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nb2
2005 Oct 20
3
different F test in drop1 and anova
Hi,
I was wondering why anova() and drop1() give different tail
probabilities for F tests.
I guess overdispersion is calculated differently in the following
example, but why?
Thanks for any advice,
Tom
For example:
> x<-c(2,3,4,5,6)
> y<-c(0,1,0,0,1)
> b1<-glm(y~x,binomial)
> b2<-glm(y~1,binomial)
> drop1(b1,test="F")
Single term deletions
Model:
y ~
2009 Nov 26
1
Arrhenius Plot 2 with lattice
...-6*.32e-4*(test$V1[c(2)]+test$V1[c(4)]-test
$V1[c(1)]-test$V1[c(3)]))
PB2<-c(1.1331/100e-6*.32e-4*(test$V1[c(6)]+test$V1[c(8)]-test
$V1[c(5)]-test$V1[c(7)]))
P2<-c(P2,(PA2+PB2)/2)
bew2<-c(bew2,-RH2[c(46)]/P2[c(46)])
QA2<-c(QA2,(test$V1[c(2)]-test$V1[c(1)])/(test$V1[c(4)]-test$V1[c(3)]))
QB2<-c(QB2,(test$V1[c(6)]-test$V1[c(5)])/(test$V1[c(8)]-test$V1[c(7)]))
#.......
Temp<-c(79,80,85,90,95,100,106,110,115,120,125,132,135,140,145,151,156,160,165,170,175,180,185,190,195,200,206,210,216,220,225,230,235,240,247,250,255,261,265,270,275,280,285,290,295,300)
#Here I transform my data...