AFAICS you are not testing a linear hypothesis (which is of the form Lb=b0 where L is a matrix and b=(a,B1,B2,B3,B3) is the parameter vector).
If, for simplicity, your model is E(y) = a + bx then -a/b is the x-value for which y is zero. When you turn to estimates then u = -a/b is the ratio of two (typically correlated) normal variables and such a ratio is *not* normal. (Just think of the Cauchy distribution.) One approach is to calculate the approximate variance of u and then construct a Wald test or similar while hoping for the best. Alternatively one could perhaps try with a parametric bootstrap test. Just ideas. Good luck. Søren -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Chris Sent: 6. september 2014 04:17 To: r-h...@stat.math.ethz.ch Subject: [R] Testing general hypotheses on regression coefficients Hi. Say I have a model like y = a + B1*x1 + B2*x2 + B3*x3 + B4*x4 + e and I want to test H0: B2/B1 = 0 or H0: B2/B1=B4/B3 (whatever H1). How can I proceed? I now about car::linearHypothesis, but I can't figure out a way to do the tests above. Any hint? Thanks. C ______________________________________________ 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. ______________________________________________ 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.
