mvoutlier (version 2.1.1)

plot.mvoutlierCoDa: Plots for interpreting multivatiate outliers of CoDa

Description

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

Usage

# S3 method for 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

Value

A plot is drawn.

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.

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

# NOT RUN {
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)
# }