[R-sig-ME] fixed vs random
Daniel Ezra Johnson
danielezrajohnson at gmail.com
Sun Mar 28 23:12:21 CEST 2010
Terms such as (gender|candidate) estimate a gender effect that can
vary across candidates. It is presumably meaningless to discuss the
gender effect of any individual candidate, so this term should not be
used.
The form of m2 is preferred to m1 in most cases when you have a
legitimate random slope variable, for example if you had a factor
"difficulty" referring to the difficulty of the questions, it would be
meaningful to estimate the effect of question difficulty separately
for each candidate, so a model like
m3 <- lmer(score ~ 1 + gender + difficulty + (difficulty|candidate), mg2006_sub)
might be sensible, but not one with gender as a random slope over candidate.
Dan
On Sun, Mar 28, 2010 at 4:21 PM, Iasonas Lamprianou
<lamprianou at yahoo.com> wrote:
>
> Dear colleagues,
> I am not sure what the difference between those models is:
>
> m0<- lmer(score ~ 1+gender+(1|candidate), mg2006_sub)
> m1<- lmer(score ~ 1+(1+gender|candidate), mg2006_sub)
> m2 <- lmer(score ~ 1+gender+(1+gender|candidate), mg2006_sub)
>
> the first model is modelling the candidate as a random effect in an examination, where two markers mark each response of a candidate (a repeated measure). I assume that the gender of the candidate is a good predictor of performance on the test, so I can use any of the three models. But I do not understand what the difference is. Why would I get different results between m0 and m1? In effect, I am just adding the gender as a fixed effect.And is m2 a valid model?
>
> thank you
>
> jason
>
>
>
> Dr. Iasonas Lamprianou
>
>
> Assistant Professor (Educational Research and Evaluation)
> Department of Education Sciences
> European University-Cyprus
> P.O. Box 22006
> 1516 Nicosia
> Cyprus
> Tel.: +357-22-713178
> Fax: +357-22-590539
>
>
> Honorary Research Fellow
> Department of Education
> The University of Manchester
> Oxford Road, Manchester M13 9PL, UK
> Tel. 0044 161 275 3485
> iasonas.lamprianou at manchester.ac.uk
>
>
> --- On Sun, 28/3/10, r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org> wrote:
>
>> From: r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org>
>> Subject: R-sig-mixed-models Digest, Vol 39, Issue 42
>> To: r-sig-mixed-models at r-project.org
>> Date: Sunday, 28 March, 2010, 11:00
>> Send R-sig-mixed-models mailing list
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>> Today's Topics:
>>
>> 1. Re: Could not get a lmer or glmer
>> summary (Eric Edeline)
>> 2. Re: Significance and lmer (Ben
>> Bolker)
>> 3. Re: Significance and lmer (Adam D. I.
>> Kramer)
>> 4. Re: Significance and lmer (David
>> Duffy)
>> 5. Re: Significance and lmer (Adam D. I.
>> Kramer)
>> 6. Very weird lmer results, compared to
>> SAS proc mix (Yong Wu)
>> 7. Re: Very weird lmer results, compared
>> to SAS proc mix
>> (hadley wickham)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Sat, 27 Mar 2010 14:59:48 +0100
>> From: Eric Edeline <edeline at biologie.ens.fr>
>> To: David Duffy <David.Duffy at qimr.edu.au>
>> Cc: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] Could not get a lmer or glmer
>> summary
>> Message-ID: <4BAE0F54.3000809 at biologie.ens.fr>
>> Content-Type: text/plain; charset=ISO-8859-1;
>> format=flowed
>>
>> I have solved my problem, which was apparently due to a
>> conflict between
>> lme4 and another library (one of these: tree, VGAM, sn,
>> Matrix,
>> mclust...) about an "rcon" object if I remember well.
>> Removing all the
>> libraries including lme4 from /usr/local/lib/R/site-library
>> and
>> re-installing lme4 in /usr/lib/R/site-library made the
>> trick. Sorry for
>> not providing more detailed information, I just do not
>> remember more!
>>
>> Cheers,
>>
>> eric
>>
>>
>>
>> David Duffy wrote:
>> > On Fri, 26 Mar 2010, Eric Edeline wrote:
>> >
>> >> Dear Ben,
>> >>
>> >> thank you for your feed-back. I have now tested
>> lmer on several
>> >> datasets and I always get the same error message
>> when asking for
>> >> model summary. So the problem is with lme4, not
>> with the data. Then,
>> >> I ran the exact same models and data on another
>> machine and it works
>> >> fine! So the lme4 problem is specific to my
>> machine. Then, I tried
>> >> brute force: uninstalling and re-installing R on
>> my machine, but the
>> >> lme4 problem remains.
>> >
>> > Therefore, you either need to "just" extract the
>> results you want from
>> > m11
>> > directly (doing any necessary calculations yourself),
>> or step through
>> > using a
>> > debugger, or send all the files to Douglas Bates ;)
>> >
>> > Cheers, David Duffy.
>>
>> --
>> Eric Edeline
>> Assistant Professor
>> UMR 7618 BIOEMCO
>> Ecole Normale Sup?rieure
>> 46 rue d'Ulm
>> 75230 Paris cedex 05
>> France
>>
>> Tel: +33 (0)1 44 32 38 84
>> Fax: +33 (0)1 44 32 38 85
>>
>> http://www.biologie.ens.fr/bioemco/biodiversite/edeline.html
>>
>>
>>
>> ------------------------------
>>
>> Message: 2
>> Date: Sat, 27 Mar 2010 15:04:42 +0000 (UTC)
>> From: Ben Bolker <bolker at ufl.edu>
>> To: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] Significance and lmer
>> Message-ID: <loom.20100327T160050-336 at post.gmane.org>
>> Content-Type: text/plain; charset=us-ascii
>>
>> Adam D. I. Kramer <adik at ...> writes:
>>
>> >
>> > Dear colleagues,
>> >
>> > Please consider this series of commands:
>> >
>> > a <- lmer(log(stddiff+.1539) ~ pred + m*v + option
>> + (option|studyID),
>> > data=r1, subset=option>1, REML=FALSE)
>> >
>> > b <- update(a, . ~ . - pred)
>> >
>> > anova(a,b)
>> >
>> > ...am I mistaken in thinking that the latter command
>> will produce a test of
>> > whether "pred" is a significant predictor of
>> log(stddiff+.1539)? I am
>> > concerned because of the results:
>> >
>>
>> [snip]
>>
>> > ...a significant result completely unrelated to the
>> t-value. My
>> > interpretation of this would be that we have no good
>> evidence that the
>> > estimate for 'pred' is nonzero, but including pred in
>> the model improves
>> > prediction.
>>
>> It is possible for Wald tests (as provided by
>> summary()) to
>> disagree radically with likelihood ratio tests (look up
>> "Hauck-Donner
>> effects", but my guess is that's not what's going
>> on here (it definitely can apply in binomial models, don't
>> think
>> it should apply to LMMs but ?).
>>
>> I have seen some wonky stuff happen with update()
>> [sorry, can't
>> provide any reproducible details], I would definitely try
>> fitting
>> b by spelling out the full model rather than using update()
>> and
>> see if that makes a difference.
>>
>> Other than that, nothing springs to mind.
>>
>> (Where does the log(x+0.1539) transformation come
>> from???)
>>
>>
>>
>> ------------------------------
>>
>> Message: 3
>> Date: Sat, 27 Mar 2010 10:09:41 -0700 (PDT)
>> From: "Adam D. I. Kramer" <adik at ilovebacon.org>
>> To: Ben Bolker <bolker at ufl.edu>
>> Cc: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] Significance and lmer
>> Message-ID: <Pine.LNX.4.64.1003270955500.17783 at ilovebacon.org>
>> Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
>>
>>
>> On Sat, 27 Mar 2010, Ben Bolker wrote:
>>
>> >> ...a significant result completely unrelated to
>> the t-value. My
>> >> interpretation of this would be that we have no
>> good evidence that the
>> >> estimate for 'pred' is nonzero, but including pred
>> in the model improves
>> >> prediction.
>> >
>> > It is possible for Wald tests (as provided by
>> summary()) to disagree
>> > radically with likelihood ratio tests (look up
>> "Hauck-Donner effects", but
>> > my guess is that's not what's going on here (it
>> definitely can apply in
>> > binomial models, don't think it should apply to LMMs
>> but ?).
>>
>> There are no Wald tests produced by the summary()...my
>> understanding from
>> reading this list is that the t-values are provided because
>> they are t-like
>> (effect / se), but that it is difficult (and perhaps
>> foolish) to estimate
>> degrees of freedom for t. So my concern is based on the
>> fact that t is very
>> small.
>>
>> > I have seen some wonky stuff happen with
>> update() [sorry, can't provide
>> > any reproducible details], I would definitely try
>> fitting b by spelling
>> > out the full model rather than using update() and see
>> if that makes a
>> > difference.
>>
>> This produces no difference in b's estimates or the anova()
>> statistics.
>> (That said, I originally was fitting [implicitly] with
>> REML=TRUE, which did
>> make a difference, but not a big one).
>>
>> > Other than that, nothing springs to mind.
>>
>> Well, thanks for the reply. Are you, then, of the opinion
>> that the above
>> interpretation is reasonable?
>>
>> > (Where does the log(x+0.1539) transformation
>> come from???)
>>
>> x is power-law distributed with a bunch of zeroes (but not
>> ordinal, or I'd
>> use family=poisson), and .1539 is the 25th percentile. This
>> normalizes is
>> pretty well. Good question, though! And thanks ofr the
>> response!
>>
>> --Adam
>>
>>
>>
>> ------------------------------
>>
>> Message: 4
>> Date: Sun, 28 Mar 2010 08:04:03 +1000 (EST)
>> From: David Duffy <David.Duffy at qimr.edu.au>
>> To: "Adam D. I. Kramer" <adik at ilovebacon.org>
>> Cc: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] Significance and lmer
>> Message-ID: <Pine.LNX.4.64.1003280753090.29716 at orpheus.qimr.edu.au>
>> Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
>>
>> On Sat, 27 Mar 2010, Adam D. I. Kramer wrote:
>> > On Sat, 27 Mar 2010, Ben Bolker wrote:
>> >
>> >>> ...a significant result completely unrelated
>> to the t-value. My
>> >>> interpretation of this would be that we have
>> no good evidence that the
>> >>> estimate for 'pred' is nonzero, but including
>> pred in the model improves
>> >>> prediction.
>> >>
>> >
>> >> I have seen some wonky stuff happen with
>> update() [sorry, can't provide
>> >> any reproducible details], I would definitely try
>> fitting b by spelling
>> >> out the full model rather than using update() and
>> see if that makes a
>> >> difference.
>> >
>> > This produces no difference in b's estimates or the
>> anova() statistics.
>> > (That said, I originally was fitting [implicitly] with
>> REML=TRUE, which did
>> > make a difference, but not a big one).
>>
>> The two models both have the same number of observations,
>> one hopes? How
>> many observations per studyID and how many studyIDs?
>>
>> > Well, thanks for the reply. Are you, then, of the
>> opinion that the above
>> > interpretation is reasonable?
>>
>> I would be a bit nervous. My interpretation would be
>> that the model is
>> inappropriate for the data (as the Wald and LR tests should
>> roughly agree
>> for a LMM, as Ben pointed out), and would look at
>> diagnostic plots of
>> residuals etc. The bunch of zeroes you mention may
>> still be stuffing
>> things up ;) Is a left-censored model plausible?
>>
>> Just my 2c, David Duffy.
>>
>> --
>> | David Duffy (MBBS PhD)
>>
>>
>> ,-_|\
>> | email: davidD at qimr.edu.au
>> ph: INT+61+7+3362-0217 fax: -0101 /
>> *
>> | Epidemiology Unit, Queensland Institute of Medical
>> Research \_,-._/
>> | 300 Herston Rd, Brisbane, Queensland 4029,
>> Australia GPG 4D0B994A v
>>
>>
>>
>> ------------------------------
>>
>> Message: 5
>> Date: Sat, 27 Mar 2010 16:17:53 -0700 (PDT)
>> From: "Adam D. I. Kramer" <adik at ilovebacon.org>
>> To: David Duffy <David.Duffy at qimr.edu.au>
>> Cc: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] Significance and lmer
>> Message-ID: <Pine.LNX.4.64.1003271609530.17783 at ilovebacon.org>
>> Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
>>
>> The problem turned out to be, indeed, differing numbers of
>> observations.
>> This is likely due to me relying too much on update() to
>> work as I
>> expected...it did not drop the observations previously
>> dropped. The help
>> page for update makes it very clear that it just
>> re-evaluates an altered
>> call, so this is my fault. Ben's comment about update()
>> being wonky should
>> have given me a hint.
>>
>> Preselecting cases using complete.cases() for both models
>> brought the t
>> values and chi-square values much closer together--when
>> t=.51 for the
>> coefficient, the chisq of a likelihood test for removing
>> the variable from
>> the model was chisq=.25, leading to a reasonable p=.62.
>>
>> Thanks very much to you and Ben Bolker!
>>
>> --Adam
>>
>> On Sun, 28 Mar 2010, David Duffy wrote:
>>
>> > On Sat, 27 Mar 2010, Adam D. I. Kramer wrote:
>> >> On Sat, 27 Mar 2010, Ben Bolker wrote:
>> >>
>> >>>> ...a significant result completely
>> unrelated to the t-value. My
>> >>>> interpretation of this would be that we
>> have no good evidence that the
>> >>>> estimate for 'pred' is nonzero, but
>> including pred in the model improves
>> >>>> prediction.
>> >>>
>> >>
>> >>> I have seen some wonky stuff happen with
>> update() [sorry, can't provide
>> >>> any reproducible details], I would definitely
>> try fitting b by spelling
>> >>> out the full model rather than using update()
>> and see if that makes a
>> >>> difference.
>> >>
>> >> This produces no difference in b's estimates or
>> the anova() statistics.
>> >> (That said, I originally was fitting [implicitly]
>> with REML=TRUE, which did
>> >> make a difference, but not a big one).
>> >
>> > The two models both have the same number of
>> observations, one hopes? How
>> > many observations per studyID and how many studyIDs?
>> >
>> >> Well, thanks for the reply. Are you, then, of the
>> opinion that the above
>> >> interpretation is reasonable?
>> >
>> > I would be a bit nervous. My interpretation
>> would be that the model is
>> > inappropriate for the data (as the Wald and LR tests
>> should roughly agree for
>> > a LMM, as Ben pointed out), and would look at
>> diagnostic plots of residuals
>> > etc. The bunch of zeroes you mention may still
>> be stuffing things up ;) Is
>> > a left-censored model plausible?
>> >
>> > Just my 2c, David Duffy.
>> >
>> > --
>> > | David Duffy (MBBS PhD)
>>
>>
>> ,-_|\
>> > | email: davidD at qimr.edu.au
>> ph: INT+61+7+3362-0217 fax: -0101 /
>> *
>> > | Epidemiology Unit, Queensland Institute of Medical
>> Research \_,-._/
>> > | 300 Herston Rd, Brisbane, Queensland 4029,
>> Australia GPG 4D0B994A v
>> >
>>
>>
>>
>> ------------------------------
>>
>> Message: 6
>> Date: Sat, 27 Mar 2010 23:25:21 -0500
>> From: Yong Wu <wuyong88 at gmail.com>
>> To: r-sig-mixed-models at r-project.org
>> Subject: [R-sig-ME] Very weird lmer results, compared to
>> SAS proc mix
>> Message-ID:
>> <cfa5b89e1003272125r1677f3ddl8004de6f726683cd at mail.gmail.com>
>> Content-Type: text/plain
>>
>> Sorry to bother you. I am struggling in this issue for long
>> time. Wish
>> somebody can help me.
>>
>> I first used lmer to do the following analysis.
>> fullmodel=lmer(BMI~1+exposure+(age|ID),data, REML=FALSE)
>>
>> reducemodel=lmer(BMI~1+(age|ID),data, REML=FALSE)
>> anova(full,red)
>> The "fullmodel" has AIC of 6874 and "reducemodel" has AIC
>> of 7106, which
>> cause "anova" analysis giving the p-value< 2.2e-16 .
>> This result is
>> definitely wrong
>>
>> I then did the similar study by SAS.
>> The fullmodel is:
>> proc mixed;
>> class exposure;
>> model BMI=exposure;
>> random age /sub=id;
>> run;
>> The AIC is 7099.7, and type 3 test of fixed effect,
>> exposure, got
>> p-value=0.74.
>>
>> The reducemodel is:
>> proc mixed;
>> class exposure;
>> model BMI=;
>> random age /sub=id;
>> run;
>> The AIC is 7101.2.
>>
>> The SAS result is correct.
>>
>> Could somebody help me to explain why lmer is wrong?
>>
>> I do not even dare to use lmer now, since I can not trust
>> its result. Thanks
>> in advance for any of your answer.
>>
>> Best,
>> Yong
>> ,
>>
>> [[alternative HTML version deleted]]
>>
>>
>>
>> ------------------------------
>>
>> Message: 7
>> Date: Sat, 27 Mar 2010 23:55:54 -0500
>> From: hadley wickham <h.wickham at gmail.com>
>> To: Yong Wu <wuyong88 at gmail.com>
>> Cc: r-sig-mixed-models at r-project.org
>> Subject: Re: [R-sig-ME] Very weird lmer results, compared
>> to SAS proc
>> mix
>> Message-ID:
>> <f8e6ff051003272155l4501611dnebf8d57c8cfe9f5e at mail.gmail.com>
>> Content-Type: text/plain; charset=ISO-8859-1
>>
>> On Sat, Mar 27, 2010 at 11:25 PM, Yong Wu <wuyong88 at gmail.com>
>> wrote:
>> > Sorry to bother you. I am struggling in this issue for
>> long time. Wish
>> > somebody can help me.
>> >
>> > I first used lmer to do the following analysis.
>> > fullmodel=lmer(BMI~1+exposure+(age|ID),data,
>> REML=FALSE)
>> > ? ? ? ? ?reducemodel=lmer(BMI~1+(age|ID),data,
>> REML=FALSE)
>> > ? ? ? ? ?anova(full,red)
>> > The "fullmodel" has AIC of 6874 and "reducemodel" has
>> AIC of 7106, which
>> > cause "anova" analysis giving the p-value< 2.2e-16
>> . This result is
>> > definitely wrong
>>
>> How do you know? It would be helpful if you provided
>> the evidence you
>> used to judge SAS correct and R incorrect.
>>
>> Hadley
>>
>>
>> --
>> Assistant Professor / Dobelman Family Junior Chair
>> Department of Statistics / Rice University
>> http://had.co.nz/
>>
>>
>>
>> ------------------------------
>>
>> _______________________________________________
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>> R-sig-mixed-models at r-project.org
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>>
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>> **************************************************
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