expectreg.boost: Quantile and expectile regression using boosting In expectreg: Expectile and Quantile Regression

Description

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.

Usage

 1 2 3 4 5 expectreg.boost(formula, data, mstop = NA, expectiles = NA, cv = TRUE, BoostmaxCores = 1, quietly = F) quant.boost(formula, data, mstop = NA, quantiles = NA, cv = TRUE, BoostmaxCores = 1, quietly = F) 

Arguments

 formula An R formula object consisting of the response variable, '~' and the sum of all effects that should be taken into consideration (see gamboost). Each effect can be linear or represented through a nonlinear or spatial base (see bbs). Each variable has to be named consistently with data. 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 mstop. Uses cvrisk. If set to FALSE, the results from mstop iterations are used. BoostmaxCores Maximum number of used cores for the different asymmetry parameters quietly If programm should run quietly.

Details

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.

Value

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 expectiles. 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 \hat{y} .

plot, predict, resid, fitted and effects methods are available for class 'expectreg'.

Author(s)

Fabian Otto- Sobotka
Carl von Ossietzky University Oldenburg
http://www.uni-Oldenburg.de

Thomas Kneib, Elmar Spiegel
Georg August University Goettingen
http://www.uni-goettingen.de

References

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
 1 2 3 4 5 6 7 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) qx <- quant.boost(logratio~bbs(range),lidar,mstop=200,quantiles=c(0.1,0.5,0.95),quietly=TRUE) plot(qx)