VarSelImputation: Imputation of missing values

In VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Description

This function permits imputation of missing values in a dataset by using mixture model. Two methods can be used for imputation:

• posterior mean (method="postmean")

• sampling from the full conditionnal distribution (method="sampling")

Usage

VarSelImputation(obj, newdata, method = "postmean")

Arguments

obj	an instance of VSLCMresults which defines the model used for imputation.
newdata	data.frame Dataset containing the missing values to impute.
method	character definiting the method of imputation: "postmean" or "sampling"

Examples

# Data loading
data("heart")

# Clustering en 2 classes
results <- VarSelCluster(heart[,-13], 2)

# Data where missing values will be imputed
newdata <- heart[1:2,-13]
newdata[1,1] <- NA
newdata[2,2] <- NA

# Imputation
VarSelImputation(results, newdata)

VarSelLCM documentation built on Oct. 23, 2020, 8:20 p.m.