dlookr (version 0.3.9)

imputate_outlier: Imputate Outliers

Description

Outliers are imputated with some representative values and statistical methods.

Usage

imputate_outlier(.data, xvar, method)

Arguments

.data

a data.frame or a tbl_df.

xvar

variable name to replace missing value.

method

method of missing values imputation.

Value

An object of imputation class. Attributes of imputation class is as follows.

• method : method of missing value imputation.

  • predictor is numerical variable

    • "mean" : arithmetic mean

    • "median" : median

    • "mode" : mode

    • "capping" : Imputate the upper outliers with 95 percentile, and Imputate the bottom outliers with 5 percentile.

• outlier_pos : position of outliers in predictor.

• outliers : outliers. outliers corresponding to outlier_pos.

• type : "outliers". type of imputation.

Details

imputate_outlier() creates an imputation class. The `imputation` class includes missing value position, imputated value, and method of missing value imputation, etc. The `imputation` class compares the imputated value with the original value to help determine whether the imputated value is used in the analysis.

See vignette("transformation") for an introduction to these concepts.

See Also

imputate_na.

Examples

# NOT RUN {
# Generate data for the example
carseats <- ISLR::Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA

# Replace the missing value of the Price variable with median
imputate_outlier(carseats, Price, method = "median")

# Replace the missing value of the Price variable with rpart
# The target variable is US.
imputate_outlier(carseats, Price, method = "capping")

## using dplyr -------------------------------------
library(dplyr)

# The mean before and after the imputation of the Price variable
carseats %>%
  mutate(Price_imp = imputate_outlier(carseats, Price, method = "capping")) %>%
  group_by(US) %>%
  summarise(orig = mean(Price, na.rm = TRUE),
    imputation = mean(Price_imp, na.rm = TRUE))

# If the variable of interest is a numarical variable
price <- imputate_outlier(carseats, Price)
price
summary(price)
plot(price)
# }