If we check the data, we see a mistake in the entry.
> tbl.8.3
mara cig alc count
1 Yes Yes Yes 911
2 No No No 538
3 Yes Yes Yes 44
4 No No No 456
5 Yes Yes Yes 3
6 No No No 43
7 Yes Yes Yes 2
8 No No No 279
Try:
table8.3 <- read.table(textConnection("alc cig mar count
Yes Yes Yes 911
Yes Yes No 538
Yes No Yes 44
Yes No No 456
No Yes Yes 3
No Yes No 43
No No Yes 2
No No No 279"),header=TRUE)
closeAllConnections()
myglm <- glm(count ~ mar+cig+alc, family=poisson, data=table8.3)
myglm$fitted.values
> myglm$fitted.values
1 2 3 4 5 6
7 8
539.98258 740.22612 282.09123 386.70007 90.59739 124.19392 47.32880
64.87990
This reproduces the fitted values for your example (Agresti, pg 323)
On Wed, Jan 14, 2009 at 7:23 AM, Gerard M. Keogh <gmke...@justice.ie> wrote:
>
> Dear all,
>
> sorry to bother you all with this but I've been trying to use the loglm in
> MASS package (v2.8.0) and cannot get any sensible output.
> I'm wondering am I doing something very foolish or missing something
> obvious.
>
> For example, I tried the documentation help(loglm) example - here's the
> code
>
> # Case 1: frequencies specified as an array.
> sapply(minn38, function(x) length(levels(x)))
> ## hs phs fol sex f
> ## 3 4 7 2 0
> minn38a <- array(0, c(3,4,7,2), lapply(minn38[, -5], levels))
> minn38a[data.matrix(minn38[,-5])] <- minn38$fol
> fm <- loglm(~1 + 2 + 3 + 4, minn38a) # numerals as names.
> deviance(fm)
> > [1] 0
>
> The deviance is zero.
> I tried other examples as well (Laura Thompson) and get pretty much
> nonsense.
>
> E.g.
>
> library(MASS)
> options(contrasts=c("contr.treatment", "contr.poly"))
> tbl.8.3 = data.frame(mara
> =factor(c("Yes","No"),levels=c("Yes","No")),
> cig
> =factor(c("Yes","No"),levels=c("No","Yes")),
> alc
> =factor(c("Yes","No"),levels=c("Yes","No")),
> count=c(911,538,44,456,3,43,2,279) )
> fit.acm = loglm( count ~ alc*cig*mara,data=tbl.8.3, param=T, fit=T)
>
>
> Any advice greatly appreciated.
>
> Gerard
>
>
>
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