problem with zero-weighted observations in predict.lm?
I believe that I now has this nailed down (a couple of further issues raised their head). Committed to r-devel. -pd
On Jul 29, 2010, at 10:11 AM, Peter Dalgaard wrote:
Peter Dalgaard wrote:
Prof Brian Ripley wrote:
I think you will find that 'n' is used in several ways in predict.lm, and since NA-handling was introduced in R 1.8.0 they may differ in value. So the safest route seems to be to change just 'n' in df <- n - p
Yes, that seems to fix things. Will commit to R-devel shortly. -p
Spoke too soon, it fixes Bill's case, but breaks one of the regression tests! In fact this goes deeper, summary.lm special-cases the same zero-rank case by using length(residuals), so it also miscalculates with zero weights:
fit <- lm(y~0,weights=c(0,rep(1,9))) summary(fit)
Call:
lm(formula = y ~ 0, weights = c(0, rep(1, 9)))
Residuals:
Min 1Q Median 3Q Max
-1.95428 -1.40571 -0.42378 -0.05795 1.05518
No Coefficients
Residual standard error: 1.119 on 10 degrees of freedom
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Hum. lm() actually returns df.residual, AFAICS in all cases, now why
don't we just use that throughout????
--
Peter Dalgaard
Center for Statistics, Copenhagen Business School
Phone: (+45)38153501
Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com
Peter Dalgaard Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com