Dear All,
 
I would like to be able to estimate confidence intervals for a linear
combination of coefficients for a GLS model.  I am familiar with John
Foxton's helpful paper on Time Series Regression and Generalised Least
Squares (GLS) and have learnt a bit about the gls function.
 
I have downloaded the gmodels package so I can use the estimable
function.  The estimable function is very useful because it allows me to
calculate confidence intervals for a linear combination of coefficients,
but only for OLS models.  For example, the code below calculates the
confidence interval for the sum of the coefficient of petrol_A and the
coefficient of petrol_B:
 
> results <- lm(all_rural_count_capita ~ petrol_A + petrol_B +
gdp_capita)
> estimable(results,cm=c(0,1,1,0),conf.int=0.95)

However, the estimable function does not seem to work for GLS objects,
as shown below.  The estimable documentation confirm that the object
must be one of the following: lm, glm, lme, lmer.

> results.gls <- gls(all_rural_count_capita ~ petrol_A + petrol_B +
gdp_capita, correlation=corARMA(p=1),method='ML')
> estimable(results.gls,cm=c(0,1,1,0),conf.int=0.95)
Error in estimable(results.gls, cm = c(0, 1, 1, 0), conf.int = 0.95) : 
        obj must be of class 'lm', 'glm', 'aov', 'lme', 'lmer', 'gee',
'geese' or 'nlme'


Therefore, I am looking for a solution to this problem.  I think that
the solution (if it exists) may be down one of the following paths:
 
1) An alternative command which allows me to generate joint confidence
intervals for the objects generated by the gls function.  p.s. I note
that the intervals function only appears to produce confidence intervals
for each coeffcient (not for a linear combination of coeffcients).
 
2) An alternative means of generating GLS estimates as lm, glm, lme or
lmer objects, so they can be inputed into the estimable function.
 
Regards,
David

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