mice.impute.norm.boot: Imputation by linear regression, bootstrap method

In mice: Multivariate Imputation by Chained Equations

Imputation by linear regression, bootstrap method

Description

Imputes univariate missing data using linear regression with bootstrap

Usage

mice.impute.norm.boot(y, ry, x, wy = NULL, ...)

Arguments

y	Vector to be imputed
ry	Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.
x	Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.
wy	Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.
...	Other named arguments.

Details

Draws a bootstrap sample from x[ry,] and y[ry], calculates regression weights and imputes with normal residuals.

Value

Vector with imputed data, same type as y, and of length sum(wy)

Author(s)

Gerko Vink, Stef van Buuren, 2018

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v045.i03")}

See Also

Other univariate imputation functions: mice.impute.cart(), mice.impute.lasso.logreg(), mice.impute.lasso.norm(), mice.impute.lasso.select.logreg(), mice.impute.lasso.select.norm(), mice.impute.lda(), mice.impute.logreg(), mice.impute.logreg.boot(), mice.impute.mean(), mice.impute.midastouch(), mice.impute.mnar.logreg(), mice.impute.mpmm(), mice.impute.norm(), mice.impute.norm.nob(), mice.impute.norm.predict(), mice.impute.pmm(), mice.impute.polr(), mice.impute.polyreg(), mice.impute.quadratic(), mice.impute.rf(), mice.impute.ri()

mice documentation built on June 8, 2025, 11:31 a.m.