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`

.

1 2 |

`overall.fit.obj` |
An object from |

`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. |

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.

Graphics displaying coefficient summary and diagnostic plot (if chosen)

Steve Su

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

`GLD.lm.full`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ```
## 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)
``` |

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