Imputes univariate missing data using linear regression with bootstrap

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

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.

Vector with imputed data, same type as `y`

, and of length
`sum(wy)`

Draws a bootstrap sample from `x[ry,]`

and `y[ry]`

, calculates
regression weights and imputes with normal residuals.

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/

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.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`