ExpOutliers {SmartEDA}

Univariate Outlier Analysis

Description

this function will run univariate outlier analysis based on boxplot or SD method. The function returns the summary of oultlier for selected numeric features and adding new features if there is any outliers

Usage

ExpOutliers(
  data,
  varlist = NULL,
  method = "boxplot",
  treatment = NULL,
  capping = c(0.05, 0.95),
  outflag = FALSE
)

Arguments

data

dataframe or matrix

varlist

list of numeric variable to perform the univariate outlier analysis

method

detect outlier method boxplot or NxStDev (where N is 1 or 2 or 3 std deviations, like 1xStDev or 2xStDev or 3xStDev)

treatment

treating outlier value by mean or median. default NULL

capping

default LL = 0.05 & UL = 0.95cap the outlier value by replacing those observations outside the lower limit with the value of 5th percentile and above the upper limit, with the value of 95th percentile value

outflag

add extreme value flag variable into output data

Details

this function provides both summary of the outlier variable and data

Univariate outlier analysis method

• boxplot is If a data value are below (Q1 minus 1.5x IQR) or boxplot lower whisker or above (Q3 plus 1.5x IQR) or boxplot upper whisker then those points are flaged as outlier value

• Standard Deviation is If a data distribution is approximately normal then about 68 percent of the data values lie within one standard deviation of the mean and about 95 percent are within two standard deviations, and about 99.7 percent lie within three standard deviations. If any data point that is more than 3 times the standard deviation, then those points are flaged as outlier value

Value

Outlier summary includes

• Num of outliers is Number of outlier in each variable

• Lower bound is Q1 minus 1.5x IQR for boxplot; Mean minus 3x StdDev for Standard Deviation method

• Upper bound is Q3 plus 1.5x IQR for boxplot; Mean plus 3x StdDev for Standard Deviation method

• Lower cap is Lower percentile capping value

• Upper cap is Upper percentile capping value

Examples

ExpOutliers(mtcars, varlist = c("mpg","disp","wt", "qsec"), method = 'BoxPlot',
capping = c(0.1, 0.9), outflag = TRUE)

ExpOutliers(mtcars, varlist = c("mpg","disp","wt", "qsec"), method = '2xStDev',
capping = c(0.1, 0.9), outflag = TRUE)

# Mean imputation or 5th percentile or 95th percentile value capping
ExpOutliers(mtcars, varlist = c("mpg","disp","wt", "qsec"), method = 'BoxPlot',
treatment = "mean", capping = c(0.05, 0.95), outflag = TRUE)