[R] Problem applying McNemar's - Different values in SPSS and R

[Johannes Huesing]

Marc Schwartz <marc_schwartz at me.com> [Tue, Dec 28, 2010 at 07:14:49PM CET]:
[...]
> > An old question of mine: Is there any reason not to use binom.test()
> > other than historical reasons?
> 

(I meant "in lieu of the McNemar approximation", sorry if some
misunderstanding ensued).

>  I may be missing the context of your question, but I frequently see
> exact binomial tests being used when one is comparing the
> presumptively known probability of some dichotomous characteristic
> versus that which is observed in an independent sample. For example,
> in single arm studies where one is comparing an observed event rate
> against a point estimate for a presumptive historical control.

In the McNemar context (as used by SPSS) the null hypothesis is p=0.5.

>  I also see the use of exact binomial (Clopper-Pearson) confidence
> intervals being used when one wants to have conservative CI's, given
> that the nominal coverage of these are at least as large as
> requested. That is, 95% exact CI's will be at least that large, but
> in reality can tend to be well above that, depending upon various
> factors. This is well documented in various papers.

Confidence intervals are not that regularly used in the McNemar context, as the
conditional probability "a > b given they are unequal" is not that much an
interpretable quantity as is the event probability in a single arm study.

> I generally tend to use Wilson CI's for binomial proportions when
  reporting analyses. I have my own code but these are implemented in
  various R functions, including Frank's binconf() in Hmisc.

Thanks for the hint.
-- 
Johannes Hüsing               There is something fascinating about science. 
                              One gets such wholesale returns of conjecture 
mailto:johannes at huesing.name  from such a trifling investment of fact.                
http://derwisch.wikidot.com         (Mark Twain, "Life on the Mississippi")