Thanks Duncan Murdoch,
Why do commonly used estimator functions (such as lm(),
glm(), etc.)
not allow negative case weights?
Residual sums of squares (or deviances) could be negative
with negative case weights. This doesn't seem like a good
thing: would you really want the fit to be far from those points?
Yes, this is actually what I want for this particular estimator. But I can
see now why this generally doesn't seem like a a good idea.
Best,
Jens
Nachricht
Von: Duncan Murdoch [mailto:murdoch (AT) stats (DOT) uwo.ca]
Gesendet: Friday, August 04, 2006 7:36 PM
An: Jens Hainmueller
Cc: r-help (AT) stat (DOT) math.ethz.ch
Betreff: Re: [R] why does lm() not allow for negative weights?
8/4/2006 1:26 PM, Jens Hainmueller wrote:
Dear List,
I suspect that there is a good reason for this.
Yet, I can see reasonable cases when one wants to use
negative case weights.
Take lm() for example:
n <- 20
Y <- rnorm(n)
X <- cbind(rep(1,n),runif(n),rnorm(n)) Weights <- rnorm(n)
# Includes
Pos and Neg Weights Weights
# Now do Weighted LS and get beta coeffs:
b <- solve(t(X)%*%diag(Weights)%*%X) %*% t(X) %*% diag(Weights)%*%Y
That formula does not necessarily give least squares
estimates in the case where weights might be negative. For
example, with a single observation y, a single parameter mu,
design matrix X = 1, and weight -1, that formula becomes
b <- y,
but that is the worst possible estimator in a least squares
sense. The residual sum of squares can be made arbitrarily
large and negative by setting b to a large value.
Duncan Murdoch
b
# This seems like a valid model, but when I try lm(Y ~
X[,2:3],weights=Weights)
# I get: "missing or negative weights not allowed"
What is the rationale for not allowing negative weights? I
ask this,
because I am currently trying to implement a (two stage) estimator
into R that involves negative case weights. Weights are
generated in
the first stage, so it would be nice if I could use canned
functions
such as
lm(,weights=Weights) in the second stage.
Thank you for your help.
Best,
Jens
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