misspattern: Missing pattern analysis for missing data

In daya6489/DriveML: Self-Drive Machine Learning Projects

Description

This function is used to summarise the missing variable, missing pattern identification and classifying the columns based on the pattern of missing values.

Usage

misspattern(data, mfeature, drop = 0.99, print = FALSE)

Arguments

data	[data.frame | Required] data set with missing values
mfeature	[character | Required] only missing variable name
drop	[numeric | optional] drop variable percentage. Example, if drop = 0.9, function will automatically drop 90per missing columns from the data set
print	[character | optional] defualt print is FALSE

Value

final variable list, summary of missing data analysis

Examples

## Sample iris data
mdata <- iris
mobject <- misspattern(mdata, mfeature = c("Sepal.Length", "Petal.Length"), drop = 0.99, print = F)

daya6489/DriveML documentation built on July 22, 2021, 4:21 a.m.