check.qgam: Some diagnostics for a fitted qgam model
In qgam: Smooth Additive Quantile Regression Models

Description Usage Arguments Details Value Author(s) References Examples

Takes a fitted gam object produced by qgam() and produces some diagnostic information about the fitting procedure and results. It is partially based on mgcv::gam.check.

1 2	## S3 method for class 'qgam' check(obj, nbin = 10, lev = 0.05, ...)

`obj`	the output of a `qgam()` call.
`nbin`	number of bins used in the internal call to `cqcheck()`.
`lev`	the significance levels used by `cqcheck()`, which determines the width of the confidence intervals.
`...`	extra arguments to be passed to `plot()`

This function provides two plots. The first shows how the number of responses falling below the fitted quantile (y-axis) changes with the fitted quantile (x-axis). To be clear: if the quantile is fixed to, say, 0.5 we expect 50% of the responses to fall below the fit. See ?cqcheck() for details. The second plot related to |F(hat(mu)) - F(mu0)|, which is the absolute bias attributable to the fact that qgam is using a smoothed version of the pinball-loss. The absolute bias is evaluated at each observation, and an histogram is produced. See Fasiolo et al. (2017) for details. The function also prints out the integrated absolute bias, and the proportion of observations lying below the regression line. It also provides some convergence diagnostics (regarding the optimization), which are the same as in mgcv::gam.check. It reports also the maximum (k') and the selected degrees of freedom of each smooth term.

Simply produces some plots and prints out some diagnostics.

Matteo Fasiolo <matteo.fasiolo@gmail.com>, Simon N. Wood.

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(0)
dat <- gamSim(1, n=200)
b<-qgam(y~s(x0)+s(x1)+s(x2)+s(x3), data=dat, qu = 0.5)
plot(b, pages=1)
check.qgam(b, pch=19, cex=.3)