type, K, D)
Arguments
y
Incomplete data vector of length n
ry
Vector of missing data pattern (FALSE=missing, TRUE=observed)
x
Matrix (n by p) of complete covariates
type
If type=1, covariates are included in both logit and poisson models.
If type=2, covariates are included only in poisson part.
If type=3, covariates are included only in logit part.
K
The number of the lag and lead variables. K=3 is default.
D
The number of donors to be drawn by predictive mean matching. D=5 is default.
Value
A vector of length nmis with imputations
Note
This function runs by the argument in mice(..., method="2l.zip.pmm",...)
Author(s)
Jung Ae Lee <jungaeleeb@gmail.com>
References
[1] Lee JA, Gill J (2016). Missing value imputation for physical activity data measured by accelerometer. Statistical Methods in Medical Research.
[2] van Buuren S, Groothuis-Oudshoorn K (2011). mice: Multivariate imputations by chained equations in R. Journal of Statistical Software.
[3] Kleinke K, Reinecke J (2013). Multiple imputation of incomplete zero-infated count data. Statistica Neerlandica.