outlier
Find and graph Mahalanobis squared distances to detect outliers
The Mahalanobis distance is \(D^2 = (x-\mu)' \Sigma^-1 (x-\mu)\) where \(\Sigma\) is the covariance of the x matrix. D2 may be used as a way of detecting outliers in distribution. Large D2 values, compared to the expected Chi Square values indicate an unusual response pattern. The mahalanobis function in stats does not handle missing data.
- Keywords
- multivariate, models
Usage
outlier(x, plot = TRUE, bad = 5,na.rm = TRUE, xlab, ylab, ...)Arguments
- x
A data matrix or data.frame
- plot
Plot the resulting QQ graph
- bad
Label the bad worst values
- na.rm
Should missing data be deleted
- xlab
Label for x axis
- ylab
Label for y axis
- …
More graphic parameters, e.g., cex=.8
Details
Adapted from the mahalanobis function and help page from stats.
Value
The D2 values for each case
References
Yuan, Ke-Hai and Zhong, Xiaoling, (2008) Outliers, Leverage Observations, and Influential Cases in Factor Analysis: Using Robust Procedures to Minimize Their Effect, Sociological Methodology, 38, 329-368.
See Also
Examples
# NOT RUN {
#first, just find and graph the outliers
d2 <- outlier(sat.act)
#combine with the data frame and plot it with the outliers highlighted in blue
sat.d2 <- data.frame(sat.act,d2)
pairs.panels(sat.d2,bg=c("yellow","blue")[(d2 > 25)+1],pch=21)
# }