Description Usage Arguments Details Value Note Author(s) References See Also Examples
Functions to make scatter plots of scores or correlation loadings, and scatter or line plots of loadings.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  scoreplot(object, ...)
## Default S3 method:
scoreplot(object, comps = 1:2, labels, identify = FALSE, type = "p",
xlab, ylab, ...)
## S3 method for class 'scores'
plot(x, ...)
loadingplot(object, ...)
## Default S3 method:
loadingplot(object, comps = 1:2, scatter = FALSE, labels,
identify = FALSE, type, lty, lwd = NULL, pch, cex = NULL,
col, legendpos, xlab, ylab, pretty.xlabels = TRUE, xlim, ...)
## S3 method for class 'loadings'
plot(x, ...)
corrplot(object, comps = 1:2, labels, plotx = TRUE, ploty = FALSE,
radii = c(sqrt(1/2), 1), identify = FALSE, type = "p",
xlab, ylab, col, ...)

object 
an R object. The fitted model. 
comps 
integer vector. The components to plot. 
scatter 
logical. Whether the loadings should be plotted as a scatter instead of as lines. 
labels 
optional. Alternative plot labels or x axis labels. See Details. 
plotx 
locical. Whether to plot the X correlation
loadings. Defaults to 
ploty 
locical. Whether to plot the Y correlation
loadings. Defaults to 
radii 
numeric vector, giving the radii of the circles drawn in

identify 
logical. Whether to use 
type 
character. What type of plot to make. Defaults to

lty 
vector of line types (recycled as neccessary). Line types can be
specified as integers or character strings (see 
lwd 
vector of positive numbers (recycled as neccessary), giving the width of the lines. 
pch 
plot character. A character string or a vector of
single characters or integers (recycled as neccessary). See

cex 
numeric vector of character expansion sizes (recycled as neccessary) for the plotted symbols. 
col 
character or integer vector of colors for plotted lines and
symbols (recycled as neccessary). See 
legendpos 
Legend position. Optional. Ignored if 
xlab,ylab 
titles for x and y axes. Typically
character strings, but can be expressions or lists. See

pretty.xlabels 
logical. If 
xlim 
optional vector of length two, with the x limits of the plot. 
x 
a 
... 
further arguments sent to the underlying plot function(s). 
plot.scores
is simply a wrapper calling scoreplot
,
passing all arguments. Similarly for plot.loadings
.
scoreplot
is generic, currently with a default method that
works for matrices and any object for which scores
returns a matrix. The default scoreplot
method
makes one or more scatter plots of the scores,
depending on how many components are selected. If one or two
components are selected, and identify
is TRUE
, the
function identify
is used to interactively identify
points.
Also loadingplot
is generic, with a default method that works
for matrices and any object where loadings
returns a
matrix. If scatter
is TRUE
, the default method works exactly
like the default scoreplot
method. Otherwise, it makes a lineplot of the selected
loading vectors, and if identify
is TRUE
,
uses identify
to interactively identify points. Also,
if legendpos
is given, a legend is drawn at the position
indicated.
corrplot
works exactly like the default scoreplot
method, except that at least two components must be selected. The
“correlation loadings”, i.e. the correlations between each
variable and the selected components (see References), are plotted as
pairwise scatter plots, with concentric circles of radii given by
radii
. Each point corresponds to a variable. The squared
distance between the point and origin equals the fraction of the
variance of the variable explained by the components in the panel.
The default radii
corresponds to 50% and 100% explained
variance. By default, only the correlation loadings of the X
variables are plotted, but if ploty
is TRUE
, also the
Y correlation loadings are plotted.
scoreplot
, loadingplot
and corrplot
can also be
called through the plot method for mvr
objects, by specifying
plottype
as "scores"
, "loadings"
or
"correlation"
, respectively. See plot.mvr
.
The argument labels
can be a vector of labels or one of
"names"
and "numbers"
.
If a scatter plot is produced (i.e., scoreplot
, corrplot
, or
loadingplot
with scatter = TRUE
), the labels
are used instead of plot symbols for the points plotted. If
labels
is "names"
or "numbers"
, the row
names or row numbers of the matrix (scores, loadings or correlation
loadings) are used.
If a line plot is produced (i.e., loadingplot
), the labels are
used as x axis labels. If labels
is "names"
or
"numbers"
, the variable names are used as labels, the
difference being that with "numbers"
, the variable names are
converted to numbers, if possible. Variable names of the forms
"number" or "number text" (where the space is optional),
are handled.
The argument pretty.xlabels
is only used when labels
is
specified for a line plot. If TRUE
(default), the code tries
to use a ‘pretty’ selection of labels. If labels
is
"numbers"
, it also uses the numerical values of the labels for
horisontal spacing. If one has excluded parts of the spectral
region, one might therefore want to use pretty.xlabels = FALSE
.
The functions return whatever the underlying plot function (or
identify
) returns.
legend
has many options. If you want greater
control over the appearance of the legend, omit the legendpos
argument and call legend
manually.
Graphical parametres (such as pch
and cex
) can also
be used with scoreplot
and corrplot
. They are not
listed in the argument list simply because they are not handled
specifically in the function (unlike in loadingplot
), but
passed directly to the underlying plot functions by ...
.
Tip: If the labels specified with labels
are too long, they get
clipped at the border of the plot region. This can be avoided by
supplying the graphical parameter xpd = TRUE
in the plot call.
The handling of labels
and pretty.xlabels
in
coefplot
is experimental.
Ron Wehrens and BjørnHelge Mevik
Martens, H., Martens, M. (2000) Modified Jackknife Estimation of Parameter Uncertainty in Bilinear Modelling by Partial Least Squares Regression (PLSR). Food Quality and Preference, 11(1–2), 5–16.
mvr
, plot.mvr
,
scores
, loadings
, identify
,
legend
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  data(yarn)
mod < plsr(density ~ NIR, ncomp = 10, data = yarn)
## These three are equivalent:
## Not run:
scoreplot(mod, comps = 1:5)
plot(scores(mod), comps = 1:5)
plot(mod, plottype = "scores", comps = 1:5)
loadingplot(mod, comps = 1:5)
loadingplot(mod, comps = 1:5, legendpos = "topright") # With legend
loadingplot(mod, comps = 1:5, scatter = TRUE) # Plot as scatterplots
corrplot(mod, comps = 1:2)
corrplot(mod, comps = 1:3)
## End(Not run)

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