R: Visual checks for the output of tuneLearn()

check.learn {qgam} | R Documentation

## Visual checks for the output of tuneLearn()

### Description

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

### Usage

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

### Arguments

`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.

### Details

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.

### Value

It produces several plots.

### Author(s)

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

### References

Fasiolo, M., Goude, Y., Nedellec, R. and Wood, S. N. (2017). Fast calibrated additive quantile regression. Available at https://arxiv.org/abs/1707.03307.

### Examples

    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)

[Package qgam version 1.3.2 Index]