[R] find all unit vectors which are orthogonal to a given vector

Is there a function in R to get the set of all unit vectors which are orthogonal to a given vector? __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http:

Re: [R] find all unit vectors which are orthogonal to a given vector

Ok, thanks! On 2015-10-29 09:03 PM, Duncan Murdoch wrote: On 29/10/2015 6:38 PM, Marco Inacio wrote: Is there a function in R to get the set of all unit vectors which are orthogonal to a given vector? No. Duncan Murdoch __ R-help@r-project.org

Re: [R] find all unit vectors which are orthogonal to a given vector

I think that's just what I needed. Thanks! On 2015-10-29 09:55 PM, peter dalgaard wrote: On 30 Oct 2015, at 00:03 , Duncan Murdoch wrote: On 29/10/2015 6:38 PM, Marco Inacio wrote: Is there a function in R to get the set of all unit vectors which are orthogonal to a given vector

[R] proportional weights

Hello all, can help clarify something? According to R's lm() doc: Non-NULL weights can be used to indicate that different observations have different variances (with the values in weights being inversely *proportional* to the variances); or equivalently, when the elements of weights are positiv

Re: [R] proportional weights

Thanks for the answers. Dear Marco and Goran, Perhaps the documentation could be clearer, but it is after all a brief help page. Using weights of 2 to lm() is *not* equivalent to entering the observation twice. The weights are variance weights, not case weights. According to your post here:

Re: [R] proportional weights

I think we can blame Tim Hesterberg for the confusion: He writes " I'll add: * inverse-variance weights, where var(y for observation) = 1/weight (as opposed to just being inversely proportional to the weight) * " And, although I'm not a native English speaker, I think there's a spurious c

Re: [R] proportional weights

No, you are perfectly fine using WLS. The constant of proportionality is the estimated error variance, i.e., the square of the residual standard error (as I think I said earlier). John You're right. That was a little hard for me to grasp. Thanks for the patience.

[R] Exposures in bernoulli glm()

Hi, is it possible to add exposures to a glm with family=binomial()? It's easy to do it for a Poisson/negative binomial: just multiply the mean by the exposure, that is, offset(log(exposure)): but this obviously wrong for a binomial/Bernoulli since the mean must be no bigger than 1. My goal w