R: This function fits a GLD regression linear model
GLD.lm
R Documentation
This function fits a GLD regression linear model
Description
Similar to lm, this function fits a linear model using RS/FKML
GLDs and assess the goodness of fit of GLD with respect to the data via qq plot
and Kolmogorov-Smirnoff test.
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
diagnostics
Defaults to TRUE, which computes Kolmogorov-Smirnoff test and do QQ plot
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.
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.
Value
Message
Short description of estimation method used and whether the
result converged
Bias Correction
Bias correction used to ensure the line has zero mean
residuals
Estimated parameters
A set of estimate coefficients from GLD regression
Fitted
Predicted response value from model
Residual
Residual of model
formula
Formula used in the model
param
Specify whether RS/FKML/FMKL GLD was used
y
The response variable
x
The explanatory variable(s)
fun
GLD fitting function used in the computation process, outputted for
internal programming use
Author(s)
Steve Su
References
Su (2014) "Flexible Parametric Quantile Regression Model" Statistics &
Computing
See Also
GLD.lm.full, GLD.quantreg
Examples
## Dummy example
library(GLDEX)
## Create dataset
set.seed(10)
x<-rnorm(200,3,2)
y<-3*x+rnorm(200)
dat<-data.frame(y,x)
## Fit a FKML GLD regression
example<-GLD.lm(y~x,data=dat,fun=fun.RMFMKL.ml.m,param="fkml")
## Not run:
## Extract the Engel dataset
library(quantreg)
data(engel)
## Fit GLD Regression
engel.fit<-GLD.lm(foodexp~income,data=engel,param="fmkl",fun=fun.RMFMKL.ml.m)
## Extract the mammals dataset
library(MASS)
mammals.fit<-GLD.lm(log(brain)~log(body),data=mammals,param="rs",
fun=fun.RPRS.lm)
## Using quantile regression coefficients as starting values
library(quantreg)
mammals.fit1<-GLD.lm(log(brain)~log(body),data=mammals,param="rs",
fun=fun.RPRS.lm,init=rq(log(brain)~log(body),data=mammals)$coeff)
# As an exercise, use the result from mammals.fit1 as initial values
GLD.lm(log(brain)~log(body),data=mammals,param="rs",
fun=fun.RPRS.lm,init=mammals.fit1[[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.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GLD.lm
> ### Title: This function fits a GLD regression linear model
> ### Aliases: GLD.lm
> ### Keywords: model
>
> ### ** Examples
>
>
> ## Dummy example
>
> library(GLDEX)
>
> ## Create dataset
>
> set.seed(10)
>
> x<-rnorm(200,3,2)
> y<-3*x+rnorm(200)
>
> dat<-data.frame(y,x)
>
> ## Fit a FKML GLD regression
>
> example<-GLD.lm(y~x,data=dat,fun=fun.RMFMKL.ml.m,param="fkml")
[,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 Nelder-Mead and it has converged"
(Intercept) x L1 L2 L3 L4
0.05895140 3.01981005 -0.01457362 1.29930852 0.22981644 0.20182903
>
> ## Not run:
> ##D
> ##D ## Extract the Engel dataset
> ##D library(quantreg)
> ##D data(engel)
> ##D
> ##D ## Fit GLD Regression
> ##D engel.fit<-GLD.lm(foodexp~income,data=engel,param="fmkl",fun=fun.RMFMKL.ml.m)
> ##D
> ##D ## Extract the mammals dataset
> ##D library(MASS)
> ##D
> ##D mammals.fit<-GLD.lm(log(brain)~log(body),data=mammals,param="rs",
> ##D fun=fun.RPRS.lm)
> ##D
> ##D ## Using quantile regression coefficients as starting values
> ##D library(quantreg)
> ##D
> ##D mammals.fit1<-GLD.lm(log(brain)~log(body),data=mammals,param="rs",
> ##D fun=fun.RPRS.lm,init=rq(log(brain)~log(body),data=mammals)$coeff)
> ##D
> ##D # As an exercise, use the result from mammals.fit1 as initial values
> ##D
> ##D GLD.lm(log(brain)~log(body),data=mammals,param="rs",
> ##D fun=fun.RPRS.lm,init=mammals.fit1[[3]])
> ## End(Not run)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.