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]