On Jun 4, 2012, at 11:31 AM, lincoln wrote:
So sorry,
My response variable is "site" (not "gender"!).
The selection process was:
If there is a natural probability interpretation to "site"==1 being a
sort of event, (say perhaps a non-lymphatic site for the primary site
of a lymphoma) then you can say that the log-odds for 'site' being 1
compared to the log-odds for being 0 are different among the cohorts.
(Or equivalently that the odds ratios are "significantly" different.)
Worries: The fact that 'age' codes are 1/0 and' birth' is 5,6,or 7
makes me wonder what sort of measurements these are. I worry when
variables usually considered as continuous get so severely
discretized. The fact that this is data measured over time also raised
further concerns about independence. Were controls observed in 1999
still subject to risk in 2000 and subsequent years? Were there
substantial differences in the time to events? I also worry when words
normally used as a location are interpreted as events and there is no
context offered.
--
David.
str(data)
'data.frame': 1003 obs. of 5 variables:
$ site : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ sex : Factor w/ 2 levels "0","1": NA NA NA NA 1 NA NA NA NA NA ...
$ age : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ cohort: Factor w/ 10 levels "1999","2000",..: 10 10 10 10 10 10 10
10 10
10 ...
$ birth : Factor w/ 3 levels "5","6","7": 3 3 2 2 2 2 2 2 2 2 ...
datasex<-subset(data, sex !="NA")
*Here below the structure of the analysis and only the anova.glm of
the
last, selected model, mod4:
*
mod1 <- glm(site ~ sex + birth + cohort + sex:birth, data=datasex,
family =
binomial)
summary(mod1)
anova(mod1,update(mod1,~.-sex:birth),test="Chisq")
mod2 <- glm(site ~ sex + birth + cohort, data=datasex, family =
binomial)
summary(mod2)
anova(mod2,update(mod2,~.-sex),test="Chisq")
mod3 <- glm(site ~ birth + cohort, data=data, family = binomial)
summary(mod3)
anova(mod3,update(mod3,~.-birth),test="Chisq")
mod4 <- glm(site ~ cohort, data=data, family = binomial)
summary(mod4)
anova(mod4,update(mod4,~.-cohort),test="Chisq")
Analysis of Deviance Table
Model 1: site ~ cohort
Model 2: site ~ 1
Resid. Df Resid. Dev Df Deviance P(>|Chi|)
1 993 1283.7
2 1002 1368.2 -9 -84.554 2.002e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
*My question:*
In this case, the Chi2 value would be the difference in deviance
between
models and d.f. the difference in d.f. (84.554 and 9)?
In other words may I correctly assess: /"cohorts were unevenly
distributed
between sites ( Chi2=84.5, df=9, p < 0.001)"/?
--
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http://r.789695.n4.nabble.com/Chi-square-value-of-anova-binomialglmnull-binomglmmod-test-Chisq-tp4632293p4632312.html
Sent from the R help mailing list archive at Nabble.com.
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David Winsemius, MD
West Hartford, CT
______________________________________________
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