Methods for objects that are fitted to determine the optimal mstop and the prediction error of a model fitted by FDboost.

```
# S3 method for validateFDboost
mstop(object, riskopt = c("mean", "median"), ...)
```# S3 method for validateFDboost
print(x, ...)

# S3 method for validateFDboost
plot(
x,
riskopt = c("mean", "median"),
ylab = attr(x, "risk"),
xlab = "Number of boosting iterations",
ylim = range(x$oobrisk),
which = 1,
modObject = NULL,
predictNA = FALSE,
names.arg = NULL,
ask = TRUE,
...
)

plotPredCoef(
x,
which = NULL,
pers = TRUE,
commonRange = TRUE,
showNumbers = FALSE,
showQuantiles = TRUE,
ask = TRUE,
terms = TRUE,
probs = c(0.25, 0.5, 0.75),
ylim = NULL,
...
)

object

object of class `validateFDboost`

riskopt

how the risk is minimized to obtain the optimal stopping iteration; defaults to the mean, can be changed to the median.

...

additional arguments passed to callies.

x

an object of class `validateFDboost`

.

ylab

label for y-axis

xlab

label for x-axis

ylim

values for limits of y-axis

which

In the case of `plotPredCoef()`

the subset of base-learners to take into account for plotting.
In the case of `plot.validateFDboost()`

the diagnostic plots that are given
(1: empirical risk per fold as a funciton of the boosting iterations,
2: empirical risk per fold, 3: MRD per fold,
4: observed and predicted values, 5: residuals;
2-5 for the model with the optimal number of boosting iterations).

modObject

if the original model object of class `FDboost`

is given
predicted values of the whole model can be compared to the predictions of the cross-validated models

predictNA

should missing values in the response be predicted? Defaults to `FALSE`

.

names.arg

names of the observed curves

ask

defaults to `TRUE`

, ask for next plot using `par(ask = ask)`

?

pers

plot coefficient surfaces as persp-plots? Defaults to `TRUE`

.

commonRange,

plot predicted coefficients on a common range, defaults to `TRUE`

.

showNumbers

show number of curve in plot of predicted coefficients, defaults to `FALSE`

showQuantiles

plot the 0.05 and the 0.95 Quantile of coefficients in 1-dim effects.

terms

logical, defaults to `TRUE`

; plot the added terms (default) or the coefficients?

probs

vector of quantiles to be used in the plotting of 2-dimensional coefficients surfaces,
defaults to `probs = c(0.25, 0.5, 0.75)`

The function `mstop.validateFDboost`

extracts the optimal mstop by minimizing the
mean (or the median) risk.
`plot.validateFDboost`

plots cross-validated risk, RMSE, MRD, measured and predicted values
and residuals as determined by `validateFDboost`

. The function `plotPredCoef`

plots the
coefficients that were estimated in the folds - only possible if the argument getCoefCV is `TRUE`

in
the call to `validateFDboost`

.