pls: validationplot – R documentation

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pls

validationplot

Validation Plots

Description

Functions to plot validation statistics, such as RMSEP or R^2, as a function of the number of components.

Usage

validationplot(object, val.type = c("RMSEP", "MSEP", "R2"), estimate,
               newdata, ncomp, comps, intercept, ...)
## S3 method for class 'mvrVal'
plot(x, nCols, nRows, type = "l", lty = 1:nEst,
     lwd = par("lwd"), pch = 1:nEst, cex = 1, col = 1:nEst, legendpos,
     xlab = "number of components", ylab = x$type, main,
     ask = nRows * nCols < nResp && dev.interactive(), ...)

Arguments

`object`	an `mvr` object.
`val.type`	character. What type of validation statistic to plot.
`estimate`	character. Which estimates of the statistic to calculate. See `RMSEP`.
`newdata`	data frame. Optional new data used to calculate statistic.
`ncomp, comps`	integer vector. The model sizes to compute the statistic for. See `RMSEP`.
`intercept`	logical. Whether estimates for a model with zero components should be calculated as well.
`x`	an `mvrVal` object. Usually the result of a `RMSEP`, `MSEP` or `R2` call.
`nCols, nRows`	integers. The number of coloumns and rows the plots will be laid out in. If not specified, `plot.mvrVal` tries to be intelligent.
`type`	character. What type of plots to create. Defaults to `"l"` (lines). Alternative types include `"p"` (points) and `"b"` (both). See `plot` for a complete list of types.
`lty`	vector of line types (recycled as neccessary). Line types can be specified as integers or character strings (see `par` for the details).
`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 `points` for all alternatives.
`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 `par` for the details.
`legendpos`	Legend position. Optional. If present, a legend is drawn at the given position. The position can be specified symbolically (e.g., `legendpos = "topright"`). This requires R >= 2.1.0. Alternatively, the position can be specified explicitly (`legendpos = t(c(x,y))`) or interactively (`legendpos = locator()`). This only works well for plots of single-response models.
`xlab,ylab`	titles for x and y axes. Typically character strings, but can be expressions (e.g., `expression(R^2)` or lists. See `title` for details.
`main`	optional main title for the plot. See Details.
`ask`	logical. Whether to ask the user before each page of a plot.
`...`	Further arguments sent to underlying plot functions.

Details

validationplot calls the proper validation function (currently MSEP, RMSEP or R2) and plots the results with plot.mvrVal. validationplot can be called through the mvr plot method, by specifying plottype = "validation".

plot.mvrVal creates one plot for each response variable in the model, laid out in a rectangle. It uses matplot for performing the actual plotting. If legendpos is given, a legend is drawn at the given position.

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. (To have different settings for the two titles, one can override the par settings with arguments to the plot function.)

Note

legend has many options. If you want greater control over the appearance of the legend, omit the legendpos argument and call legend manually.

Author(s)

Ron Wehrens and Bjørn-Helge Mevik

Examples

data(oliveoil)
mod <- plsr(sensory ~ chemical, data = oliveoil, validation = "LOO")
## Not run: 
## These three are equivalent:
validationplot(mod, estimate = "all")
plot(mod, "validation", estimate = "all")
plot(RMSEP(mod, estimate = "all"))
## Plot R2:
plot(mod, "validation", val.type = "R2")
## Plot R2, with a legend:
plot(mod, "validation", val.type = "MSEP", legendpos = "top") # R >= 2.1.0

## End(Not run)

pls

Partial Least Squares and Principal Component Regression

v2.7-3

GPL-2

Authors

Bjørn-Helge Mevik [aut, cre], Ron Wehrens [aut], Kristian Hovde Liland [aut], Paul Hiemstra [ctb]

Initial release

2020-08-04