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R: This function fits a GLD regression linear model and conducts...

GLD.lm.full

R Documentation

This function fits a GLD regression linear model and conducts simulations to display the statistical properties of estimated coefficients

Description

The function is an extension of GLD.lm and defaults to 1000 simulation runs, coefficients and statistical properties of coefficients can be plotted as part of the output.

Usage

GLD.lm.full(formula, data, param, maxit = 20000, fun, method = "Nelder-Mead", 
range = c(0.01, 0.99), n.simu = 1000, summary.plot = TRUE, init = NULL)

Arguments

`formula`	A symbolic expression of the model to be fitted, similar to the formula argument in `lm`, see `formula` for more information
`data`	Dataset containing variables of the model
`param`	Can be "rs", "fmkl" or "fkml"
`maxit`	Maximum number of iterations for numerical optimisation
`fun`	If param="fmkl" or "fkml", this can be one of `fun.RMFMKL.ml.m`, `fun.RMFMKL.ml`, for maximum likelihood estimation (.ml.m is a faster implementation of .ml) and `fun.RMFMKL.lm` for L moment matching. If param="rs", this can be one of `fun.RPRS.ml.m`, `fun.RPRS.ml`, for maximum likelihood estimation (.ml.m is a faster implementation of .ml) and `fun.RPRS.lm` for L moment matching.
`method`	Defaults to "Nelder-Mead" algorithm, can also be "SANN" but this is a lot slower and may not as good
`range`	The is the quantile range to plot the QQ plot, defaults to 0.01 and 0.99 to avoid potential problems with extreme values of GLD which might be -Inf or Inf.
`n.simu`	Number of times to repeat the simulation runs, defaults to 1000.
`summary.plot`	Whether to plot the coefficients graphically, defaults to TRUE.
`init`	Choose a different set of initial values to start the optimisation process. This can either be full set of parameters including GLD parameter estimates, or it can just be the coefficient estimates of the regression model.

Details

This function usually takes some time to run, as it involves refitting the GLD regression model many times, the progress of the simulation is outputted to the R screen, so users can guage the progress of the computation.

Value

`[[1]]`	Output of `GLD.lm`
`[[2]]`	A matrix showing the bias adjustment, coefficents of the model, parameters of GLD and whether the result converged at each run
`[[3]]`	Adjusted simulation result so that the empirical mean of coefficients is the same as the estimated parameters obtained in `GLD.lm`

Author(s)

Steve Su

References

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

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)

## Not run: 
## Extract the Engel dataset 

library(quantreg)
data(engel)

## Fit a full GLD regression

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

## Extract the mammals dataset 
library(MASS)

## Fit a full GLD regression

mammals.fit.full<-GLD.lm.full(log(brain)~log(body),data=mammals,param="fmkl",
fun=fun.RMFMKL.ml.m)

## Using quantile regression coefficients as starting values
library(quantreg)

mammals.fit1.full<-GLD.lm.full(log(brain)~log(body),data=mammals,param="fmkl",
fun=fun.RMFMKL.ml.m, init=rq(log(brain)~log(body),data=mammals)$coeff)

## Using the result of mammals.fit.full as initial values

mammals.fit2.full<-GLD.lm.full(log(brain)~log(body),data=mammals,param="fmkl",
fun=fun.RMFMKL.ml.m, init=mammals.fit1.full[[1]][[3]])

## 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.lm.full.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GLD.lm.full
> ### Title: This function fits a GLD regression linear model and conducts
> ###   simulations to display the statistical properties of estimated
> ###   coefficients
> ### Aliases: GLD.lm.full
> ### 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="Rplots967.pdf")
> 
> ## Not run: 
> ##D ## Extract the Engel dataset 
> ##D 
> ##D library(quantreg)
> ##D data(engel)
> ##D 
> ##D ## Fit a full GLD regression
> ##D 
> ##D engel.fit.full<-GLD.lm.full(foodexp~income,data=engel,param="fmkl",
> ##D fun=fun.RMFMKL.ml.m)
> ##D 
> ##D ## Extract the mammals dataset 
> ##D library(MASS)
> ##D 
> ##D ## Fit a full GLD regression
> ##D 
> ##D mammals.fit.full<-GLD.lm.full(log(brain)~log(body),data=mammals,param="fmkl",
> ##D fun=fun.RMFMKL.ml.m)
> ##D 
> ##D ## Using quantile regression coefficients as starting values
> ##D library(quantreg)
> ##D 
> ##D mammals.fit1.full<-GLD.lm.full(log(brain)~log(body),data=mammals,param="fmkl",
> ##D fun=fun.RMFMKL.ml.m, init=rq(log(brain)~log(body),data=mammals)$coeff)
> ##D 
> ##D ## Using the result of mammals.fit.full as initial values
> ##D 
> ##D mammals.fit2.full<-GLD.lm.full(log(brain)~log(body),data=mammals,param="fmkl",
> ##D fun=fun.RMFMKL.ml.m, init=mammals.fit1.full[[1]][[3]])
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
png 
  2 
>