f.screen.outliers: Deletes multivariate outliers using Mahalanobis distance

In awmeade/psymetlab: Provides useful functions for APA formatting and writing output to Excel

Description

Accepts a dataframe and optional list of variables within that dataframe for which to screen the data. The function computes mahalanobis distace and associated chi-square on the screening data and then returns a dataframe that is a subset of the original all.data dataframe based on non-significant chi-square values.

Usage

f.screen.outliers(all.data, screening.vars = NULL, p.val = 0.05)

Arguments

all.data	is the database from which to remove outliers
screening.vars	is an optional array of variable names on which the screening should be based.
p.val	is the cutoff value of the chi-square distribution to use. default is .05.

Author(s)

Adam Meade [e-mail]

Examples

## Not run: 
nrow(trees)
new.data <- f.screen.outliers(trees)
nrow(new.data)

## End(Not run)

awmeade/psymetlab documentation built on Nov. 21, 2020, 11:39 p.m.