R: Imputation by linear regression, bootstrap method

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

## 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. https://www.jstatsoft.org/v45/i03/

### See Also

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

[Package mice version 3.13.0 Index]