vals for lme models. It is not easy to write such a function for the
general case, but it may be relatively easy to write your own for special
cases of lme models.
Jim
> Message: 9
> Date: Mon, 4 Oct 2004 21:42:20 +1000
> From: Andrew Robinson <[EMAIL PROTECTED]>
> Subject:
The function estimable() from the gmodels part of the gregmisc package
will do this, if applied appropriately.
It allows for the estimation of arbitrary linear combinations of the
parameter estimates of a model object, and calcualtes confidence
intervals.
I hope that this helps,
Andrew
On Tue
On Mon, 4 Oct 2004, Pierre MONTPIED wrote:
Thanks Spencer but the intervals function gives confidence intervals of the
parameters of the model not the predicted values. In the Soybean example it
would be the CI of predicted weight for a given time, knowing all the
parameters (Asym, xmid, scal, v
Thanks Spencer but the intervals function gives confidence intervals of
the parameters of the model not the predicted values. In the Soybean
example it would be the CI of predicted weight for a given time, knowing
all the parameters (Asym, xmid, scal, variance function and residual)
and their d
Pinhiero and Bates (2000) Mixed-Effects Models in S and S-Plus
(Springer) describe the use of "intervals" for that. In library(nlme),
R 1.9.1 for Windows, I found documentation for intervals, intervals.gls,
intervals.lme, intervals.lmList, and gnls. To an example in the
documentation for
Hi
I use gnls to fit non linear models of the form y = alpha * x**beta
(alpha and beta being linear functions of a 2nd regressor z i.e.
alpha=a1+a2*z and beta=b1+b2*z) with variance function
varPower(fitted(.)) which sounds correct for the data set I use.
My purpose is to use the fitted models
