robustMD {faoutlier}

Robust Mahalanobis

Description

Obtain Mahalanobis distances using the robust computing methods found in the MASS package. This function is generally only applicable to models with continuous variables.

Usage

robustMD(data, method = "mve", ...)

## S3 method for class 'robmah'
print(x, ncases = 10, digits = 5, ...)

## S3 method for class 'robmah'
plot(x, y = NULL, type = "xyplot", main, ...)

Arguments

data

matrix or data.frame

method

type of estimation for robust means and covariance (see cov.rob)

...

additional arguments to pass to MASS::cov.rob()

x

an object of class robmah

ncases

number of extreme cases to print

digits

number of digits to round in the final result

y

empty parameter passed to plot

type

type of plot to display, can be either 'qqplot' or 'xyplot'

main

title for plot. If missing titles will be generated automatically

Author(s)

Phil Chalmers [e-mail]

References

Chalmers, R. P. & Flora, D. B. (2015). faoutlier: An R Package for Detecting Influential Cases in Exploratory and Confirmatory Factor Analysis. Applied Psychological Measurement, 39, 573-574. doi: 10.1177/0146621615597894

Flora, D. B., LaBrish, C. & Chalmers, R. P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. Frontiers in Psychology, 3, 1-21. doi: 10.3389/fpsyg.2012.00055

See Also

gCD, obs.resid, LD

Examples

## Not run: 
data(holzinger)
output <- robustMD(holzinger)
output
plot(output)
plot(output, type = 'qqplot')

## End(Not run)