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 = F)quant.boost(formula, data, mstop = NA, quantiles = NA, cv = TRUE,
BoostmaxCores = 1, quietly = F)
data frame (is required).
vector, number of bootstrap iterations for each of the 11 quantiles/expectiles that are fitted. Default is 4000.
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.
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.
Maximum number of used cores for the different asymmetry parameters
If programm should run quietly.
An object of class 'expectreg', which is basically a list consisting of:
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.
Vector of the response variable.
The formula object that was given to the function.
Vector of fitted expectile asymmetries as given by argument expectiles
.
List of characters giving the types of covariates.
List of additional parameters like neighbourhood structure for spatial effects or 'phi' for kriging.
Fitted values \( \hat{y} \).
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.
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.
# NOT RUN {
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)
# }
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