The change is in pchisq:
2.4.1:
pchisq(3, 0, lower=F)
[1] NaN
2.7.1:
pchisq(3, 0, lower=F)
[1] 0
And that is correct according to the definition.
On Mon, 11 Aug 2008, Lutz Ph. Breitling wrote:
Dear List,
recently tried to reproduce the results of some custom model selection
function after updating R, which unfortunately failed. However, I
ultimately found the issue to be that testing with pchisq() in drop1()
seems to have changed. In the below example, earlier versions (e.g. R
2.4.1) produce a missing P-value for the variable x,
Are you sure it was not NaN?
while newer versions (e.g. R 2.7.1) produce 0 (2.2e-16).
I would assume that the former is more appropriate, so I was just
curious if this is an intentional change.
In this case the computed value ought to be zero, and hence
pchisq(0, 0, lower=F)
[1] 0
Given that the computed value will be unreliable, NA would be more
appropriate, so will change.
Kind regards-
Lutz
y<-rbinom(100,1,0.5)
x<-rep(0,100)
m<-glm(y~x, family=binomial)
summary(m)
drop1(m, test="Chisq")
#################
R241> drop1(m, test="Chisq")
Single term deletions
Model:
y ~ x
Df Deviance AIC LRT Pr(Chi)
<none> 138.59 140.59
x 0 138.59 140.59 0.00
Warning message:
NaNs produced in: pchisq(q, df, lower.tail, log.p)
##################
R271> drop1(m, test="Chisq")
Single term deletions
Model:
y ~ x
Df Deviance AIC LRT Pr(Chi)
<none> 137.99 139.99
x 0 137.99 139.99 0.00 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
###################
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Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
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