pqrfe-package {pqrfe} | R Documentation |
Penalized Quantile Regression with Fixed Effects
Description
Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO.
Package Content
Index of help topics:
check_lambda check lambda choice_p choice model clean_data Clean missings d_psi_als D Psi ALS d_psi_mq D Psi M-quantile f_den Kernel density f_tab Tabular function loss_er Loss expectile regression loss_erfe Loss expectile regression with fixed effects loss_erlasso Loss lasso expectile regression with fixed effects loss_mqr Loss M-quantile regression loss_mqrfe Loss M-quantile regression with fixed effects loss_mqrlasso Loss lasso M-quantile regression with fixed effects loss_qr Loss quantile regression loss_qrfe Loss quantile regression with fixed effects loss_qrlasso Loss lasso quantile regression with fixed effects mpqr Multiple penalized quantile regression optim_er optim expectile regression optim_erfe optim expectile regression with fixed effects optim_erlasso optim expectile regression with fixed effects and LASSO optim_mqr optim M-quantile regression optim_mqrfe optim quantile regression with fixed effects optim_mqrlasso optim M-quantile regression with fixed effects and LASSO optim_qr optim quantile regression optim_qrfe optim quantile regression with fixed effects optim_qrlasso optim quantile regression with fixed effects and LASSO plot_taus Plot multiple penalized quantile regression pqr Penalized quantile regression with fixed effects pqrfe-package Penalized Quantile Regression with Fixed Effects print.PQR Print an PQR psi_als Psi ALS psi_mq Psi M-quantile q_cov Covariance rho_koenker Rho Koenker rho_mq Rho M-quantile sgf Identify significance
Maintainer
NA
Author(s)
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[Package pqrfe version 1.1 Index]