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

Phil Chalmers rphilip.chalmers@gmail.com

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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.3389/fpsyg.2012.00055")

Examples

Run this code


if (FALSE) {
data(holzinger)
output <- robustMD(holzinger)
output
plot(output)
plot(output, type = 'qqplot')
}