removeMissingLevels: A function to remove levels of categorical variables with...

In mnodzenski/metabomxtr: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions

A function to remove levels of categorical variables with completely missing outcome data from data frames or matrices.

Description

This function removes levels of categorical variables with completely missing outcome data from data frames or matrices.

Usage

removeMissingLevels(missing.levels.list,  dataset)

Arguments

missing.levels.list	A list, output by function idMissingLevels, containing the specific levels of categorical variables that are missing all outcome data.
dataset	A data frame containing the variables specified in yname and missing.levels.list.

Value

Returns a data frame with the levels indicated in missing.levels.list removed.

Author(s)

Michael Nodzenski

Examples

#create example analysis data 
data(euMetabCData)
example.data<-euMetabCData
example.data[example.data$batch==1, "aspartic_acid"]<-NA

#check to determine if aspartic acid values are entirely missing for 
#any level of batch or pheno 
missing.levels.check<-lapply( "aspartic_acid", anyMissingLevels, cat.vars=c("pheno", "batch"), dataset=example.data)
names(missing.levels.check)<-"aspartic_acid"
missing.levels.check

#find the specific missing level
missing.levels<-idMissingLevels( "aspartic_acid", missing.levels.check, example.data)

#remove the missing level 
cleaned.data<-removeMissingLevels(missing.levels, example.data)

mnodzenski/metabomxtr documentation built on Aug. 24, 2022, 1:40 p.m.