Wrapper Function to Imputation Methods in the imputeR Package
The imputation methods "imputeR.lmFun" and "imputeR.cFun" provide
interfaces to imputation methods in the imputeR package for
continuous and binary data, respectively.
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, ... )
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 |
Methods for continuous variables:
Methods for binary variables:
imputeR::gbmC,
imputeR::lassoC,
imputeR::ridgeC,
imputeR::rpartC,
imputeR::stepBackC,
imputeR::stepBothC,
imputeR::stepForC
A vector of length nmis=sum(!ry) with imputed values.
## 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)
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