pls (version 2.1-0)

scoreplot: Plots of Scores, Loadings and Correlation Loadings

Description

Functions to make scatter plots of scores or correlation loadings, and scatter or line plots of loadings.

Usage

scoreplot(object, ...)
## S3 method for class 'default':
scoreplot(object, comps = 1:2, labels, identify = FALSE, type = "p",
          xlab, ylab, \dots)
## S3 method for class 'scores':
plot(x, \dots)
loadingplot(object, ...)
## S3 method for class '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, \dots)
## S3 method for class 'loadings':
plot(x, \dots)
corrplot(object, comps = 1:2, labels, radii = c(sqrt(1/2), 1),
         identify = FALSE, type = "p", xlab, ylab, ...)

Value

• The functions return whatever the underlying plot function (or identify) returns.

encoding

latin1

Details

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 an $X$ 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.

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.

References

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.

See Also

mvr, plot.mvr, scores, loadings, identify, legend

Examples

data(yarn)
mod <- plsr(density ~ NIR, ncomp = 10, data = yarn)
## These three are equivalent:
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)