On 15-Aug-07 21:16:32, Rolf Turner wrote:
>
> I have a data matrix X (n x k, say) each row of which constitutes
> an observation of a k-dimensional random variable which I am willing,
> if not happy, to assume to be Gaussian, with mean ``mu'' and
> covariance matrix ``Sigma''. Distinct rows of X
Thank you ***very*** much Ted and Chuck. I will install these two
packages and study
them over, and then in all probability pester you with more questions
when I find things
that I don't understand!
Thanks again.
cheers,
Rolf
On 16
Ted Harding wrote:
> Hi Rolf!
>
> Have a look at the 'norm' package.
>
> This does just what you;re asking for (assuming multivariate
> normal, and allowing CAR missingness -- i.e. probability of
> missing may depend on observed values, but must not depend on
> unobserved).
>
> Read the document
Hi Rolf!
Have a look at the 'norm' package.
This does just what you;re asking for (assuming multivariate
normal, and allowing CAR missingness -- i.e. probability of
missing may depend on observed values, but must not depend on
unobserved).
Read the documentation for the various function *very* c
I have a data matrix X (n x k, say) each row of which constitutes an
observation of
a k-dimensional random variable which I am willing, if not happy, to
assume to be
Gaussian, with mean ``mu'' and covariance matrix ``Sigma''. Distinct
rows of X may
be assumed to correspond to independent re
