From: Spencer Graves <spencer.graves_at_pdf.com>

Date: Fri, 28 Mar 2008 07:49:21 -0700

R-help_at_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. Received on Fri 28 Mar 2008 - 14:51:43 GMT

Date: Fri, 28 Mar 2008 07:49:21 -0700

Do you think p values are bad? That's not my understanding. P values may not be reported by some software, because the algorithm developers didn't know how to efficiently compute a reasonably accurate p value. And if you do a thousand or a million statistical tests and report only the results with the smallest p value, that's ultimately fraudulent. (http://en.wikipedia.org/wiki/Multiple_comparisons).

However, the concept of a significance probability or p value is quite valuable (http://en.wikipedia.org/wiki/P-value), though like any tool or concept, it can be misused.

Hope this helps. Spencer Graves

Monica Pisica wrote:

> Hi Spencer and David,

*>
**> Thanks for your answers .... first - yes it is deviance but just
**> before i just spoke and explain that it is the equivalent of r square
**> from the "normal" regression.....
**>
**> I hope i can do the comparison and show that the model is significant
**> and hopefully i am off the hook. Sincerely i try to avoid all this
**> business with p-values but certainly some are quite found of it. The
**> problem is that you get almost by default a p-value from an F test if
**> you use lm for example, so ..... quite few times i was asked to
**> provide a similar thing for quite different models.
**>
**> Thanks again,
**>
**> Monica
**>
**> > Date: Thu, 27 Mar 2008 16:38:12 -0700
**> > From: spencer.graves_at_pdf.com
**> > To: pisicandru_at_hotmail.com
**> > CC: r-help_at_r-project.org
**> > Subject: Re: [R] dreaded p-val for d^2 of a glm / gam
**> >
**> > I assume you mean 'deviance', not 'squared deviance'; if the
**> > latter, then I have no idea.
**> >
**> > If the former, then a short and fairly quick answer to your
**> > question is that 2*log(likelihood ratio) for nested hypotheses is
**> > approximately chi-square with numbers of degrees of freedom = the
**> number
**> > of parameters in the larger model fixed to get the smaller model, under
**> > standard regularity conditions, the most important of which is that the
**> > maximum likelihood is not at a boundary.
**> >
**> > For specificity, consider the following modification of the first
**> > example in the 'glm' help page:
**> >
**> > counts <- c(18,17,15,20,10,20,25,13,12)
**> > outcome <- gl(3,1,9)
**> > treatment <- gl(3,3)
**> > glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
**> > glm.D93t <- glm(counts ~ treatment, family=poisson())
**> > anova(glm.D93t, glm.D93, test="Chisq")
**> >
**> > The p-value is not printed by default, because some people would
**> > rather NOT give an answer than give an answer that might not be very
**> > accurate in the cases where this chi-square approximation is not very
**> > good. To check that, you could do a Monte Carlo, refit the model with,
**> > say, 1000 random permutations of your response variable, collect
**> > anova(glm.D93t, glm.D93)[2, "Deviance"] in a vector, and then find out
**> > how extreme the deviance you actually got is relative to this
**> > permutation distribution.
**> >
**> > Hope this helps.
**> > Spencer Graves
**> > p.s. Regarding your 'dread', please see fortune("children")
**> >
**> > Monica Pisica wrote:
**> > > OK,
**> > >
**> > > I really dread to ask that .... much more that I know some
**> discussion about p-values and if they are relevant for regressions
**> were already on the list. I know to get p-val of regression
**> coefficients - this is not a problem. But unfortunately one editor of
**> a journal where i would like to publish some results insists in giving
**> p-values for the squared deviance i get out from different glm and gam
**> models. I came up with this solution, but sincerely i would like to
**> get yours'all opinion on the matter.
**> > >
**> > > p1.glm <- glm(count ~be+ch+crr+home, family = 'poisson')
**> > >
**> > > # count - is count of species (vegetation)
**> > > # be, ch, crr, home - different lidar metrics
**> > >
**> > > # calculating d^2
**> > > d2.p1 <- round((p1.glm[[12]]-p1.glm[[10]])/p1.glm[[12]],4)
**> > > d2.p1
**> > > 0.6705
**> > >
**> > > # calculating f statistics with N = 148 and n=4; f =
**> (N-n-1)/(N-1)(1-d^2)
**> > > f <- (148-4-1)/(147*(1-0.6705))
**> > > f
**> > > [1] 2.952319
**> > >
**> > > #calculating p-value
**> > > pval.glm <- 1-pf(f, 147,143)
**> > > pval.glm
**> > > [1] 1.135693e-10
**> > >
**> > > So, what do you think? Is this acceptable if i really have to give
**> a p-value for the deviance squared? If it is i think i will transform
**> everything in a fuction ....
**> > >
**> > > Thanks,
**> > >
**> > > Monica
**> > > _________________________________________________________________
**> > > Windows Live Hotmail is giving away Zunes.
**> > >
**> > > M_Mobile_Zune_V3
**> > > ______________________________________________
**> > > R-help_at_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.
**> > >
**>
**>
**> ------------------------------------------------------------------------
**> Watch “Cause Effect,” a show about real people making a real
**> difference. Learn more.
**> <http://im.live.com/Messenger/IM/MTV/?source=text_watchcause>
*

R-help_at_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. Received on Fri 28 Mar 2008 - 14:51:43 GMT

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.2.0, at Fri 28 Mar 2008 - 15:30:25 GMT.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*