Graphical display of output from GLD.lm.full

Description

This function display the coefficients and the distribution of coefficients obtained from GLD regression model. For a discussion on goodness of fit, please see the description under GLD.lm.

Usage

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summaryGraphics.gld.lm(overall.fit.obj, alpha = 0.05, label = NULL, 
ColourVersion = TRUE, diagnostics = TRUE, range = c(0.01, 0.99))

Arguments

overall.fit.obj

An object from GLD.lm.full

alpha

Specifying the range of interval for the coefficients, default is 0.05, which specifies a 95% interval. This also specifies the significance level of KS test.

label

A character vector indicating the labelling for the coefficients

ColourVersion

Whether to display colour or not, default is TRUE, if set as FALSE, a black and white plot is given. This is only applicable to the coefficient summary graph and has no effect on QQ plots.

diagnostics

If TRUE, then QQ plot will be given along with Kolmogorov-Smirnoff test results

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.

Details

The reason QQ plots are not displayed in black and white even if ColourVersion is set to FALSE is because the colour is necessary in those plots for clarity of display.

Value

Graphics displaying coefficient summary and diagnostic plot (if chosen)

Author(s)

Steve Su

References

Su (2015) "Flexible Parametric Quantile Regression Model" Statistics & Computing May 2015, Volume 25, Issue 3, pp 635-650

See Also

GLD.lm.full

Examples

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## 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)

## Note this is for illustration only, need to set number
## of simulations around 1000 usually for the graphics below 
## to be meaningful

summaryGraphics.gld.lm(fit,ColourVersion=FALSE,diagnostic=FALSE)

## 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)

## Plot coefficient summary

summaryGraphics.gld.lm(engel.fit.full,ColourVersion=FALSE,diagnostic=FALSE)

summaryGraphics.gld.lm(engel.fit.full)

## 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)

## Plot coefficient summary

summaryGraphics.gld.lm(mammals.fit.full,label=c("intercept","log of body weight"))


## End(Not run)