Description Details Author(s) References Examples
This package implements a penalized Frumento and Bottai's (2015) method for quantile regression coefficient modeling (qrcm), in which quantile regression coefficients are described by (flexible) parametric functions of the order of the quantile. This package fits lasso qrcm using coordinate descent.
Package: | qrcmPen |
Type: | Package |
Version: | 1.0 |
Date: | 2016-10-05 |
License: | GPL-2 |
The function piqr
permits specifying the lasso regression model. The function gof.piqr
permits to select the best tuning parameter through AIC, BIC, GIC and GCV criteria.
The auxiliary functions summary.piqr
, predict.piqr
, and plot.piqr
can be used to extract information from the fitted model.
Gianluca Sottile
Maintainer: Gianluca Sottile <gianluca.sottile@unipa.it>
Frumento, P., and Bottai, M. (2015). Parametric modeling of quantile regression coefficient functions. Biometrics, doi: 10.1111/biom.12410.
Friedman, J., Hastie, T. and Tibshirani, R. (2008). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010.
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