bagplot {aplpack}  R Documentation 
compute.bagplot()
computes an object describing a bagplot
of a bivariate data set. plot.bagplot()
plots a bagplot object.
bagplot()
computes and plots a bagplot.
bagplot(x, y, factor = 3, na.rm = FALSE, approx.limit = 300, show.outlier = TRUE, show.whiskers = TRUE, show.looppoints = TRUE, show.bagpoints = TRUE, show.loophull = TRUE, show.baghull = TRUE, create.plot = TRUE, add = FALSE, pch = 16, cex = 0.4, dkmethod = 2, precision = 1, verbose = FALSE, debug.plots = "no", col.loophull="#aaccff", col.looppoints="#3355ff", col.baghull="#7799ff", col.bagpoints="#000088", transparency=FALSE, show.center = TRUE, ... ) compute.bagplot(x, y, factor = 3, na.rm = FALSE, approx.limit = 300, dkmethod=2,precision=1,verbose=FALSE,debug.plots="no") ## S3 method for class 'bagplot' plot(x, show.outlier = TRUE, show.whiskers = TRUE, show.looppoints = TRUE, show.bagpoints = TRUE, show.loophull = TRUE, show.baghull = TRUE, add = FALSE, pch = 16, cex = 0.4, verbose = FALSE, col.loophull="#aaccff", col.looppoints="#3355ff", col.baghull="#7799ff", col.bagpoints="#000088", transparency=FALSE, show.center = TRUE, ...)
x 
x values of a data set;
in 
y 
y values of the data set 
factor 
factor defining the loop 
na.rm 
if TRUE 'NA' values are removed otherwise exchanged by median 
approx.limit 
if the number of data points exceeds

show.outlier 
if TRUE outlier are shown 
show.whiskers 
if TRUE whiskers are shown 
show.looppoints 
if TRUE loop points are plottet 
show.bagpoints 
if TRUE bag points are plottet 
show.loophull 
if TRUE the loop is plotted 
show.baghull 
if TRUE the bag is plotted 
create.plot 
if FALSE no plot is created 
add 
if TRUE the bagplot is added to an existing plot 
pch 
sets the plotting character 
cex 
sets characters size 
dkmethod 
1 or 2, there are two method of approximating the bag, method 1 is very rough (only based on observations 
precision 
precision of approximation, default: 1 
verbose 
automatic commenting of calculations 
debug.plots 
if TRUE additional plots describing intermediate results are constructed 
col.loophull 
color of loop hull 
col.looppoints 
color of the points of the loop 
col.baghull 
color of bag hull 
col.bagpoints 
color of the points of the bag 
transparency 
see section details 
show.center 
if TRUE the center is shown 
... 
additional graphical parameters 
A bagplot is a bivariate generalization of the well known
boxplot. It has been proposed by Rousseeuw, Ruts, and Tukey.
In the bivariate case the box of the boxplot changes to a
convex polygon, the bag of bagplot. In the bag are 50 percent
of all points. The fence separates points within the fence from
points outside. It is computed by increasing the
the bag. The loop is defined as the convex hull containing
all points inside the fence.
If all points are on a straight line you get a classical
boxplot.
bagplot()
plots bagplots that are very similar
to the one described in Rousseeuw et al.
Remarks:
The two dimensional median is approximated.
For large data sets the error will be very small.
On the other hand it is not very wise to make a (graphical)
summary of e.g. 10 bivariate data points.
In case you want to plot multiple (overlapping) bagplots,
you may want plots that are semitransparent. For this
you can use the transparency
flag.
If transparency==TRUE
the alpha layer is set to '99' (hex).
This causes the bagplots to appear semitransparent,
but ONLY if the output device is PDF and opened using:
pdf(file="filename.pdf", version="1.4")
.
For this reason, the default is transparency==FALSE
.
This feature as well as the arguments
to specify different colors has been proposed by Wouter Meuleman.
compute.bagplot
returns an object of class
bagplot
that could be plotted by
plot.bagplot()
.
An object of the bagplot class is a list with the following
elements: center
is a two dimensional vector with
the coordinates of the center. hull.center
is a
two column matrix, the rows are the coordinates of the
corners of the center region. hull.bag
and
hull.loop
contain the coordinates of the hull of the bag
and the hull of the loop. pxy.bag
shows you the
coordinates of the points of the bag. pxy.outer
is
the two column matrix of the points that are within the
fence. pxy.outlier
represent the outliers. The vector
hdepths
shows the depths of data points. is.one.dim
is TRUE
if the data set is (nearly) one dimensional.
The dimensionality is decided by analysing the result of prcomp
which is stored in the element prdata
. xy
shows you
the data that are used for the bagplot. In the case of very large
data sets subsets of the data are used for constructing the
bagplot. A data set is very large if there are more data points
than approx.limit
. xydata
are the input data structured
in a two column matrix.
Version of bagplot: 10/2012
Peter Wolf
P. J. Rousseeuw, I. Ruts, J. W. Tukey (1999): The bagplot: a bivariate boxplot, The American Statistician, vol. 53, no. 4, 382–387
# example: 100 random points and one outlier dat<cbind(rnorm(100)+100,rnorm(100)+300) dat<rbind(dat,c(105,295)) bagplot(dat,factor=2.5,create.plot=TRUE,approx.limit=300, show.outlier=TRUE,show.looppoints=TRUE, show.bagpoints=TRUE,dkmethod=2, show.whiskers=TRUE,show.loophull=TRUE, show.baghull=TRUE,verbose=FALSE) # example of Rousseeuw et al., see Rpackage rpart cardata < structure(as.integer( c(2560,2345,1845,2260,2440, 2285, 2275, 2350, 2295, 1900, 2390, 2075, 2330, 3320, 2885, 3310, 2695, 2170, 2710, 2775, 2840, 2485, 2670, 2640, 2655, 3065, 2750, 2920, 2780, 2745, 3110, 2920, 2645, 2575, 2935, 2920, 2985, 3265, 2880, 2975, 3450, 3145, 3190, 3610, 2885, 3480, 3200, 2765, 3220, 3480, 3325, 3855, 3850, 3195, 3735, 3665, 3735, 3415, 3185, 3690, 97, 114, 81, 91, 113, 97, 97, 98, 109, 73, 97, 89, 109, 305, 153, 302, 133, 97, 125, 146, 107, 109, 121, 151, 133, 181, 141, 132, 133, 122, 181, 146, 151, 116, 135, 122, 141, 163, 151, 153, 202, 180, 182, 232, 143, 180, 180, 151, 189, 180, 231, 305, 302, 151, 202, 182, 181, 143, 146, 146)), .Dim = as.integer(c(60, 2)), .Dimnames = list(NULL, c("Weight", "Disp."))) bagplot(cardata,factor=3,show.baghull=TRUE, show.loophull=TRUE,precision=1,dkmethod=2) title("car data Chambers/Hastie 1992") # points of y=x*x bagplot(x=1:30,y=(1:30)^2,verbose=FALSE,dkmethod=2) # one dimensional subspace bagplot(x=1:100,y=1:100)