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 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
