Significance stars (was Re: R-beta: glm bug)
maechler at stat.math.ethz.ch
Fri Jun 19 21:14:55 CEST 1998
>>>>> "PD" == Peter Dalgaard BSA <p.dalgaard at biostat.ku.dk> writes:
PD> Martin Maechler <maechler at stat.math.ethz.ch> writes:
>> Jim Lindsey wanted a *global* option so that he can turn it off for all
>> his students when they just use
>> Any proposals for a NAME of the global option?
PD> Anything wrong with signif.stars ?
well, yes insofar that you must explicitly use
print( summary(glm( model1...)), signif.stars = FALSE)
print( summary(glm( model2...)), signif.stars = FALSE)
print( summary(glm( model3...)), signif.stars = FALSE)
as opposed to
options(show.signif.stars = FALSE)
I've actually implemented the above behavior in the 0.63 development
Note that the default option remains TRUE,
but anyone can change this by using [.]Rprofile(s).
>> JL even looked for a possibility to turn off the P values..
>> Do we want/need this as well?
PD> Nope. (Did he?)
yes, he did.
But I think we should leave them.
If anyone hates them, he can write a package
which redefines every thing he wants...
PD> These are much too often important when you need to
PD> compensate for multiple testing and so forth. Even though a lot of
PD> good things can be said about confidence intervals, they do have the
PD> problem that the implied tests are always at a fixed level.
>> NOTE: We do *NOT* advocate believing in P-values,
was not "R-core" but ``people at ETH stat.dept''
PD> Well, actually, I *do* advocate that. At least in the sense that
PD> probability exist and one needs to understand data in a probabilistic
PD> context. This is not, however, the same as the boneheaded logic of
PD> setting strict on/off rules based on the formal p-values. P-values are
PD> usually fairly good approximations of a reasonably well-defined
PD> quantity, the main problem being that it is often not that quantity
PD> which is the relevant one.
Ok, I hope this closes the topic...
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