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fit_cox
2009 Nov 12
1
naive "collinear" weighted linear regression
...ercept and 2
for the slope. Furthermore, it seems completely plausible (or not?)
that, since the y_i have associated non-vanishing ``errors''
(dispersions), there should be corresponding non-vanishing ``errors''
associated to the best fit coefficients, right?
When I try:
> fit_mod <- lm(y~x,weights=1/error^2)
I get
Warning message:
In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
extra arguments weigths are just disregarded.
Keeping on, despite the warning message, which I did not quite
understand, when I type:
> summary(fit_mod)
I get
Call...