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 of class
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.
an object of class
label for y-axis
label for x-axis
values for limits of y-axis
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).
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
should missing values in the response be predicted? Defaults to
names of the observed curves
TRUE, ask for next plot using
par(ask = ask) ?
plot coefficient surfaces as persp-plots? Defaults to
plot predicted coefficients on a common range, defaults to
show number of curve in plot of predicted coefficients, defaults to
plot the 0.05 and the 0.95 Quantile of coefficients in 1-dim effects.
logical, defaults to
TRUE; plot the added terms (default) or the coefficients?
vector of quantiles to be used in the plotting of 2-dimensional coefficients surfaces,
probs = c(0.25, 0.5, 0.75)
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
the call to