outlierPlot | R Documentation |
This is a wrapper for two plot functions which can be used to analyse the results of outlier detection with the extremevalues package.
outlierPlot(y, L, mode="qq", ...)
qqFitPlot(y, L, title=NA, xlab=NA, ylab=NA, fat=FALSE)
plotMethodII(y, L, title=NA, xlab=NA, ylab=NA, fat=FALSE)
y |
A vector of values |
L |
The result of L <- getOutliers(y,...) |
mode |
Plot type. "qq" for Quantile-quantile plot with indicated outliers, "residual" for plot of fit residuals with indicated outliers (Method II only) |
... |
Optional arguments, to be transferred to qqFitPlot or plotMethodII (see below) |
title |
A custom title (must be a string) |
xlab |
A custom label for the x-axis (must be a string) |
ylab |
A custim label for the y-axis (must be a string) |
fat |
If TRUE, axis, fonts, labels, points and lines are thicker for export and publication |
Outliers are marked with a color or special symbol. If mode="qq":
observed agains predicted y-values are plotted. Points between vertical lines
were used in the fit. If L$method="Method I"
, horizontal lines indicate the
limits below (above) which observations are outliers. mode="residuals"
only works when L$Method="Method II"
. It generates a residual plot where
points between two vertical lines were used in the fit. Horizontal lines
indicate the computed confidence limits. The outermost points in the gray areas
are outliers.
Mark van der Loo, www.markvanderloo.eu
The file <your R directory>/R-<version>/library/extremevalues/extremevalues.pdf contains a worked example. It can also be downloaded from my website.
y <- rlnorm(100)
y <- c(0.1*min(y),y,10*max(y))
K <- getOutliers(y,method="I",distribution="lognormal")
L <- getOutliers(y,method="II",distribution="lognormal")
par(mfrow=c(1,2))
outlierPlot(y,K,mode="qq")
outlierPlot(y,L,mode="residual")
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