Growth Charts via Smooth Regression Quantiles with Automatic
Smoothness Estimation and Additive Terms
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms via B-splines with L1-norm difference penalties. The smoothing parameters are estimated as part of the model fitting. Monotonicity and concavity constraints on the fitted curves are allowed. See Muggeo, Sciandra, Tomasello and Calvo (2013) <10.1007> and <10.13140> for some code examples.
Smoothing parameter selection with additive terms is discussed in Muggeo and others (2020) <10.1177>.10.1177>10.13140>10.1007>
- Changes in quantregGrowth *
- gcrq() now computes and saves the inverse of transformation function (argument 'transf') to be used when plotting fitted quantiles on the original scale (thanks to Sophie Carles for her input)
- predict.gcrq() did not work for objects including multiple smooth terms (thanks to Sean Fleming for reporting)
- 'newdata' in predic.gcrq may be missing (fitted values are returned)
- plot.gcrq() gains arguments 'smoos' to plot data using a smoothed scatterplot (useful for large datasets)
- bugs fixed in plot.gcrq
- gcrq() now prints a warning message when cv is used to select the smoothing parameter, and the selected lambda is on the boundary of the set range.
- plot.gcrq() gains arguments 'rug' and 'grid'; 'col<0' is allowed to use a color palette for the fitted quantile curves.
in gcrq() an error message is printed if interaction terms with ps() (e.g. "ps(x)*z") are included in the formula.
- plot.gcrq() gains arguments 'conf.level' and 'shade' to portray fitted quantiles along with pointwise confidence intervals (based on cases resampling bootstrap). Thanks to Shervin Asgari for the final input. Also argument 'overlap' has been added to display the legend on the fitted curves.
- predict.gcrq() introduced (it replaces predicQR()). It gains argument 'se.fit' to compute standard errors of the fitted quantiles
- Some minor corrections/changes/bug fixes.
- cv defaults to TRUE when multiple tuning parameter values are provided in ps()
- bugs fixed (in plot.gcrq() 'lwd' was erroneusly affecting the data points)
- predictQR() did not work for objects fitted with a scalar tau (thanks to Michael Frank for reporting)
- some corrections in the help files
- Multiple ps() terms (with known lambda) are allowed in gcrq().
- plot.gcrq() gains argument 'term' to specify which smooth relationship to plot.
- gcrq() gains argument 'eps' to tune the distance between the fitted curves.
- ncross.rq.fitB() deleted; now ncross.rq.fitXB() makes the necessary job.
version 0.2-0 (not on CRAN)
- Nonparametric bootstrap resampling introduced (argument 'n.boot' in gcrq()).
- Methods summary.gcrq() and vcov.gcrq() introduced to return var-cov matrices based on bootstrap.
- Some very minor changes in plot.gcrq() to better control graphical display (when cv=TRUE)
- Some bugs fixed: gcrq() was not working with factors included in the linear predictor;
plot.gcrq() was not working for models including linear terms; the lambda set in ps() is now really lambda (it was actually sqrt(lambda)).
- Argument 'transf' added to cgrq() and plot.gcrq(). This argument can be useful when modelling bounded oucomes (whereby monotone transformations could be employed).
- The returned "gcrq" object now also includes 'fitted.values' and 'residuals'.
- gcrq() was not working with values of tau always greater than .5 (or always less then .5); also a single value of tau was not allowed.
- The objective values for taus < 0.5 returned in the 'rho' component of the fitted "gcrq" object were wrong.
- Some minor graphical arguments added to plot.gcrq() to better control graphical display.
- Argument 'interc' added to cgrq() to include B-splines and the model intercept.
- Some bugs fixed: gcrq() was not working with formulas such as 'gcrq(y~0+x)';
the component info.smooth was not correctly named in "gcrq" objects causing troubles
with predictQR() (thanks to Oscar Camacho for finding the error)
- First public release on CRAN