mice.impute.2l.zipln.pmm {accelmissing}

Imputation by PMM under ZIPLN model.

Description

Imputes univariate missing data using the predictive mean matching (PMM) under the zero-inflated Poisson Log-normal (ZIPLN) model.

Usage

mice.impute.2l.zipln.pmm(y, ry, x, wy=NULL, type, K, zs = zs, 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

wy

defalut wy=NULL

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.

zs

Matrix (N by 2K+1) with the elements of log(yhat)-log(lambda) (See Lee and Gill, 2016)

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.zipln.pmm",...)

Author(s)

Jung Ae Lee <[e-mail]>

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.

See Also

mice, mice.impute.2l.zip.pmm, mice.impute.2l.zipln