# fun.plot.q: 2-D Plot for Quantile Regression lines In GLDreg: Fit GLD Regression Model and GLD Quantile Regression Model to Empirical Data

## Description

This function plots quantile regression lines from `GLD.lm` and one of `fun.gld.slope.vary.int.fixed`, `fun.gld.slope.fixed.int.vary`, `fun.gld.slope.fixed.int.vary.emp`, `fun.gld.all.vary.emp`, `fun.gld.all.vary`, `fun.gld.slope.vary.int.fixed.emp`, `GLD.quantreg`.

## Usage

 `1` ```fun.plot.q(x, y, fit, quant.info, ...) ```

## Arguments

 `x` A numerical vector of explanatory variable `y` A numerical vector of response variable `fit` An object from `GLD.lm` `quant.info` An object from one of `fun.gld.slope.vary.int.fixed`, `fun.gld.slope.fixed.int.vary`, `fun.gld.slope.fixed.int.vary.emp`, `fun.gld.all.vary.emp`, `fun.gld.all.vary`, `fun.gld.slope.vary.int.fixed.emp`, `GLD.quantreg` `...` Additional arguments to be passed to plot function, such as axis labels and title of the graph

## Details

This is intended to plot only two variables, for quantile regression involving more than one explanatory variable, consider plotting the actual values versus fitted values by fitting a secondary GLD quantile model between actual and fitted values.

## Value

A graph showing quantile regression lines

Steve Su

## References

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

## 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) ## Find median regression, use empirical method med.fit<-GLD.quantreg(0.5,fit,slope="fixed",emp=TRUE) fun.plot.q(x=x,y=y,fit=fit[[1]],med.fit, xlab="x",ylab="y") ## Not run: ## Plot result of quantile regression ## Extract the Engel dataset library(quantreg) data(engel) ## Fit GLD Regression along with simulations engel.fit.all<-GLD.lm.full(foodexp~income,data=engel, param="fmkl",fun=fun.RMFMKL.ml.m) ## Fit quantile regression from 0.1 to 0.9, with equal spacings between ## quantiles result<-GLD.quantreg(seq(0.1,.9,length=9),engel.fit.all,intercept="fixed") ## Plot the quantile regression lines fun.plot.q(x=engel\$income,y=engel\$foodexp,fit=engel.fit.all[[1]],result, xlab="income",ylab="Food Expense") ## End(Not run) ```

GLDreg documentation built on May 30, 2017, 3:30 a.m.