# [R] ms and congratulations!

gb@stat.umu.se gb at stat.umu.se
Tue Feb 29 17:12:22 CET 2000

```On 28 Feb 2000, Douglas Bates wrote:

> Douglas Bates <bates at stat.wisc.edu> writes:
>
> > Assuming that the quadratic form evaluates to a scalar, try
> >
> > opt.func <- function(alf, beta)
> >   t(Y-(X[,1] * alf + X[,2] * bet)^delta) %*% covariance.matrix.inverse %*%
> >             (Y-(X[,1] * alf + X[,2] * bet)^delta)
> >
> > nlm(opt.func, c(alf = 5, bet = 0.5))
> >
> > or
> >
> > optim(c(alf = 5, bet = 0.5), opt.func)
>
> Those are wrong.  The function being optimized has to be a function of
> a single argument.  If alf and bet are both scalars you can combine
> them into a vector and use
>
> opt.func <- function(arg)
>   t(Y-(X[,1] * arg[1] + X[,2] * arg[2])^delta) %*% covariance.matrix.inverse %*%
>             (Y-(X[,1] * arg[1] + X[,2] * arg[2])^delta)

Doug, thank you for the help. Of course it works perfectly!
And of course my thanks go to the whole  R  team on a day
like today!

Göran
----------------------------------------------------------
Göran Broström                      tel: +46 90 786-5223
Department of Statistics            fax: +46 90 786-6614
Umeå University
SE-90187 Umeå, Sweden              email: gb at stat.umu.se

http://www.stat.umu.se/egna/gb    ftp://capa.stat.umu.se
----------------------------------------------------------

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