robustMD: Robust Mahalanobis

In faoutlier: Influential Case Detection Methods for Factor Analysis and Structural Equation Models

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. \Sexpr[results=rd]{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. \Sexpr[results=rd]{tools:::Rd_expr_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)

faoutlier documentation built on April 4, 2025, 5:17 a.m.