# [R] ms?

Douglas Bates bates at stat.wisc.edu
Mon Feb 28 17:39:04 CET 2000

```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)
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```