outlier outlier - Robust multivariate outlier detection outlier

SAS Macro Programs: outlier

$Version: 1.5-2 (01 Aug 2008)
Michael Friendly
York University

OUTLIER macro ( [download] get outlier.sas)

The OUTLIER macro calculates robust Mahalanobis distances for each observation in a data set. The results are robust in that potential outliers do not contribute to the distance of any other observations. For a multivariate normal sample, the points will lie on a straight line of unit slope; outliers will have squared distances well above the line. A high-resolution plot may be constructed from the output data set; see the examples in "Section 9.3"

The macro makes one or more passes through the data. Each pass assigns 0 weight to observations whose DSQ value has Prob ( chi² ) < PVALUE. The number of passes should be determined empirically so that no new observations are trimmed on the last step.


Name of the data set to analyze
List of input variables
Name of an optional ID variable to identify observations
Name of the output data set for plotting. The robust squared distances are named DSQ. The corresponding theoretical quantiles are named EXPECTED. The variable _WEIGHT_ has the value 0 for observations identified as possible outliers.
Probability value of chi² statistic used to trim observations.
Number of passes of the iterative trimming procedure.
Print the OUT= data set?