outlierplot {compositions}R Documentation

Plot various graphics to analyse outliers.

Description

A collection of plots emphasing different aspects of possible outliers.

Usage

outlierplot(X,...)
## S3 method for class 'acomp':
outlierplot(X,colcode=colorsForOutliers1,pchcode=pchForOutliers1,
  type=c("scatter","biplot","dendrogram","ecdf","portion","nout"),
  legend.position,pch=19,...,clusterMethod="ward",
  myCls=classifier(X,alpha=alpha,type=class.type,corrected=corrected),
  classifier=OutlierClassifier1,
  alpha=0.05,
  class.type="best",
  Legend,pow=1,
  main=paste(deparse(substitute(X))),
  corrected=TRUE,robust=TRUE,princomp.robust=FALSE,
                              mahRange=exp(c(-5,5))^pow,
                              flagColor="red",
                              meanColor="blue",
                              grayColor="gray40",
                              goodColor="green",
                              mahalanobisLabel="Mahalanobis Distance"
                              )

Arguments

X The dataset as an acomp object
colcode A color palette for factor given by the myCls, or function to create it from the factor. Use colorForOutliers2 if class.method="all" is used.
pchcode A function to create a plot character palette for the factor returned by the myCls call
type The type of plot to be produced. See details for more precise definitions.
legend.position The location of the legend. Must!!! be given to draw a classical legend.
pch A default plotting char
... Further arguments to the used plotting function
clusterMethod The clustering method for hclust based outlier grouping.
myCls A factor presenting the groups of outliers
classifier The routine to create a factor presenting the groups of outliers heuristically. It is only used in the default argument to myCls.
alpha The confidence level to be used for outlier classification tests
class.type The type of classification that should be generated by classifier
Legend The content will be substituted and stored as list entry legend in the result of the function. It can than be evaluated to actually create a seperate legend on another device (e.g. for publications).
pow The power of Mahalanobis distances to be used.
main The title of the graphic
corrected Literature typically proposes to compare the Mahalanobis distances with the distribution of a random Mahalanobis distance. However it would be needed to correct this for (dependent) multiple testing, since we always test the whole dataset, which means comparing against the distribution of the maximum Mahalanobis distance. This argument switches to this second behavior, giving less outliers.
robust A robustness description as define in robustnessInCompositions
princomp.robust Either a logical determining wether or not the principal component analysis should be done robustly or a principle component object for the dataset.
mahRange The range of Mahalanobis distances displayed. This is fixed to make views comparable among datasets. However if the preset default is not enough a warning is issued and a red mark is drawn in the plot
flagColor The color to draw critical situations.
meanColor The color to draw typical curves.
goodColor The color to draw confidence bounds.
grayColor The color to draw less important things.
mahalanobisLabel The axis label to be used for axes displaying Mahalanobis distances.

Details

See outliersInCompositions for a comprehensive introduction into the outlier treatment in compositions.

Value

a list respresenting the criteria computed to create the plots. The content of the list depends on the plotting type selected.

Note

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, ClusterFinder1

Examples

data(SimulatedAmounts)
outlierplot(acomp(sa.outliers5))
## Not run: 

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) {
outlierplot(x,type="scatter",class.type="grade");
  title(y)
},datas,names(datas))

par(mfrow=c(2,3),pch=19,mar=c(3,2,2,1))  
tmp<-mapply(function(x,y) {
  myCls2 <- OutlierClassifier1(x,alpha=0.05,type="all",corrected=TRUE)
  outlierplot(x,type="scatter",classifier=OutlierClassifier1,class.type="best",
  Legend=legend(1,1,levels(myCls),xjust=1,col=colcode,pch=pchcode),
  pch=as.numeric(myCls2));
  legend(0,1,legend=levels(myCls2),pch=1:length(levels(myCls2)))
  title(y)
},datas,names(datas))
# To slow
par(mfrow=c(2,3),pch=19,mar=c(3,2,2,1))  
for( i in 1:length(datas) ) 
  outlierplot(datas[[i]],type="ecdf",main=names(datas)[i])
par(mfrow=c(2,3),pch=19,mar=c(3,2,2,1))  
for( i in 1:length(datas) ) 
  outlierplot(datas[[i]],type="portion",main=names(datas)[i])
par(mfrow=c(2,3),pch=19,mar=c(3,2,2,1))  
for( i in 1:length(datas) ) 
  outlierplot(datas[[i]],type="nout",main=names(datas)[i])
par(opar)
## End(Not run)

[Package compositions version 1.01-1 Index]