labdsv (version 1.2-0)

plot.pca: Plotting Routines For Principal Components Ordinations

Description

A set of routines for plotting, highlighting points, or adding fitted surfaces to PCAs.

Usage

## S3 method for class 'pca':
plot(x, ax = 1, ay = 2, col = 1, title = "", pch = 1, \dots)
## S3 method for class 'pca':
points(x, which, ax = 1, ay = 2, col = 2, pch = 1, cex = 1, \dots)
## S3 method for class 'pca':
plotid(ord, ids = seq(1:nrow(ord$scores)), ax = 1, ay = 2, 
    col = 1, ...)
## S3 method for class 'pca':
hilight(ord, overlay, ax = 1, ay = 2, cols=c(2,3,4,5,6,7), glyph=c(1,3,5), 
    origpch = 1, blank = '#FFFFFF', ...)
## S3 method for class 'pca':
chullord(ord, overlay, ax = 1, ay = 2, cols=c(2,3,4,5,6,7), ltys = c(1,2,3), ...)
## S3 method for class 'pca':
surf(ord, var, ax = 1, ay = 2, col = 2, labcex = 0.8, 
    family = gaussian, \dots)
## S3 method for class 'pca':
jsurf(ord, var, ax = 1, ay = 2, col = 2, labcex = 0.8, 
    family = gaussian, \dots)
## S3 method for class 'pca':
varplot(x, dim=length(x$sdev))

Arguments

x

an object of class pca

ax

the dimension to use for the X axis

ay

the dimension to use for the Y axis

title

a title for the plot

which

a logical variable to specify points to be highlighted

ord

an object of class pca

overlay

a factor or integer vector to hilight or distinguish

cols

the sequence of color indices to be used

glyph

the sequence of glyphs (pch) to be used

origpch

the pch number of the glyph employed in the original plot (to be obliterated by blank)

blank

the color to use to erase the glyphs of the original plot

ltys

the sequence of line types to be used

var

a variable to be surfaced

family

controls the link function passed to gam: one of gaussian, binomial, or poisson

ids

identifier labels for samples. Defaults to 1:n

dim

number of dimensions to include in barplot

col

color index for points or contours

labcex

size of contour interval labels

pch

plot character: glyph to plot

cex

character expansion factor: size of plotted characters

...

arguments to pass to the plot function

Value

• Function plotid returns a vector of row numbers of identified plots.

  Function varplot.pca produces two plots: (1) the variance accounted for by eigenvector up to the specified number of dimensions (default = all), and (2) the cumulative variance accounted for by dimension.

Details

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, surf, and jsurf 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.

Functions surf and jsurf calculate and plot fitted surfaces for logical or quantitative variables; jsurf jitters the coordinates to avoid problems with identical values in fitting the surface. The functions are simply wrappers for the gam function, and send the sample coordinates and the var variable to gam to model. The coordinates and the fitted values from gam are then passed to interp, which plots the contours. 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.

References

http://ecology.msu.montana.edu/labdsv/R

Examples

data(bryceveg)
data(brycesite)
pca.1 <- pca(bryceveg)
plot(pca.1)
points(pca.1,brycesite$elev>8000)
surf(pca.1,brycesite$elev)
plotid(pca.1,ids=row.names(bryceveg))