continuousSuperLearner: Function to generate imputations using SuperLearner for data...

In abshev/superMICE: SuperLearner Method for MICE

Function to generate imputations using SuperLearner for data with a continuous outcome

Description

Function to generate imputations using SuperLearner for data with a continuous outcome

Usage

continuousSuperLearner(y, x, wy, SL.library, kernel, bw, bw.update, ...)

Arguments

y	Vector of observed and missing/imputed values of the variable to be imputed.
x	Numeric matrix of variables to be used as predictors in SuperLearner models with rows corresponding to observed values of the variable to be imputed and columns corresponding to individual predictor variables.
wy	Logical vector. A TRUE value indicates locations in y that are missing or imputed.
SL.library	Either a character vector of prediction algorithms or a list containing character vectors. A list of functions included in the SuperLearner package can be found with SuperLearner::listWrappers().
kernel	one of gaussian, uniform, or triangular. Specifies the kernel to be used in estimating the distribution around a missing value.
bw	NULL or numeric value for bandwidth of kernel function (as standard deviations of the kernel).
bw.update	logical indicating whether bandwidths should be computed every iteration or only on the first iteration. Default is TRUE, but FALSE may speed up the run time at the cost of accuracy.
...	further arguments passed to SuperLearner().

Value

numeric vector of randomly drawn imputed values.

abshev/superMICE documentation built on May 10, 2022, 11:27 a.m.