Functions to make scatter plots of scores or correlation loadings, and scatter or line plots of loadings.
scoreplot(object, ...)# S3 method for default
scoreplot(
object,
comps = 1:2,
labels,
identify = FALSE,
type = "p",
xlab,
ylab,
...
)
# S3 method for scores
plot(x, ...)
loadingplot(object, ...)
# S3 method for default
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 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,
...
)
The functions return whatever the underlying plot function (or
identify) returns.
an object. The fitted model.
further arguments sent to the underlying plot function(s).
integer vector. The components to plot.
optional. Alternative plot labels or \(x\) axis labels. See Details.
logical. Whether to use identify to interactively
identify points. See below.
character. What type of plot to make. Defaults to "p"
(points) for scatter plots and "l" (lines) for line plots. See
plot for a complete list of types (not all types are
possible/meaningful for all plots).
titles for \(x\) and \(y\) axes. Typically character
strings, but can be expressions or lists. See title for
details.
a scores or loadings object. The scores or loadings
to plot.
logical. Whether the loadings should be plotted as a scatter instead of as lines.
vector of line types (recycled as neccessary). Line types can be
specified as integers or character strings (see par for the
details).
vector of positive numbers (recycled as neccessary), giving the width of the lines.
plot character. A character string or a vector of single
characters or integers (recycled as neccessary). See points
for all alternatives.
numeric vector of character expansion sizes (recycled as neccessary) for the plotted symbols.
character or integer vector of colors for plotted lines and
symbols (recycled as neccessary). See par for the details.
Legend position. Optional. Ignored if scatter is
TRUE. If present, a legend is drawn at the given position. The
position can be specified symbolically (e.g., legendpos =
"topright"). This requires >= 2.1.0. Alternatively, the position can be
specified explicitly (legendpos = t(c(x,y))) or interactively
(legendpos = locator()).
logical. If TRUE, loadingplot tries to
plot the \(x\) labels more nicely. See Details.
optional vector of length two, with the \(x\) limits of the plot.
locical. Whether to plot the \(X\) correlation loadings.
Defaults to TRUE.
locical. Whether to plot the \(Y\) correlation loadings.
Defaults to FALSE.
numeric vector, giving the radii of the circles drawn in
corrplot. The default radii represent 50% and 100% explained
variance of the \(X\) variables by the chosen components.
Ron Wehrens and Bjørn-Helge Mevik
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.
Martens, H., Martens, M. (2000) Modified Jack-knife Estimation of Parameter Uncertainty in Bilinear Modelling by Partial Least Squares Regression (PLSR). Food Quality and Preference, 11(1--2), 5--16.
data(yarn)
mod <- plsr(density ~ NIR, ncomp = 10, data = yarn)
## These three are equivalent:
if (FALSE) {
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)
}
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