[R] lmer fixed effects, SE, t . . . and p
array chip
arrayprofile at yahoo.com
Fri Sep 10 18:51:59 CEST 2010
But as far as I know, profile() seems to be de-activated in the lme4 package.
----- Original Message ----
From: Gavin Simpson <gavin.simpson at ucl.ac.uk>
To: John Sorkin <jsorkin at grecc.umaryland.edu>
Cc: r-help at r-project.org; Bert Gunter <gunter.berton at gene.com>
Sent: Fri, September 10, 2010 2:05:37 AM
Subject: Re: [R] lmer fixed effects, SE, t . . . and p
On Thu, 2010-09-09 at 23:40 -0400, John Sorkin wrote:
> Bert,
> I appreciate you comments, and I have read Doug Bates writing about p
> values in mixed effects regression. It is precisely because I read
> Doug's material that I asked "how are we to interpret the estimates"
> rather than "how can we compute a p value". My question is a simple
> question whose answer is undoubtedly complex, but one that needs an
> answer. Without p values, or confidence intervals, I am not certain
> what to make of the results of my analysis. Does my analysis suggest,
> or does it not suggest that there is a relation between time and y? If
> I can't answer this question after running the analysis, I don't have
> any more information than I did before I ran the analysis, and a fair
> question would be why did I run the analysis? I am asking for help not
> in calculation a p value or a CI, but rather to know what I can and
> can't say about the results of the analysis. If this basic question
> can not be answered, I am at a loss to interpret my results.
> Thank you,
> John
Doug talks quite a lot about profiling lmer fits using 'profile
deviance' to investigate variability in fixed effects. For example, see
section 1.5 in the draft of chapter 1 of Doug's book on mixed models:
http://lme4.r-forge.r-project.org/book/
HTH
G
> John David Sorkin M.D., Ph.D.
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)>>> Bert
>Gunter <gunter.berton at gene.com> 9/9/2010 11:21 PM >>>
> John:
>
> Search on this issue in the list archives. Doug Bates has addressed it
> at length. Basically, he does not calculate CI's or p-values because
> he does not know how to reliably do so.
>
> However, the key remark in your query was:
>
> > (2) lmer does not give p values or confidence intervals for the fixed
>effects. How we are to interpret the estimates given that no p value or CI is
>given for the estimates?
>
> Think about it. A statistical analysis -- ANY statistical analysis --
> treats the data in isolation: it is not informed by physics,
> thermodynamics, biology, other similar data, prior experience, or,
> indeed, any part of the body of relevant scientific knowledge. Do you
> really think that any such analysis, especially when predicated upon
> often tenuous or even (necessarily) unverifiable assumptions and
> simplifications should be considered authoritative? Classical
> statistical inference is just another piece of the puzzle, and not
> even particularly useful when, as if typically the case, hypotheses
> are formulated AFTER seeing the data (this invalidates the probability
> calculations -- hypotheses must be formulated before seeing the data
> to be meaningfully assessed). Leo Breiman called this statistics'
> "quiet scandal" something like 20 years ago, and he was no dummy.
>
> It is comforting, perhaps, but illusory to believe that statistical
> inference can be relied on to give sound, objective scientific
> results. True, without such a framework, science seems rather
> subjective, perhaps closer to religion and arbitrary cultural
> archetypes than we care to admit. But see Thomas Kuhn and Paul
> Feuerabend for why this is neither surprising nor necessarily a bad
> thing.
>
> Cheers,
> Bert Gunter
>
>
>
>
> On Thu, Sep 9, 2010 at 8:00 PM, John Sorkin <jsorkin at grecc.umaryland.edu>
>wrote:
> > windows Vista
> > R 2.10.1
> >
> >
> > (1) How can I get the complete table of for the fixed effects from lmer. As
>can be seen from the example below, fixef(fit2) only give the estimates and not
>the SE or t value
> >
> >> fit3<- lmer(y~time + (1|Subject) + (time|Subject),data=data.frame(data))
> >> summary(fit3)
> > Linear mixed model fit by REML
> > Formula: y ~ time + (1 | Subject) + (time | Subject)
> > Data: data.frame(data)
> > AIC BIC logLik deviance REMLdev
> > -126.2 -116.4 70.1 -152.5 -140.2
> > Random effects:
> > Groups Name Variance Std.Dev. Corr
> > Subject (Intercept) 2.9311e+01 5.41396385
> > Subject (Intercept) 0.0000e+00 0.00000000
> > time 0.0000e+00 0.00000000 NaN
> > Residual 8.1591e-07 0.00090328
> > Number of obs: 30, groups: Subject, 10
> >
> > Fixed effects:
> > Estimate Std. Error t value
> > (Intercept) 14.998216 1.712046 9
> > time -0.999779 0.000202 -4950
> >
> > Correlation of Fixed Effects:
> > (Intr)
> > time -0.001
> >> fixef(fit3)
> > (Intercept) time
> > 14.9982158 -0.9997793
> >
> > (2) lmer does not give p values or confidence intervals for the fixed
>effects. How we are to interpret the estimates given that no p value or CI is
>given for the estimates?
> >
> >
> >
> >
> > John David Sorkin M.D., Ph.D.
> > Chief, Biostatistics and Informatics
> > University of Maryland School of Medicine Division of Gerontology
> > Baltimore VA Medical Center
> > 10 North Greene Street
> > GRECC (BT/18/GR)
> > Baltimore, MD 21201-1524
> > (Phone) 410-605-7119
> > (Fax) 410-605-7913 (Please call phone number above prior to faxing)
> >
> > Confidentiality Statement:
> > This email message, including any attachments, is for ...{{dropped:25}}
>
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