We need to define what it means for these models to be the same or different. With the usual lm assumptions suppose for i=1, 2 (the two models) that:
y1 = a1 + X b1 + error1 y2 = a2 + X b2 + error2 which implies the following which also satisfies the usual lm assumptions: y1-y2 = (a1-a2) + X(b1-b2) + error Here X is a matrix, a1 and a2 are scalars and all other elements are vectors. We say the models are the "same" if b1=b2 (but allow the intercepts to differ even if the models are the "same"). If y1 and y2 are as in the built in anscombe data frame and x3 and x4 are the x variables, i.e. columns of X, then: > fm1 <- lm(y1 - y2 ~ x3 + x4, anscombe) > # this model reduces to the following if b1 = b2 > fm0 <- lm(y1 - y2 ~ 1, anscombe) > anova(fm0, fm1) Analysis of Variance Table Model 1: y1 - y2 ~ 1 Model 2: y1 - y2 ~ x3 + x4 Res.Df RSS Df Sum of Sq F Pr(>F) 1 10 20.637 2 8 18.662 2 1.9751 0.4233 0.6687 so we cannot reject the hypothesis that the models are the "same". On Wed, Jun 9, 2010 at 11:19 AM, Or Duek <[email protected]> wrote: > Hi, > I would like to compare to regression models - each model has a different > dependent variable. > The first model uses a number that represents the learning curve for reward. > The second model uses a number that represents the learning curve from > punishment stimuli. > The first model is significant and the second isn't. > I want to compare those two models and show that they are significantly > different. > How can I do that? > Thank you. > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] 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. > ______________________________________________ [email protected] 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.
