Description Usage Arguments Value Note Author(s) References See Also
Imputes univariate missing data using the predictive mean matching (PMM) under the zero-inflated Poisson (ZIP) model.
1 | mice.impute.2l.zip.pmm(y, ry, x, wy=NULL, type, K, D)
|
y |
Incomplete data vector of length n |
ry |
Vector of missing data pattern ( |
x |
Matrix (n by p) of complete covariates |
wy |
defalut wy=NULL |
type |
If |
K |
The number of the lag and lead variables. |
D |
The number of donors to be drawn by predictive mean matching. |
A vector of length nmis
with imputations
This function runs by the argument in mice(..., method="2l.zip.pmm",...)
Jung Ae Lee <jungaeleeb@gmail.com>
[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.
mice
, mice.impute.2l.zipln.pmm
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