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
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.
See Also
gCD, obs.resid, LD
Examples
## Not run:
data(holzinger)
output <- robustMD(holzinger)
output
plot(output)
plot(output, type = 'qqplot')
## End(Not run)