R help - Jun 2011 - a Weighted Least Square Model for a Binary Outcome

Dear R Users,

I am new to the mailing list. I posted this message about two hours
ago but did not receive it through the list, so I am posting it again.
Sorry for duplicates.

I would like to use R to fit a Weighted Least Square model for a
binary outcome, say Y.  The model is the one widely used for a binary
dependent variable when the logistic model has not been proposed.

Does anyone know how to specify the weight as the square root of
1/(E(Y)(1-E(Y)) in lm() or any other regression functions?

I know that varPower() in the package of gls() can provide an optimal
alpha estimator for a weight with the form of $E(Y)^{-2\alpha}$, which
does not include the weight form I need. Please correct me if I am
wrong.

Thanks for any replies in advance!

Best Regards,
Vivian

You can specify the weights=... argument in the lm() function as vector of
weights, one for each observation. Should that not do what your are trying
to do?

HTH,
Daniel

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