Thanks Kingsford! I will cc r-help.
varPower() works for me. I want to use lm because predict.gls does not give
lwr and upr values, and also the bptest and ncv.test only work with lm
models...
On 4/29/08, Kingsford Jones [EMAIL PROTECTED] wrote:
hi Tom,
Basically, a simple way to model non-constant variance when the
variance increases (the common case) or decreases with the conditional
mean of your response is to use weights = varPower(), and gls will
estimate the parameter \alpha that defines the power relationship
between the regression line and the error variances. If for some
reason you wanted to use lm rather then gls for your final model, then
after estimating the gls model you could supply a weights argument to
lm of the form:
weights =
fitted(your.gls.model)^(-2*the.alpha.estimate.from.the.gls.model).
and you should get the same (or very close) estimates as from the gls
model but have a model of class 'lm'.
There are many different ways to model non-constant variance and
you'll need to choose one that is appropriate for your data. If the
model I described isn't appropriate then you should look at Ch 5 of
PB to learn about the other varFunc classes.
good luck,
Kingsford
ps - would you mind forwarding to r-help in case this others have the
same question.
On Tue, Apr 29, 2008 at 3:24 PM, tom soyer [EMAIL PROTECTED] wrote:
Thanks Kingsford! What are the varClasses? I don't know how to use
them
If I choose varPower(), then does it mean the function would generate
the
weights, w, so that w gives the most likely explanation of the
relationship
between the variance and the independent variable?
On 4/29/08, Kingsford Jones [EMAIL PROTECTED] wrote:
On Tue, Apr 29, 2008 at 6:20 AM, tom soyer [EMAIL PROTECTED]
wrote:
Hi,
I would like to use a weighted lm model to reduce
heteroscendasticity.
I am
wondering if the only way to generate the weights in R is through
the
laborious process of trial and error by hand. Does anyone know if R
has
a
function that would automatically generate the weights need for lm?
Hi Tom,
The 'weights' argument to the 'gls' function in the nlme package
provides a great deal of flexibility in estimate weighting parameters
and model coefficients. For example, if you want to model monotonic
heteroscedasticity by estimating the weights $E(Y)^{-2\alpha}$,
you can use the varPower variance function class. E.g., something
like
f1 - gls(y ~ x1 + x2, data = your.data, weights = varPower())
will estimate the regression coefficients and alpha parameter together
via maximum likelihood. (note that the usual specification for
varPower
is
varPower(form = ~ your.formula), but by default the mean is used. See
Ch 5 of the Pinheiro and Bates Mixed-effects Models book for details)
Kingsford Jones
--
Tom
--
Tom
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