mice.impute.2l.zipln: Imputation by Bayesian ZIPLN model.

In accelmissing: Missing Value Imputation for Accelerometer Data

Imputation by Bayesian ZIPLN model.

Description

Imputes univariate missing data using Bayesian model under the zero-inflated Poisson Log-normal (ZIPLN) distribution.

Usage

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

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. 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)

Value

A vector of length nmis with imputations

Note

This function is called when you set accel.impute(..., method = "zipln"; interally, it then calls mice(..., method="2l.zipln",...).

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, accel.impute

accelmissing documentation built on June 8, 2025, 1:54 p.m.