[R-sig-ME] fixed vs random
Iasonas Lamprianou
lamprianou at yahoo.com
Mon Mar 29 07:49:16 CEST 2010
Thank you, very kind of you to respond
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, Daniel Ezra Johnson <danielezrajohnson at gmail.com> wrote:
> From: Daniel Ezra Johnson <danielezrajohnson at gmail.com>
> Subject: Re: [R-sig-ME] fixed vs random
> To: "Iasonas Lamprianou" <lamprianou at yahoo.com>
> Cc: r-sig-mixed-models at r-project.org
> Date: Sunday, 28 March, 2010, 22:12
> 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
> >> submissions to
> >> r-sig-mixed-models at r-project.org
> >>
> >> To subscribe or unsubscribe via the World Wide
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> is more
> >> specific
> >> than "Re: Contents of R-sig-mixed-models
> digest..."
> >>
> >>
> >> 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/
> >>
> >>
> >>
> >> ------------------------------
> >>
> >> _______________________________________________
> >> R-sig-mixed-models mailing list
> >> R-sig-mixed-models at r-project.org
> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>
> >>
> >> End of R-sig-mixed-models Digest, Vol 39, Issue
> 42
> >>
> **************************************************
> >>
> >
> >
> >
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org
> mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
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