[R] proportional weights

[Marco Inacio]

> I think we can blame Tim Hesterberg for the confusion:
>
> He writes
>
> "
> I'll add:
> * inverse-variance weights, where var(y for observation) = 1/weight   (as opposed to just being inversely proportional to the weight) *
> "
>
> And, although I'm not a native English speaker, I think there's a spurious comma in there. The intention was clearly to have this as a  4th type of weight which is a special case of inverse-variance weights, not as an elaboration on the definition of inv.var. weights.
>
> I.e., it is the difference between
>
> Motorists who are reckless drivers...
>
> and
>
> Motorists, who are reckless drivers...
>
> -pd
In fact, that wasn't what caused the confusion as I have understood what 
he meant despite the problem with the comma.

But I got the idea now, R uses weighted least squares and:

"Var[\epsilon | X] = \Omega" (equal, not proportion)
(https://en.wikipedia.org/wiki/Generalized_least_squares) (since WLS is 
just a special case of GLS)

"The weights should, ideally, be equal to the reciprocal of the variance 
of the measurement."
(https://en.wikipedia.org/wiki/Linear_least_squares_(mathematics)#Weighted_linear_least_squares)

I guess I need to find another strategy to use proportional weights 
(weights know up to a constant, as John says).

So, thank you much to you all, and sorry the inconvenience I caused.