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2010 Jul 27
1
problem with zero-weighted observations in predict.lm?
...the
same way, decrementing the number of degrees of
freedom for each dropped observation. However,
predict.lm() does not treat them the same. It
doesn't seem to diminish the df to account for the
0-weighted observations. E.g., the last printout
from the following script is as follows, where
predw is the prediction from the fit that used
0-weights and preds is from using FALSE's in the
subset argument. Is this difference proper?
predw preds
$fit $fit
fit lwr upr fit...
2009 Feb 16
1
Overdispersion with binomial distribution
...Median 3Q Max
-9.1022 -2.7899 -0.4781 2.6058 8.4852
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.35095 0.06612 20.433 < 2e-16 ***
PredF -0.34811 0.11719 -2.970 0.00297 **
PredSN -3.29156 0.10691 -30.788 < 2e-16 ***
PredW -1.46451 0.12787 -11.453 < 2e-16 ***
PredWF -0.56412 0.13178 -4.281 1.86e-05 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2815.5 on 108 degrees of freedom
Residual devianc...