quokar v0.1.0

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Quantile Regression Outlier Diagnostics with K Left Out Analysis

Diagnostics methods for quantile regression models for detecting influential observations: robust distance methods for general quantile regression models; generalized Cook's distance and Q-function distance method for quantile regression models using aymmetric Laplace distribution. Reference of this method can be found in Luis E. Benites, V<c3><ad>ctor H. Lachos, Filidor E. Vilca (2015) <arXiv:1509.05099v1>; mean posterior probability and Kullback<e2><80><93>Leibler divergence methods for Bayes quantile regression model. Reference of this method is Bruno Santos, Heleno Bolfarine (2016) <arXiv:1601.07344v1>.

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quokar

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R package for diagnosing quantile regression models

  • quokar can be easily installed on Windows and Linux using command:
    devtools::install_github("wenjingwang/quokar")
    
  • To install quokar on Mac, you need to install a fortran compiler. You can install gfortran by command,
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

Functions in quokar

Name Description
ALDqr_GCD Generalized Cook's distance for each observation in quantile regression model
ALDqr_QD Q-function distance for each observation in quantile regression model
frame_bayes Mean probability of posterior distribution and Kullback-Leibler divergence for observations in Bayesian quantile regression model
frame_br Visualization of quantile regression model fitting: br algorithem
trout Fish habbit of trout
ais Australia Institute of Sport data
baseball Baseball Hitter Data
frame_distance Residual-robust distance plot of quantile regression model
frame_distance_complex Residual-robust distance plot of quantile regression model
frame_ald Density function plot of the error term for quantile regression model using asymmetric Laplace distribution
frame_ald_weight Weighting Matrix of Quantile regression using Asymmetric Laplace Distrubtion
frame_fn_obs Visualization of quantile regression model fitting: interior point algorithm
bayesKL Kullback-Leibler divergence for each observation in Baysian quantile regression model
bayesProb Mean posterior probability for each observation in Baysian quantile regression model
frame_fn_path Visualization of the fitting path of quantile regression: interior point method
qrod_bayes Outlier Dignostic for Quantile Regression Based on Bayesian Estimation
qrod_mle Outlier Dignostic for Quantile Regression with Asymmetric Laplace Distribution
ALDqr_case_deletion Calculate the case-deletion coefficience of the MLE estimation of quantile regression
ah House Price
frame_mle General Cook's distance or Q-function distance of quantile regression
frame_nlrq Visualization of fitting process of non-linear quantile regression: interior point algorithm
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Vignettes of quokar

Name
quokar.Rmd
report_code.R
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Details

License GPL (>= 2)
Encoding UTF-8
Type Package
NeedsCompilation yes
LazyLoad false
VignetteBuilder knitr
RoxygenNote 6.0.1
URL https://github.com/wenjingwang/quokar
BugReports https://github.com/wenjingwang/quokar/issues
LazyData true
Packaged 2017-11-10 04:38:30 UTC; Thinkpad
Repository CRAN
Date/Publication 2017-11-10 10:21:36 UTC

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