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
1 2 
Arguments
formula 
A symbolic expression of the model to be fitted, similar to the formula
argument in 
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 If param="rs", this can be one of 
method 
Defaults to "NelderMead" 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 
[[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

Author(s)
Steve Su
References
Su (2015) "Flexible Parametric Quantile Regression Model" Statistics & Computing May 2015, Volume 25, Issue 3, pp 635650
See Also
GLD.lm
, GLD.quantreg
,
summaryGraphics.gld.lm
Examples
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  ## 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)
