mice.impute.norm.boot {mice}R Documentation

Imputation by Linear Regression, Bootstrap Method

Description

Imputes univariate missing data using linear regression with boostrap

Usage

mice.impute.norm.boot(y, ry, x, ridge=0.00001, ...)

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern (FALSE=missing, TRUE=observed)

x

Matrix (n x p) of complete covariates.

ridge

Ridge parameter

...

Other named arguments.

Details

Draws a bootstrap sample from x[ry,] and y[ry], calculates regression weights and imputes with normal residuals. The ridge parameter adds a penalty term ridge*diag(xtx) to the variance-covariance matrix xtx.

Value

A vector of length nmis with imputations.

Author(s)

Stef van Buuren, 2011

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/


[Package mice version 2.11 Index]