predplot.RdFunctions to plot predicted values against measured values for a fitted model.
predplot(object, ...) # S3 method for default predplot(object, ...) # S3 method for mvr predplot(object, ncomp = object$ncomp, which, newdata, nCols, nRows, xlab = "measured", ylab = "predicted", main, ask = nRows * nCols < nPlots && dev.interactive(), ..., font.main, cex.main) predplotXy(x, y, line = FALSE, labels, type = "p", main = "Prediction plot", xlab = "measured response", ylab = "predicted response", line.col = par("col"), line.lty = NULL, line.lwd = NULL, ...)
| object | a fitted model. |
|---|---|
| ncomp | integer vector. The model sizes (numbers of components) to use for prediction. |
| which | character vector. Which types of predictions to plot.
Should be a subset of |
| newdata | data frame. New data to predict. |
| nCols, nRows | integer. The number of coloumns and rows the
plots will be laid out in. If not specified, |
| xlab,ylab | titles for \(x\) and \(y\) axes. Typically
character strings, but can be expressions or lists. See
|
| labels | optional. Alternative plot labels to use. Either a
vector of labels, or |
| type | character. What type of plot to make. Defaults to
|
| main | optional main title for the plot. See Details. |
| ask | logical. Whether to ask the user before each page of a plot. |
| font.main | font to use for main titles. See |
| cex.main | numeric. The magnification to be used for main titles relative to the current size. Also see Details below. |
| x | numeric vector. The observed response values. |
| y | numeric vector. The predicted response values. |
| line | logical. Whether a target line should be drawn. |
| line.col, line.lty, line.lwd | character or numeric. The
|
| ... | further arguments sent to underlying plot functions. |
predplot is a generic function for plotting predicted versus
measured response values, with default and mvr methods
currently implemented. The default method is very simple, and doesn't
handle multiple responses or new data.
The mvr method, handles multiple responses,
model sizes and types of predictions by making one plot for each
combination. It can also be called through the plot method for
mvr, by specifying plottype = "prediction" (the
default).
The argument main can be used to specify the main title of the
plot. It is handled in a non-standard way. If there is only on (sub)
plot, main will be used as the main title of the plot. If
there is more than one (sub) plot, however, the presence of
main will produce a corresponding ‘global’ title on the
page. Any graphical parametres, e.g., cex.main, supplied to
coefplot will only affect the ‘ordinary’ plot titles,
not the ‘global’ one. Its appearance can be changed by setting
the parameters with par, which will affect both
titles (with the exception of font.main and cex.main,
which will only affect the ‘global’ title when there is more
than one plot). (To have different settings for the two titles, one can
override the par settings with arguments to predplot.)
predplotXy is an internal function and is not meant for
interactive use. It is called by the predplot methods, and its
arguments, e.g, line, can be given in the predplot call.
The functions invisibly return a matrix with the (last) plotted data.
The font.main and cex.main must be (completely) named.
This is to avoid that any argument cex or font matches
them.
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.
data(yarn) mod <- plsr(density ~ NIR, ncomp = 10, data = yarn[yarn$train,], validation = "CV") if (FALSE) { predplot(mod, ncomp = 1:6) plot(mod, ncomp = 1:6) # Equivalent to the previous ## Both cross-validated and test set predictions: predplot(mod, ncomp = 4:6, which = c("validation", "test"), newdata = yarn[!yarn$train,]) } data(oliveoil) mod.sens <- plsr(sensory ~ chemical, ncomp = 4, data = oliveoil) if (FALSE) plot(mod.sens, ncomp = 2:4) # Several responses gives several plots