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.

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 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

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