[R] Root mean square on binned GAM results
dwinsemius at comcast.net
Sat Jun 19 03:31:54 CEST 2010
On Jun 18, 2010, at 7:54 PM, David Jarvis wrote:
> Standard correlations (Pearson's, Spearman's, Kendall's Tau) do not
> accurately reflect how closely the model (GAM) fits the data. I was
> 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
> oangry <- c( mean(o[1:5]), mean(o[6:10]), ... )
> mangry <- c( mean(m[1:5]), mean(m[6:10]), ... )
> 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)
> 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
> length vector using an appropriate number of bins (fixed at 5, or
> calculated using Sturges' formula)?
> I have also posted a more detailed version of this question on
> Many thanks.
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