log-linear model and log-likelihood ratio

resid(f1)

f1$lrt # G2 stat

f1$df # associated df

Dear all,

I am trying learn something (on analysis of frequencies) by reproducing the
examples from Quinn & Keough book (experimental design...) from chapter 14,
section 14.3 on log-linear models.

# This would be the data
floodplain=data.frame(y=c(15,4,0, 13,8,17),
             position=gl(3,1,6, labels=c("bottom","middle","top")),
             dead_coli=gl(2,3,labels=c("with","without")))

# The first full model would be (log-linear model to calculate the
log-likelihood ratio):
loglm(y ~ position + dead_coli + position:dead_coli, floodplain) # which
includes interaction.. and does not work, not even if I add a constant of .5
to y. In the book it is suppose to give: -10.429

# the second reduced model would be (does not work)
loglm(y~ position + dead_coli, floodplain) #which works, but gives a
different result from the book. In the book it gives: -19.735

This is needed to calculate G2 statistic (from Sokal and Rohlf) for which I
could not find anything in R, also.

Any idea about what is wrong or the correct calculations? I looked on Sokal
and Rohlf, and had the same problem...

Thank you very much e in advance,
Best wishes,
Marta