Fit a GLD quantile regression parametrically or non parametrically

Description

The GLD quantile regression can be: 1) Fixed intercept, allowing all other coefficients to vary, 2) Only intercept is allowed to vary and 3) All coefficients can vary. Minimisation is achieved numerically through least squares between the proportion of estimated GLD error distribution below zero versus the specified quantile for parametric approach. For non parametric approach, minimisation is achieved using a least squares approach to find a q-th quantile GLD line such that the percentage of observations below the line corresponds to the q-th quantile.

Usage

GLD.quantreg(q, fit.obj, intercept = "", slope = "", emp=FALSE)

Arguments

Details

This is a wrapper function for fun.gld.all.vary, fun.gld.slope.fixed.int.vary, fun.gld.slope.vary.int.fixed.

Value

A matrix showing the estimated coefficients for the specified quantile regression model, the objective function value and whether convergence is reached in the optimisation process. A value of 0 indicates convergence is reached. The convergence value is the same as the one from the optim function.

Author(s)

Steve Su

References

Su (2014) "Flexible Parametric Quantile Regression Model" Statistics & Computing

See Also

GLD.lm.full,fun.plot.q, summaryGraphics.gld.lm

Examples

## Dummy example

## Create dataset

set.seed(10)

x<-rnorm(200,3,2)
y<-3*x+rnorm(200)

dat<-data.frame(y,x)

## Fit FKML GLD regression with 3 simulations

fit<-GLD.lm.full(y~x,data=dat,fun=fun.RMFMKL.ml.m,param="fkml",n.simu=3)

## Find median regression, use empirical method

med.fit<-GLD.quantreg(0.5,fit,slope="fixed",emp=TRUE)

## Not run: 

## Extract the Engel dataset 

library(quantreg)
data(engel)

## Fit GLD Regression along with simulations

engel.fit.all<-GLD.lm.full(foodexp~income,data=engel,
param="fmkl",fun=fun.RMFMKL.ml.m)

## Fit parametric GLD quantile regression from 0.1 to 0.9, with equal spacings 
## between quantiles

result<-GLD.quantreg(seq(0.1,.9,length=9),engel.fit.all,intercept="fixed")

## Non parametric quantile regression

GLD.quantreg(seq(0.1,.9,length=9),engel.fit.all,intercept="fixed",emp=T)


## End(Not run)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GLDreg)
Loading required package: GLDEX
Loading required package: cluster
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GLDreg/GLD.quantreg.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GLD.quantreg
> ### Title: Fit a GLD quantile regression parametrically or non
> ###   parametrically
> ### Aliases: GLD.quantreg
> ### Keywords: model
> 
> ### ** Examples
> 
> 
> ## Dummy example
> 
> ## Create dataset
> 
> set.seed(10)
> 
> x<-rnorm(200,3,2)
> y<-3*x+rnorm(200)
> 
> dat<-data.frame(y,x)
> 
> ## Fit FKML GLD regression with 3 simulations
> 
> fit<-GLD.lm.full(y~x,data=dat,fun=fun.RMFMKL.ml.m,param="fkml",n.simu=3)
     [,1]                                                                      
[1,] "This analysis was carried out using FKML GLD"                            
[2,] "The error distribution was estimated using Maximum Likelihood Estimation"
[3,] "The optimisation procedure used was method and it has converged"         
(Intercept)           x          L1          L2          L3          L4 
 0.05895140  3.01981005 -0.01457362  1.29930852  0.22981644  0.20182903 
[1] 1
[1] 2
[1] 3
dev.new(): using pdf(file="Rplots965.pdf")
> 
> ## Find median regression, use empirical method
> 
> med.fit<-GLD.quantreg(0.5,fit,slope="fixed",emp=TRUE)
[1] 0.5
                       0.5
(Intercept)     0.02894985
x               3.01981005
Objective Value 0.00000000
Convergence     0.00000000
> 
> ## Not run: 
> ##D 
> ##D ## Extract the Engel dataset 
> ##D 
> ##D library(quantreg)
> ##D data(engel)
> ##D 
> ##D ## Fit GLD Regression along with simulations
> ##D 
> ##D engel.fit.all<-GLD.lm.full(foodexp~income,data=engel,
> ##D param="fmkl",fun=fun.RMFMKL.ml.m)
> ##D 
> ##D ## Fit parametric GLD quantile regression from 0.1 to 0.9, with equal spacings 
> ##D ## between quantiles
> ##D 
> ##D result<-GLD.quantreg(seq(0.1,.9,length=9),engel.fit.all,intercept="fixed")
> ##D 
> ##D ## Non parametric quantile regression
> ##D 
> ##D GLD.quantreg(seq(0.1,.9,length=9),engel.fit.all,intercept="fixed",emp=T)
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
png 
  2 
>