IsMahalanobisOutlier {compositions}  R Documentation 
Checking for outliers
Description
Detect outliers with respect to a normal distribution model.
Usage
IsMahalanobisOutlier(X,...,alpha=0.05,goodOnly=NULL,
replicates=1000,corrected=TRUE,robust=TRUE,crit=NULL)
Arguments
X 
a dataset (e.g. given as acomp, rcomp, aplus, rplus or rmult) object
to which 
... 
further arguments to MahalanobisDist/gsi.mahOutlier 
alpha 
The confidence level for identifying outliers. 
goodOnly 
an integer vector. Only the specified index of the dataset should be used for estimation of the outlier criteria. This parameter if only a small portion of the dataset is reliable. 
replicates 
The number of replicates to be used in the Monte
Carlo simulations for determination of the quantiles. The

corrected 
logical. Literatur often proposed to compare the Mahalanobis distances with ChisqApproximations of there distributions. However this does not correct for multiple testing. If corrected is true a correction for multiple testing is used. In any case we do not use the chisqapproximation, but a simulation based procedure to compute confidence bounds. 
robust 
A robustness description as define in

crit 
The critical value to be used. Typically the routine is called mainly for the purpose of finding this value, which it does, when crit is NULL, however sometimes we might want to specifiy a value used by someone else to reproduce the results. 
Details
See outliersInCompositions and robustnessInCompositions for a comprehensive introduction into the outlier treatment in compositions.
See OutlierClassifier1
for a highlevel method to
classify observations in the context of outliers.
Value
A logical vector giving for each element the result of the alphalevel test for beeing an outlier. TRUE corresponds to a significant result.
Note
For some unkown reasons the computation sometimes produces NaN's. In this case a warning is issued and a recomputation is tried.
The package robustbase is required for using the robust estimations.
Author(s)
K.Gerald v.d. Boogaart http://www.stat.boogaart.de
See Also
OutlierClassifier1
, outlierplot
,
ClusterFinder1
Examples
## Not run:
data(SimulatedAmounts)
datas < list(data1=sa.outliers1,data2=sa.outliers2,data3=sa.outliers3,
data4=sa.outliers4,data5=sa.outliers5,data6=sa.outliers6)
opar<par(mfrow=c(2,3),pch=19,mar=c(3,2,2,1))
tmp<mapply(function(x,y){
plot(x,col=ifelse(IsMahalanobisOutlier(x),"red","gray"))
title(y)
},datas,names(datas))
## End(Not run)