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qgam

Smooth Additive Quantile Regression Models

Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.

Functions (18)

AUDem

Australian electricity demand data

UKload

UK electricity load data

check

Generic checking function

check.learn

Visual checks for the output of tuneLearn()

check.learnFast

Visual checks for the output of tuneLearnFast()

check.qgam

Some diagnostics for a fitted qgam model

cqcheck

Visually checking a fitted quantile model

cqcheckI

Interactive visual checks for additive quantile fits

elf

Extended log-F model with fixed scale

elflss

Extended log-F model with variable scale

log1pexp

Calculating log(1+exp(x)) accurately

mqgam

Fit multiple smooth additive quantile regression models

pinLoss

Pinball loss function

qdo

Manipulating the output of mqgam

qgam

Fit a smooth additive quantile regression model

sigmoid

Sigmoid function and its derivatives

tuneLearn

Tuning the learning rate for Gibbs posterior

tuneLearnFast

Fast learning rate calibration for the Gibbs posterior

qgam

Smooth Additive Quantile Regression Models

v1.3.3
GPL (>= 2)
Authors
Matteo Fasiolo [aut, cre], Simon N. Wood [ctb], Margaux Zaffran [ctb], Yannig Goude [ctb], Raphael Nedellec [ctb]
Initial release
2021-04-20

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