A set of routines for plotting, highlighting points, or adding fitted surfaces to PCOs.
# S3 method for pco
plot(x, ax = 1, ay = 2, col = 1, title = "", pch = 1, …)
# S3 method for pco
points(x, which, ax = 1, ay = 2, col = 2, pch = 1, cex = 1, …)
# S3 method for pco
plotid(ord, ids = seq(1:nrow(ord$points)), ax = 1, ay = 2,
col = 1, …)
# S3 method for pco
hilight(ord, overlay, ax = 1, ay = 2, title="", cols=c(2,3,4,5,6,7),
glyph=c(1,3,5), …)
# S3 method for pco
chullord(ord, overlay, ax = 1, ay = 2, cols=c(2,3,4,5,6,7),
ltys = c(1,2,3), …)
# S3 method for pco
density(ord, overlay, ax = 1, ay = 2, cols = c(2, 3, 4, 5,
6, 7), ltys = c(1, 2, 3), numitr=100, ...)
# S3 method for pco
surf(ord, var, ax = 1, ay = 2, thinplate=TRUE, col = 2, labcex = 0.8,
family = gaussian, grid=50, gamma=1, …)
# S3 method for pco
thull(ord,var,grain,ax=1,ay=2,col=2,grid=50,nlevels=5,levels=NULL,
lty=1,numitr=100,...)an object of class ‘pco’
the dimension to use for the X axis
the dimension to use for the Y axis
a title for the plot
a logical variable to specify points to be highlighted
an object of class ‘pco’
a factor or integer vector to hilight or distinguish
the sequence of color indices to be used
the sequence of glyphs (pch) to be used
the sequence of line types to be used
number of iterations to use in estimating the probability of obtaining the observed density
a variable to be surfaced
a logical variable to control how the surface is fit: thinplate = TRUE (the default) fits a thinplate spline, thinplate = FALSE fits independent smooth splines. If you have too few data points you may have to specify thinplate-FALSE
controls the link function passed to ‘gam’: one of ‘gaussian’, ‘binomial’, or ‘poisson’
controls the smoothness of the fit from gam
the number of X and Y points to use in establishing a grid for surf
identifier labels for samples. Defaults to 1:n
color index for points or contours
size of contour interval labels
plot character: glyph to plot
character expansion factor: size of plotted characters
the size of moving window to use in calculating the tensioned hull
the number of contour intervals to draw on the tensioned hull
specific levels to use in drawing the tensioned hull
the line type to use in drawing the tensioned hull contours
arguments to pass to the plot function
Function ‘plotid’ returns a vector of row numbers of identified plots
Function ‘plot’ produces a scatterplot of sample scores for the specified axes, erasing or over-plotting on the current graphic device. Axes dimensions are controlled to produce a graph with the correct aspect ratio.
Functions ‘points’, ‘plotid’, ‘hilight’, ‘chullord’, and ‘surf’ add detail to an existing plot. The axes specified must match the underlying plot exactly.
Function ‘plotid’ identifies and labels samples (optionally with values from a third vector) in the PCO, and requires interaction with the mouse: left button identifies, right button exits.
Function ‘points’ is passed a logical vector to identify a set of samples by color of glyph. It can be used to identify a single set meeting almost any criterion that can be stated as a logical expression.
Function ‘hilight’ is passed a factor vector or integer vector, and identifies factor values by color and glyph. By specifying values for arguments ‘cols’ and ‘glyph’ it is possible to control the sequence of colors and pch glyphs used in the hilight.
Function ‘chullord’ is passed a factor vector or integer vector, and plots a convex hull around all points in each factor class. By specifying values for arguments ‘cols’ and ‘ltys’ it is possible to control the sequence of colors and linetypes of the convex hulls.
Function ‘density’ calculates the fraction of points within the convex hull that belong to the specified type.
Function ‘surf’ calculates and plots fitted surfaces for logical or
quantitative variables. The function employs the gam
function to fit a variable to the ordination coordinates, and to predict the
values at all grid points. The grid is established with the
‘expand.grid’ function, and the grid is then specified in a call to
‘predict.gam’. The predicted values are trimmed to the the convex hull
of the data, and the contours are fit by ‘contour’. The default link
function for fitting the GAMs is ‘gaussian’, suitable for unbounded
continuous variables. For logical variables you should specify ‘family
= binomial’ to get a logistic GAM, and for integer counts you should specify
‘family = poisson’ to get a Poisson GAM.
# NOT RUN {
data(bryceveg)
data(brycesite)
dis.bc <- dsvdis(bryceveg,'bray/curtis')
pco.1 <- pco(dis.bc,5)
plot(pco.1)
points(pco.1,brycesite$elev>8000)
surf(pco.1,brycesite$elev)
# }
# NOT RUN {
plotid(pco.1,ids=row.names(bryceveg))
# }
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