validationplot {pls} | R Documentation |
Functions to plot validation statistics, such as RMSEP or R2, as a function of the number of components.
validationplot(object, val.type = c("RMSEP", "MSEP", "R2"), estimate, newdata, comps, intercept, ...) ## S3 method for class 'mvrVal': plot(x, nCols, nRows, type = "l", ...)
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. |
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). |
... |
Further arguments sent to underlying plot functions. |
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.
The functions do not return any values.
The handling of optional arguments (...) between these functions is a bit rough, and not thoroughly tested.
Ron Wehrens and Bjørn-Helge Mevik
mvr
, plot.mvr
, RMSEP
,
MSEP
, R2
data(sensory) mod <- plsr(Panel ~ Quality, data = sensory, 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") ## End(Not run)