Thanks to all, Yes, I meant a single test for both coefficients. Peter's reply is what I wanted. I've tried with linear.hypothesis but I must confess that with my limited statistical experience and without the car book at hand, the nomenclature for the function was a bit difficult to understand for me. A toy example of linear.hypothesis for my case would be great. Thanks, Manuel --- Peter Dalgaard <[EMAIL PROTECTED]> escribió: > "John Fox" <[EMAIL PROTECTED]> writes: > > > Dear Vito, > > > > Since Manuel says that he wants to "obtain a test" > and not "obtain two > > tests," I assume that he's interested in the > F-test for the hypothesis that > > both coefficients are simultaneously equal to the > specified values rather > > than in the t-tests for the individual hypotheses. > > > > Regards, > > John > > ...in which case one answer is this: > > > y<-3+0.6*x1+0.3*x2 + rnorm(100,sd=.1) # as meant, > no? > > fm<-lm(y~x1+x2) > > anova(fm, lm(y~offset(0.6*x1+0.3*x2))) > Analysis of Variance Table > > Model 1: y ~ x1 + x2 > Model 2: y ~ offset(0.6 * x1 + 0.3 * x2) > Res.Df RSS Df Sum of Sq F Pr(>F) > 1 97 1.06118 > 2 99 1.06184 -2 -0.00066 0.0302 0.9703 > > > > -- > O__ ---- Peter Dalgaard Blegdamsvej > 3 > c/ /'_ --- Dept. of Biostatistics 2200 Cph. N > > (*) \(*) -- University of Copenhagen Denmark > Ph: (+45) 35327918 > ~~~~~~~~~~ - ([EMAIL PROTECTED]) > FAX: (+45) 35327907 >
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