alqrfe-package {alqrfe} | R Documentation |

## Adaptive Lasso Quantile Regression with Fixed Effects

### Description

Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.

### Package Content

Index of help topics:

alqrfe-package Adaptive Lasso Quantile Regression with Fixed Effects bic_hat Bayesian Information Criteria clean_data Clean missings df_hat degrees of fredom f_den Kernel density f_tab Tabular function loss_alqr Loss adaptive lasso quantile regression with fixed effects loss_lqr Loss lasso quantile regression with fixed effects loss_qr Loss quantile regression loss_qrfe Loss quantile regression with fixed effects make_z Incident matrix Z mqr multiple penalized quantile regression mqr_alpha multiple penalized quantile regression - alpha optim_alqr optim adaptive lasso quantile regression with fixed effects optim_lqr optim lasso quantile regression with fixed effects optim_qr optim quantile regression optim_qrfe optim quantile regression with fixed effects plot_alpha plot multiple penalized quantile regression - alpha plot_taus plot multiple penalized quantile regression print.ALQRFE Print an ALQRFE q_cov Covariance qr quantile regression rho_koenker Rho Koenker sgf Identify significance

### Maintainer

NA

### Author(s)

NA

[Package

*alqrfe*version 1.1 Index]