Description Usage Arguments Details

View source: R/crossvalidation.R

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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