[R] odds ratios in multiway tables (stratified)
Wim Bertels
wim.bertels at khleuven.be
Wed Aug 6 10:49:03 CEST 2008
On Thu, 2008-07-31 at 10:31 -0500, Marc Schwartz wrote:
> on 07/31/2008 07:40 AM Wim Bertels wrote:
> > On Wed, 2008-07-30 at 12:13 -0500, Marc Schwartz wrote:
> >> on 07/30/2008 08:48 AM Wim Bertels wrote:
> >>> Hi,
> >>>
> >>> does anyone know of a function to calculate odds ratios in multiway
> >>> tables (stratified) (+ the other usual statistics involved)
> >>>
> >>> i mean:
> >>> say we have a table r*c*d,
> >>> For every d (depth) we have a r*c table,
> >>> and in this table the odds ratio's are calculated for every 2*2 subtable
> >>> in it.
> >>>
> >>> logically this function would look like):
> >>> ORs(multiwaytable)
> >>> or
> >>> ORs(data$var1r,data$var2c,data$var3d)
> >>>
> >>> (eg. not taking the lot together, keeping the paradox of simpson in
> >>> mind)
> >>>
> >>> mvg,
> >>> Wim
> >> In ?mantelhaen.test, there is some code in the examples using the
> >> UCBAdmissions data set. There is also code for the Woolf test in the
> >> same example.
> >
> > thanks Marc,
> > but CMH testing supposes a common odds ratio, (hence the woolf test)
>
> Strictly speaking that is not correct, whether one is using the Woolf
> test or the Breslow-Day test. In fact, since you reference SAS below, my
> copy of "Categorical Data Analysis Using the SAS System" by Stokes et al
> from 1995 (back in the days when I was using SAS) notes this in the
> chapter on stratified 2x2 tables (second para on page 53).
>
> This is also noted in CDA 2nd Edition, Agresti (2002), on page 235.
>
> That being said, a significant Woolf or BD test should give you pause
> relative to the validity of the _common_ odds ratio and to consider
> alternatives that enable the analysis of more interesting relationships
> in your data.
i'm not interest in a common odds ratio as such,
more in the "fixed" effects between the different categories
>
> > i am looking for a way to just get all the odds ratios calculated, with
> > a family p-values (for each one) and/or family confidence intervals
> > (since i am doing multiple testing then,.. data snooping..)
> > [i know this is easily done in SAS, but i prefer R..]
> >
> >> In addition, there is similar code in the 'vcd' and 'rmeta' CRAN packages.
> >
> > tnx,
> > structplot looks nice as an extra
>
> I presume that is strucplot in the vcd package?
>
> You might also want to look at the mh() function in the Epi package,
> which I noted doing a quick search this morning.
>
> Also, if the assumption of the homogeneity of odds ratio is not valid in
> this situation, you may want to consider that there is an interaction
> going on and that an alternative analytic approach, such as logistic
> regression with an appropriate interaction term might make sense here.
i know, i completely agree,
helas, i tried this route with lots of variaties,
but had no succes in finding a good model
(btw: i am still looking for a goodness of fit statistic for multinomial
regression, also called baseline regression, like eg the goeman and
lecessie statistic, but it seems not be implemented in R)
>
> This came up in a prior thread, which you might find helpful:
>
> https://stat.ethz.ch/pipermail/r-help/2007-February/126254.html
nicely explained,
also the function to recode to a 3D table is very usefull,
tnx,
Wim
>
> Regards,
>
> Marc
>
>
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