impute: Impute missing values with the median/mode or `randomForest`

Description

When the median/mode method is used: character vectors and factors are imputed with the mode. Numeric and integer vectors are imputed with the median. When the random forest method is used predictors are first imputed with the median/mode and each variable is then predicted and imputed with that value. For predictive contexts there is a compute and an impute function. The former is used on a training set to learn the values (or random forest models) to impute (used to predict). The latter is used on both the training and new data to impute the values (or deploy the models) learned by the compute function.

Usage

impute(data, object = NULL, method = "median/mode", flag = FALSE)

Value

An imputed data frame.

Arguments

data: A data frame with dummies or numeric variables. When method=="median/mode" columns can be of type "character". When method="randomForest" columns cannot be of type "character".
object: If NULL impute will call compute on the current dataset. Otherwise it will accept the output of a call to compute
method: Either "median/mode" or "randomForest". Only works if object = NULL
flag: Add dummy variables to indicate which rows have been imputed for each variable

Author

Matthijs Meire, Michel Ballings, Dirk Van den Poel, Maintainer: Matthijs.Meire@UGent.be

Examples

Run this code

#Compute the values on a training dataset and impute them on new data.
#This is very convenient in predictive contexts. For example:

#define training data
(train <- data.frame(v_int=as.integer(c(3,3,2,5,1,2,4,6)),
                 v_num=as.numeric(c(4.1,NA,12.2,11,3.4,1.6,3.3,5.5)),
                 v_fact=as.factor(c('one','two',NA,'two','two','one','two','two')),
                 stringsAsFactors = FALSE))

#Compute values on train data
#randomForest method
values <- compute(train, method="randomForest")
#median/mode method
values2 <- compute(train)

#define new data
(newdata <- data.frame(v_int=as.integer(c(1,1,2,NA)),
                 v_num=as.numeric(c(1.1,NA,2.2,NA)),
                 v_fact=as.factor(c('one','one','one',NA)),
                 stringsAsFactors = FALSE))

#locate the NA's
is.na(newdata)
#how many missings per variable?
colSums(is.na(newdata))

#Impute on newdata
impute(newdata,object=values) #using randomForest values
impute(newdata,object=values2) #using median/mode values

#One can also impute directly in newdata without the compute step
impute(newdata)

#Flag parameter
impute(newdata,flag=TRUE)