Package 'imputeMissings'

Title: Impute Missing Values in a Predictive Context
Description: Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.
Authors: Matthijs Meire [aut], Michel Ballings [aut, cre], Dirk Van den Poel [aut]
Maintainer: Michel Ballings <[email protected]>
License: GPL (>= 2)
Version: 0.0.4
Built: 2025-02-27 02:44:46 UTC
Source: https://github.com/cran/imputeMissings

Help Index


Compute the missing values to later impute them in another dataset

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

compute(data, method = "median/mode", ...)

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".

method

Either "median/mode" or "randomForest"

...

additional arguments for randomForest

Value

Values or models used for imputation

Author(s)

Matthijs Meire, Michel Ballings, Dirk Van den Poel, Maintainer: [email protected]

See Also

impute

Examples

#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)

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)

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

Value

An imputed data frame.

Author(s)

Matthijs Meire, Michel Ballings, Dirk Van den Poel, Maintainer: [email protected]

See Also

compute

Examples

#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)