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 > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
