| 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 |
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.
compute(data, method = "median/mode", ...)compute(data, method = "median/mode", ...)
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 |
Values or models used for imputation
Matthijs Meire, Michel Ballings, Dirk Van den Poel, Maintainer: [email protected]
#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)#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)
randomForest
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.
impute(data, object = NULL, method = "median/mode", flag = FALSE)impute(data, object = NULL, method = "median/mode", flag = FALSE)
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 |
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 |
An imputed data frame.
Matthijs Meire, Michel Ballings, Dirk Van den Poel, Maintainer: [email protected]
#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)#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)