Description Usage Arguments Details
Methods for objects that are fitted to determine the optimal mstop and the prediction error of a model fitted by FDboost.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## S3 method for class 'validateFDboost'
mstop(object, riskopt = c("mean", "median"), ...)
## S3 method for class 'validateFDboost'
print(x, ...)
## S3 method for class '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(0.05, 0.5, 0.95), 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 |
ylab |
label for y-axis |
xlab |
label for x-axis |
ylim |
values for limits of y-axis |
which |
In the case of |
modObject |
if the original model object of class |
predictNA |
should missing values in the response be predicted? Defaults to |
names.arg |
names of the observed curves |
ask |
defaults to |
pers |
plot coefficient surfaces as persp-plots? Defaults to |
commonRange, |
plot predicted coefficients on a common range, defaults to |
showNumbers |
show number of curve in plot of predicted coefficients, defaults to |
showQuantiles |
plot the 0.05 and the 0.95 Quantile of coefficients in 1-dim effects. |
terms |
logical, defaults to |
probs |
vector of quantiles to be used in the plotting of 2-dimensional coefficients surfaces,
defaults to |
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
.
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