DriveML (version 0.1.0)

misspattern: Missing pattern analysis for missing data

Description

this function for summarise the missing variable, missing pattern identification, classifying the columns based on 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

# NOT RUN {
## Sample iris data
mdata <- iris
mobject <- misspattern(mdata, mfeature = c("Sepal.Length", "Petal.Length"), drop = 0.99, print = F)
# }