Re: [R] Strange R squared, possible error

From: derek <jan.kacaba_at_gmail.com>
Date: Thu, 17 Mar 2011 02:08:32 -0700 (PDT)

Exuse me, I don't claim R^2 can't be negative. What I say if I get R^2 negative then the data are useless.
I know, that what Thomas said is true in general case. But in my special case of data, using nonzero intercept is nonsense, and to get R^2 less than 0.985 is considered poor job (standard R^2>0.995). (R^2 given by R^2 = 1 - Sum(R[i]^2) / Sum((y[i])^2) )

Because lm() uses two differrent formulas for computing R^2, it is confusing to get R^2 closer to 1 when linear model with zero intercept y=a*x (a = slope) is used, rather than in case with model y=a*x+b (a=slope, b= nonzero intercept).

I think R^2 is only measure of good fit for least squares optimization and it doesn't matter which formula is used: (R^2 = 1 - Sum(R[i]^2) / Sum((y[i])^2) or R^2 = 1 - Sum(R[i]^2) / Sum((y[i])^2-y*)), but using both is confusing.

So I would like to know why two different formulas for R^2 are used?

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Received on Thu 17 Mar 2011 - 14:05:39 GMT

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