| 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)
NA
[Package pqrfe version 1.1 Index]