robustMD function

Robust Mahalanobis

Robust Mahalanobis

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

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

Examples

## Not run:

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

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")

See Also

gCD, obs.resid, LD

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

faoutlier packageRead PDF manual