Smooth Additive Quantile Regression Models
Visual checks for the output of tuneLearn()
Visual checks for the output of tuneLearnFast()
Some diagnostics for a fitted qgam model
Generic checking function
Visually checking a fitted quantile model
Interactive visual checks for additive quantile fits
Extended log-F model with fixed scale
Extended log-F model with variable scale
Calculating log(1+exp(x)) accurately
Fit multiple smooth additive quantile regression models
Pinball loss function
Manipulating the output of mqgam
Fit a smooth additive quantile regression model
Sigmoid function and its derivatives
Tuning the learning rate for Gibbs posterior
Fast learning rate calibration for the Gibbs posterior
Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>. See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. 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.