Confidence intervals for gls models?

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What about the obvious:

tstDf <- data.frame(x=1:9, y=rnorm(9), w=1:9)
fit <- lm(y~x, tstDf, weights=w)
pred <- predict(fit, se.fit=T)
pred$fit + outer(pred$se.fit, c(-2, 2))

"predict.lm" might need weights for interval="prediction" with newdata, 
but not with interval="confidence" ... or am I missing something?

hth.  spencer graves
Is there an easy way to compute confidence intervals (or prediction
intervals) for gls models?

E.g. for standard linear models, with the predict.lm function, we can set
interval="confidence" , level = 0.95 and type="response".

Thanks in advance!

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What about the obvious:

tstDf <- data.frame(x=1:9, y=rnorm(9), w=1:9)
fit <- lm(y~x, tstDf, weights=w)
pred <- predict(fit, se.fit=T)
pred$fit + outer(pred$se.fit, c(-2, 2))

"predict.lm" might need weights for interval="prediction" with newdata, 
but not with interval="confidence" ... or am I missing something?
That's weighted least squares, not generalized least squares.

predict.gls does not have an `se.fit' argument.

Howver, lm.gls in package MASS will do the trick at the existing data 
points.  (To predict at newdata you would need to have a model for the 
covariance matrix, and once you have that you are doing time series or 
kriging or ... and there are many other possibilities.)
hth.  spencer graves

Brown, David wrote:
Is there an easy way to compute confidence intervals (or prediction
intervals) for gls models?

E.g. for standard linear models, with the predict.lm function, we can set
interval="confidence" , level = 0.95 and type="response".

Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595