check.learn: Visual checks for the output of tuneLearn()
In qgam: Smooth Additive Quantile Regression Models

Description Usage Arguments Details Value Author(s) References Examples

Provides some visual plots showing how the calibration criterion and the effective degrees of freedom of each smooth component vary with the learning rate.

1 2	## S3 method for class 'learn' check(obj, sel = 1:2, ...)

`obj`	the output of a call to `tuneLearn`.
`sel`	this function produces two plots, set this parameter to 1 to plot only the first, to 2 to plot only the second or leave it to 1:2 to plot both.
`...`	currently not used, here only for compatibility reasons.

The first plot shows how the calibrations loss, which we are trying to minimize, varies with the log learning rate. This function should look quite smooth, if it doesn't then try to increase err or control$K (the number of bootstrap samples) in the original call to tuneLearn. The second plot shows how the effective degrees of freedom of each smooth term vary with log(sigma). Generally as log(sigma) increases the complexity of the fit decreases, hence the slope is negative.

It produces several plots.

Matteo Fasiolo <matteo.fasiolo@gmail.com>.

Fasiolo, M., Wood, S.N., Zaffran, M., Nedellec, R. and Goude, Y., 2020. Fast calibrated additive quantile regression. Journal of the American Statistical Association (to appear). https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1725521.

library(qgam)
set.seed(525)
dat <- gamSim(1, n=200)
b <- tuneLearn(lsig = seq(-0.5, 1, length.out = 10), 
               y~s(x0)+s(x1)+s(x2)+s(x3), 
               data=dat, qu = 0.5)
check(b)