imputeEM: Expectation Maximization (EM) for imputation of missing...

In mvdalab: Multivariate Data Analysis Laboratory

Expectation Maximization (EM) for imputation of missing values.

Description

Missing values are iterarively updated via an EM algorithm.

Usage

imputeEM(data, impute.ncomps = 2, pca.ncomps = 2, CV = TRUE, Init = "mean",
         scale = TRUE, iters = 25, tol = .Machine$double.eps^0.25)

Arguments

data	a dataset with missing values.
impute.ncomps	integer corresponding to the minimum number of components to test.
pca.ncomps	minimum number of components to use in the imputation.
CV	Use cross-validation in determining the optimal number of components to retain for the final imputation.
Init	For continous variables impute either the mean or median.
scale	Scale variables to unit variance.
iters	For continous variables impute either the mean or median.
tol	the threshold for assessing convergence.

Details

A completed data frame is returned that mirrors a model.matrix. NAs are replaced with convergence values as obtained via EM. If object contains no NAs, it is returned unaltered.

Value

imputeEM returns a list containing the following components:

Imputed.DataFrames	A list of imputed data frames across impute.comps
Imputed.Continous	A list of imputed values, at each EM iteration, across impute.comps
CV.Results	Cross-validation results across impute.comps
ncomps	impute.comps

Author(s)

Nelson Lee Afanador ([e-mail]), Thanh Tran ([e-mail])

References

B. Walczak, D.L. Massart. Dealing with missing data, Part I. Chemom. Intell. Lab. Syst. 58 (2001); 15:27

Examples

dat <- introNAs(iris, percent = 25)
imputeEM(dat)

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.