imputeMissings (version 0.0.1)

compute: 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 mean/median 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. Categorical variables (i.e., non-dummy / indicator) variables work up to a certain number but are not recommended.

method

Either "median/mode" or "randomForest"

...

additional arguments for randomForest

Value

Values or models used for imputation

See Also

impute

Examples

#Example:
#create some data
data <- data.frame(V1=as.factor(c('yes','no','no',NA,'yes','yes','yes')),
                  V2=as.character(c(1,2,3,4,4,4,NA)),
                  V3=c(1:6,NA),V4=as.numeric(c(1:6,NA)))
#demonstrate function
object <- compute(data,method="randomForest")
object <- compute(data,method="median/mode")

impute(data)
impute(data,object=compute(data, method="randomForest"))
impute(data,method="randomForest")