mice.impute.imputeR.lmFun {miceadds} | R Documentation |
Wrapper Function to Imputation Methods in the imputeR Package
Description
The imputation methods "imputeR.lmFun"
and "imputeR.cFun"
provide
interfaces to imputation methods in the imputeR package for
continuous and binary data, respectively.
Usage
mice.impute.imputeR.lmFun(y, ry, x, Fun=NULL, draw_boot=TRUE, add_noise=TRUE, ... )
mice.impute.imputeR.cFun(y, ry, x, Fun=NULL, draw_boot=TRUE, ... )
Arguments
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
Fun |
Name of imputation functions in imputeR package, e.g.,
|
draw_boot |
Logical indicating whether a Bootstrap sample is taken for sampling model parameters |
add_noise |
Logical indicating whether empirical residuals should be added to predicted values |
... |
Further arguments to be passed |
Details
Methods for continuous variables:
imputeR::CubistR
,
imputeR::glmboostR
,
imputeR::lassoR
,
imputeR::pcrR
,
imputeR::plsR
,
imputeR::ridgeR
,
imputeR::stepBackR
,
imputeR::stepBothR
,
imputeR::stepForR
Methods for binary variables:
imputeR::gbmC
,
imputeR::lassoC
,
imputeR::ridgeC
,
imputeR::rpartC
,
imputeR::stepBackC
,
imputeR::stepBothC
,
imputeR::stepForC
Value
A vector of length nmis=sum(!ry)
with imputed values.
Examples
## Not run:
#############################################################################
# EXAMPLE 1: Example with binary and continuous variables
#############################################################################
library(mice)
library(imputeR)
data(nhanes, package="mice")
dat <- nhanes
dat$hyp <- as.factor(dat$hyp)
#* define imputation methods
method <- c(age="",bmi="norm",hyp="imputeR.cFun",chl="imputeR.lmFun")
Fun <- list( hyp=imputeR::ridgeC, chl=imputeR::ridgeR)
#** do imputation
imp <- mice::mice(dat1, method=method, maxit=10, m=4, Fun=Fun)
summary(imp)
## End(Not run)