plot.mvoutlierCoDa: Plots for interpreting multivatiate outliers of CoDa

In mvoutlier: Multivariate Outlier Detection Based on Robust Methods

Description

Plots the computed information by mvoutlier.CoDa for supporting the interpretation of multivariate outliers in case of compositional data.

Usage

## S3 method for class 'mvoutlierCoDa'
plot(x, ..., which = c("biplot", "map", "uni", "parallel"), 
   choice = 1:2, coord = NULL, map = NULL, onlyout = TRUE, bw = FALSE, symb = TRUE, 
   symbtxt = FALSE, col = NULL, pch = NULL, obj.cex = NULL, transp = 1)

Arguments

x	resulting object from function mvoutlier.CoDa
...	further plotting arguments
which	type of plot that should be made
choice	select the pair of PCs used for the biplot
coord	coordinates for the presentation in a map
map	coordinates for the background map; see details below
onlyout	if TRUE only the outliers are shown in the plot
bw	if TRUE symbold will be in grey scale rather than in color
symb	if TRUE special symbols are used according to outlyingness
symbtxt	if TRUE text labels are used for plotting
col	define colors to be used for outliers and non-outliers
pch	define plotting symbols to be used for outliers and non-outliers
obj.cex	define symbol size for outliers and non-outliers
transp	define transparancy for parallel coordinate plot

Details

The function mvoutlier.CoDa prepares the information needed for this plot function: In a first step, the raw compositional data set in transformed by the isometric logratio (ilr) transformation to the usual Euclidean space. Then adaptive outlier detection is perfomed: Starting from a quantile 1-alpha of the chisquare distribution, one looks for the supremum of the differences between the chisquare distribution and the empirical distribution of the squared Mahalanobis distances. The latter are derived from the MCD estimator using the proportion quan of the data. The supremum is the outlier cutoff, and certain colors and symbols for the outliers are computed: The colors should reflect the magnitude of the median element concentration of the observations, which is done by computing for each observation along the single ilr variables the distances to the medians. The mediab of all distances determines the color (or grey scale): a high value, resulting in a red (or dark) symbol, means that most univariate parts have higher values than the average, and a low value (blue or light symbol) refers to an observation with mainly low values. The symbols are according to the cut-points from the quantiles 0.25, 0.5, 0.75, and the outlier cutoff of the squared Mahalanobis distances. This plot function then allows to visualize the information.

The optional background map for the representation of the outliers in a map can be included using the argument map. This should consist of one or more polygons representing the geographical x- and y-coordinates of the background map. Of course, this map should be represented in the same coordinate system as the coordinates for the sample locations provided by coord. The structure of map is as follows: It should consist of 2 columns, one for the x-, one for the y-coordinates. If a polygon ends, a row with 2 entries NA should follow. At the end two NA rows are needed. See also examples below.

Value

A plot is drawn.

Author(s)

Peter Filzmoser <[e-mail]> http://cstat.tuwien.ac.at/filz/

References

P. Filzmoser, K. Hron, and C. Reimann. Interpretation of multivariate outliers for compositional data. Submitted to Computers and Geosciences.

See Also

mvoutlier.CoDa, arw, map.plot, uni.plot

Examples

data(humus)
el=c("As","Cd","Co","Cu","Mg","Pb","Zn")
dsel <- humus[,el]
data(kola.background) # contains different information (coast, borders, etc.)
coo <- rbind(kola.background$coast,kola.background$boundary,kola.background$borders)
XY <- humus[,c("XCOO","YCOO")]
set.seed(123)
res <- mvoutlier.CoDa(dsel)

par(ask=TRUE)
### Parallel coordinate plot:
## show for all obvervations (transp is only useful when generating e.g. a pdf):
# plot(res,onlyout=FALSE,bw=TRUE,which="parallel",symb=FALSE,symbtxt=FALSE,transp=0.3)
## show only outliers with special colors and labels in the margins:
plot(res,onlyout=TRUE,bw=FALSE,which="parallel",symb=TRUE,symbtxt=TRUE,transp=0.3)

### Biplot:
## show all data points, outliers are in different color and have different symbol:
# plot(res,onlyout=FALSE,which="biplot",bw=FALSE,symb=FALSE,symbtxt=FALSE)
## show only the outliers with special symbols and colors:
plot(res,onlyout=TRUE,which="biplot",bw=FALSE,symb=TRUE,symbtxt=TRUE)

### Map:
## show all data points, outliers are in different color and have different symbol:
# plot(res,coord=XY,map=coo,onlyout=FALSE,which="map",bw=FALSE,symb=FALSE,symbtxt=FALSE)
## show only the outliers with special symbols and colors:
plot(res,coord=XY,map=coo,onlyout=TRUE,which="map",bw=FALSE,symb=TRUE,symbtxt=TRUE)

### Univariate scatterplot:
## show all data points, outliers are in different color and have different symbol:
# plot(res,onlyout=FALSE,which="uni",symb=FALSE,symbtxt=FALSE)
## show only the outliers with special symbols and colors:
plot(res,onlyout=TRUE,which="uni",symb=TRUE,symbtxt=TRUE)

Example output

Loading required package: sgeostat
sROC 0.1-2 loaded

mvoutlier documentation built on July 30, 2021, 9:09 a.m.