One r2 for each beta column/predictor
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The 'stats' output from regress returns a 1x4 vector, first value of which is r2. If you do regress(Y,X) where X is not one column vector, but a matrix of predictors (columns), then you would get as many beta columns as predictors, am I right?
Would you also get as many r2 as beta columns (or predictors)? Because I am only getting one r2 for X and Y, even though X is not one predictor, but many. Is this correct? Or am I indexing wrongly the stats output and missing data?
Thank you all
Greg Heath on 12 Jul 2012
With n points and p predictors you get p+1 betas (b0,b1,...bp) and a R^2 quantifying prformance
For any subset of predictors the corresponding R^2 will be less.
Although there is no universally accepted way to divide R^2 p+1 ways and attribute each part to a single predictor, I am satisfied to use the function stepwisefit in the backward mode to obtain such a result.
Hope this helps.