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

Description Usage Arguments Details Value Author(s) References Examples

Provides some visual checks to verify whether the Brent optimizer used by tuneLearnFast() worked correctly.

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

`obj`	the output of a call to `tuneLearnFast`.
`sel`	integer vector determining which of the plots will be produced. For instance if `sel = c(1, 3)` only the 1st and 3rd plots are showed. No entry of `sel` can be bigger than one plus the number of quantiles considered in the original `tuneLearnFast()` call. That is, if we estimated the learning rate for `qu = c(0.1, 0.4)`, then `max(sel)` must be <= 3.
`...`	currently not used, here only for compatibility reasons.

The top plot in the first page shows the bracket used to estimate log(sigma) for each quantile. The brackets are delimited by the crosses and the red dots are the estimates. If a dot falls very close to one of the crosses, that might indicate problems. The bottom plot shows, for each quantile, the value of parameter err used. Sometimes the algorithm needs to increase err above its user-defined value to achieve convergence. Subsequent plots show, for each quantile, the value of the loss function corresponding to each value of log(sigma) explored by Brent algorithm.

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 <- tuneLearnFast(y ~ s(x0)+s(x1)+s(x2)+s(x3), 
                   data = dat, qu = c(0.4, 0.5), 
                   control = list("tol" = 0.05)) # <- sloppy tolerance to speed-up calibration 
check(b) 
check(b, 3) # Produces only third plot