[R] Root mean square on binned GAM results

[David Winsemius]

On Jun 18, 2010, at 7:54 PM, David Jarvis wrote:

> Hi,
>
> Standard correlations (Pearson's, Spearman's, Kendall's Tau) do not
> accurately reflect how closely the model (GAM) fits the data. I was  
> told
> that the accuracy of the correlation can be improved using a root mean
> square deviation (RMSD) calculation on binned data.

By whom? ...  and with what theoretical basis?

>
> For example, let 'o' be the real, observed data and 'm' be the model  
> data. I
> believe I can calculate the root mean squared deviation as:
>
> sqrt( mean( o - m ) ^ 2 )
>
> However, this does not bin the data into mean sets. What I would  
> like to do
> is:
>
> oangry <- c( mean(o[1:5]), mean(o[6:10]), ... )
> mangry <- c( mean(m[1:5]), mean(m[6:10]), ... )
>
> Then:
>
> sqrt( mean( oangry - mangry ) ^ 2 )
>
> That calculation I would like to simplify into (or similar to):
>
> sqrt( mean( bin( o, 5 ) - bin( m, 5 ) ) ^ 2 )

I doubt that your strategy offers any statistical advantage, but if  
you want to play around with it then consider:

binned.x <- round( (x + 2.5)/5)

-- 
David.
>
> I have read the help for ?cut, ?table, ?hist, and ?split, but am  
> stumped for
> which one to use in this case--if any.
>
> How do you calculate c( mean(o[1:5]), mean(o[6:10]), ... ) for an  
> arbitrary
> length vector using an appropriate number of bins (fixed at 5, or  
> perhaps
> calculated using Sturges' formula)?
>
> I have also posted a more detailed version of this question on
> StackOverflow:
>
> http://stackoverflow.com/questions/3073365/root-mean-square-deviation-on-binned-gam-results-using-r
>
> Many thanks.
>
> Dave
>
> 	[[alternative HTML version deleted]]
>
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