A set of routines for plotting, identifying, or highlighting points in a multidimensional fuzzy set ordination (MFSO).
# S3 method for mfso
plot(x, dis=NULL, pch=1, ax=NULL, ay=NULL, ...)
# S3 method for mfso
points(x, overlay, col=2, pch=1, ...)
# S3 method for mfso
plotid(ord, dis=NULL, labels=NULL, ...)
# S3 method for mfso
hilight(ord, overlay, cols = c(2, 3, 4, 5,
6, 7), symbol = c(1, 3, 5), ...)
# S3 method for mfso
chullord(ord, overlay, cols = c(2, 3, 4, 5,
6, 7), ltys = c(1, 2, 3), ...)
# S3 method for mfso
boxplot(x, ...)
# S3 method for mfso
thull(ord,var,grain,ax=1,ay=2,col=2,grid=50,
nlevels=5,levels=NULL,lty=1,numitr=100,...)none
an object of class ‘mfso’
X axis number
Y axis number
an object of class ‘mfso’
an object of class ‘mfso’
an object of class ‘dist’ from dist,
dsvdis, or
‘vegdist’
a logical vector of the same length as the number of points in the plot
a vector of labels to print next to the identified points
an integer or vector of integers to control which symbols
are printed in which order on the plot by specifying values to
pch
an integer or vector of integers to control the line styles of convex hull polygons
the symbol to plot
the color to use for plotted symbols
an integer vector for color order
a variable to fit with a tensioned hull
the size of the moving window used to calculate the tensioned hull
the number of cells in the image version of the tensioned hull
the number of contour levels to plot the tensioned hull
a logical variable to control plotting the contours on the tensioned hull
the line type to use in drawing the contours
the number of random iterations to use to compute the probability of obtaining as small a tensioned hull as observed
arguments to pass to function points
David W. Roberts droberts@montana.edu
Multidimensional fuzzy set ordinations (MFSO) are almost inherently graphical, and routines to facilitate plotting and overlaying are essential to work effectively with them.
A multidimensional fuzzy set ordination object (an object of class
‘mfso’) generally contains at least two axes, and may contain many more.
By default, the plot routine plots all possible axis pairs in order.
If ‘ax’ and ‘ay’ are specified only a single plot is produced
with X axis ax and Y axis ay. If
‘dist’ object is passed with the ‘dis=’ argument, the final panel
is a plot of the dissimilarity or distance matrix values on the X axis and the
pair-wise ordination distances on the Y axis with the correlation coefficient in
the upper left hand corner.
The ‘points’ function can be used to highlight or identify specific points in the plot. The ‘points’ function requires a logical vector (TRUE/FALSE) of the same length as the number of points in the plot. The default behavior is to color the points with a respective TRUE value red. It is possible to control the color (with col=), size (with cex=) and symbol (with pch=) of the points.
The ‘plotid’ function can be used to label or identify specific points with the mouse. Clicking the left mouse button adjacent to a point causes the point to be labeled, offset in the direction of the click relative to the point. Clicking the right mouse button exits the routine. The default (labels=NULL) is to label points with the row number in the data.frame (or position in the vector) for the point. Alternatively, specifying a vector of labels (labels=) prints the respective labels. If the data were derived from a data.frame, the row.names of the data.frame are often a good choice, but the labels can also be used with a factor vector to identify the distribution of values of a factor in the ordination (but see hilight as well).
The ‘hilight’ function identifies the factor values of points in the ordination, using color and symbols to identify unique values (up to 18 values by default). The colors and symbols used can be specified by the ‘col=’ and ‘symbol=’ arguments, which should both be integers or integer vectors. The default of colors 2, 3, 4, 5, 6, 7 and symbols 1, 3, 5 shows well in most cases, but on colored backgrounds you may need to adjust ‘col=’. If you have a factor with more than 18 classes you will need to augment the ‘symbol=’ vector with more values.
The ‘chullord’ function plots convex hulls around all points sharing the
same value for a factor variable, and colors all points of that value to match.
The convention on colors follows
hilight.
The ‘boxplot’ function plots boxplots of the \(\mu\) membership values in the MFSO.
The ‘thull’ funntion drapes a tensioned hull for variable ‘var’ over the plotted mfso.
require(labdsv) # to obtain access to data sets and dissimilarity function
data(bryceveg) # vegetation data
data(brycesite) # environmental data
dis.bc <- dsvdis(bryceveg,'bray/curtis') # produce \sQuote{dist} object
demo.mfso <- mfso(~elev+slope+av,dis.bc,data=brycesite)
plot(demo.mfso)
if (FALSE) hilight(demo.mfso,brycesite$quad) # requires interaction
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