> To: [EMAIL PROTECTED]
> From: [EMAIL PROTECTED]
> Date: Wed, 13 Feb 2008 11:38:43 +1100
> Subject: AIC Quantile Regression
>
> Dear R,
> I currently trying to fit quantile regression models to test the
> relationship between abalone and algal cover at different quadrat sizes.
> I am using the AIC values as a measure of model fit. Each model has the
> same number of parameters and data points but uses different data sets,
> for example
>
> fit1<-rg(log(Hrubra+0.001)~%coverSessileInvertebrates, tau=0.9,
> data=Quadrat0.25 x 0.25m)
> fit1<-rg(log(Hrubra+0.001)~%coverSessileInvertebrates, tau=0.9,
> data=Quadrat0.5 x0.5m)
> etc
> An example of the data is below. After look through the help files I
> still have a couple of questions. My questions are
>
> 1. Is this an appropriate use of AIC given that I am testing different
> data sets?
> 2. Why does is the AIC going down in value although the fit of the models
> appear to be getting worse as the size of the model increases
> 3. How are the AIC values calculated in quantile regression.
>
> Thanks in advance
> Beth Strain
>
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