View source: R/expectreg.boost.R

expectreg.boost | R Documentation |

Generalized additive models are fitted with gradient boosting for optimizing arbitrary loss functions to obtain the graphs of 11 different expectiles for continuous, spatial or random effects.

expectreg.boost(formula, data, mstop = NA, expectiles = NA, cv = TRUE, BoostmaxCores = 1, quietly = FALSE) quant.boost(formula, data, mstop = NA, quantiles = NA, cv = TRUE, BoostmaxCores = 1, quietly = FALSE)

`formula` |
An R formula object consisting of the response variable, '~'
and the sum of all effects that should be taken into consideration (see |

`data` |
data frame (is required). |

`mstop` |
vector, number of bootstrap iterations for each of the 11 quantiles/expectiles that are fitted. Default is 4000. |

`expectiles, quantiles` |
In default setting, the expectiles (0.01,0.02,0.05,0.1,0.2,0.5,0.8,0.9,0.95,0.98,0.99) are calculated. You may specify your own set of expectiles in a vector. |

`cv` |
A cross-validation can determine the optimal amount of boosting iterations between 1 and |

`BoostmaxCores` |
Maximum number of used cores for the different asymmetry parameters |

`quietly` |
If programm should run quietly. |

A (generalized) additive model is fitted using a boosting algorithm based on component-wise univariate base learners.
The base learner can be specified via the formula object. After fitting the model a cross-validation is done using
`cvrisk`

to determine the optimal stopping point for the boosting which results in the best fit.

An object of class 'expectreg', which is basically a list consisting of:

`values` |
The fitted values for each observation and all expectiles, separately in a list for each effect in the model, sorted in order of ascending covariate values. |

`response` |
Vector of the response variable. |

`formula` |
The formula object that was given to the function. |

`asymmetries` |
Vector of fitted expectile asymmetries as given by argument |

`effects` |
List of characters giving the types of covariates. |

`helper` |
List of additional parameters like neighbourhood structure for spatial effects or 'phi' for kriging. |

`fitted` |
Fitted values |

`plot`

, `predict`

, `resid`

, `fitted`

and `effects`

methods are available for class 'expectreg'.

Fabian Otto- Sobotka

Carl von Ossietzky University Oldenburg

https://uol.de

Thomas Kneib, Elmar Spiegel

Georg August University Goettingen

https://www.uni-goettingen.de

Fenske N and Kneib T and Hothorn T (2009)
* Identifying Risk Factors for Severe Childhood Malnutrition
by Boosting Additive Quantile Regression*
Technical Report 052, University of Munich

Sobotka F and Kneib T (2010)
* Geoadditive Expectile Regression *
Computational Statistics and Data Analysis,
doi: 10.1016/j.csda.2010.11.015.

`expectreg.ls`

, `gamboost`

, `bbs`

, `cvrisk`

data("lidar", package = "SemiPar") ex <- expectreg.boost(logratio ~ bbs(range),lidar, mstop=200, expectiles=c(0.1,0.5,0.95),quietly=TRUE) plot(ex)

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