On Wed, 18 Feb 2009, maiya wrote:
I realise that in the case of loglin the parameters are clacluated post
festum from the cell frequencies,
however other programmes that use Newton-Raphson as opposed to IPF work the
other way round, right?
In which case one would expect the output of parameters to be limited to the
particular contrast used. But since loglin uses IPF I would have thought the
choice of style of parameter to be output could be made...
Anyway, this is the line that interests me:
lm( as.vector( loglin(...,fit=TRUE)$fit ) ~ < your favored contrasts > )
only I'm not profficient in R to figure out the last term :(
How would I go about this then if my prefered contrasti is setting the first
categories as reference cats?
See An Introduction to R Chapter 11
and try this:
for ( i in ls('package:stats',pat='contr[.]')){
cat( i, '\n' )
print( get(i)(letters[1:5]) )
options(contrasts=c(unordered=i,ordered='contr.poly'))
print( coef( glm( Freq~ Dept*Gender,
as.data.frame(UCBAdmissions),family=poisson)) )
}
I literaly just need the equivalent of
loglin(matrix(c(1,2,3,4), nrow=2), list(c(1,2)), param=TRUE)
which would give me parameters under indicator contrast. glm... well, I'd
have to work on it
Regarding the more general points
ad 2) I would have thought that direct inspection of cell frequencies is
precisely the wrong/misleading thing to do - the highest order coefficients
can be inspected directly in order to see the interaction without the
(lower) marginal effects, or alternatively the table can be standardized to
uniform margins for the same sort of inspection.
OK, to each her own.
But try this out yourself. What is the story here?
(Review ?UCBAdmissions, if you need to.)
options(contrasts=c(unordered='contr.sum',ordered='contr.poly'))
print( cbind(coef( glm( Freq~ Admit*Dept*Gender,
as.data.frame(UCBAdmissions),family=poisson)) ))
[,1]
(Intercept) 4.786575880
Admit1 -0.277614562
Dept1 0.067824911
Dept2 -0.758615446
Dept3 0.560293364
Dept4 0.446131873
Dept5 -0.001254892
Gender1 0.355262130
Admit1:Dept1 0.786694268
Admit1:Dept2 0.599494828
Admit1:Dept3 -0.021374963
Admit1:Dept4 -0.053867688
Admit1:Dept5 -0.250913079
Admit1:Gender1 -0.050744703
Dept1:Gender1 0.782600986
Dept2:Gender1 1.216370861
Dept3:Gender1 -0.646880514
Dept4:Gender1 -0.308737151
Dept5:Gender1 -0.691810320
Admit1:Dept1:Gender1 -0.212274286
Admit1:Dept2:Gender1 -0.004260932
Admit1:Dept3:Gender1 0.081975109
Admit1:Dept4:Gender1 0.030247904
Admit1:Dept5:Gender1 0.100791458
OK, got the whole story? Could you explain it to someone who is not a
statistician?
Now try it again. But with this display:
ftable(UCBAdmissions)
Dept A B C D E F
Admit Gender
Admitted Male 512 353 120 138 53 22
Female 89 17 202 131 94 24
Rejected Male 313 207 205 279 138 351
Female 19 8 391 244 299 317
round( ftable(prop.table(UCBAdmissions,2:3)) ,2)
Dept A B C D E F
Admit Gender
Admitted Male 0.62 0.63 0.37 0.33 0.28 0.06
Female 0.82 0.68 0.34 0.35 0.24 0.07
Rejected Male 0.38 0.37 0.63 0.67 0.72 0.94
Female 0.18 0.32 0.66 0.65 0.76 0.93
You can pretty easily see that admission rates vary by department, that
all departments but one have pretty equal admission rates by gender and
that in that department the rate is a 20% higher for females. (And yes,
a significance test confirms this).
Maybe not a "statistified" as talking about three-way interactions and
coefficients of products of contrasts, but I'll bet a lot of scientists
would find the tables more compelling.
HTH,
Chuck
ad 3) and yes, I figured as much! I can't see how lower order terms can be
interpreted at all if higher order interactions exist? I've seen it done,
e.g I've seen it claimed that in a standardized table the lower order terms
are all equal to zero, which is of course not true?
Thanks!
Maja
--
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http://www.nabble.com/indicator-or-deviation-contrasts-in-log-linear-modelling-tp22090104p22093070.html
Sent from the R help mailing list archive at Nabble.com.
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Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cbe...@tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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