plot.validateFDboost: Methods for objects of class validateFDboost
In FDboost: Boosting Functional Regression Models

mstop.validateFDboost

R Documentation

Methods for objects of class validateFDboost

Description

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

Usage

## 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(0.25, 0.5, 0.75),
  ylim = NULL,
  ...
)

Arguments

`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)`

Details

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.