[R-sig-ME] MCMCglmm
Iasonas Lamprianou
lamprianou at yahoo.com
Fri Apr 9 15:33:18 CEST 2010
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 Fri, 9/4/10, Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
> From: Jarrod Hadfield <j.hadfield at ed.ac.uk>
> Subject: Re: [R-sig-ME] MCMCglmm
> To: "Iasonas Lamprianou" <lamprianou at yahoo.com>
> Cc: r-sig-mixed-models at r-project.org
> Date: Friday, 9 April, 2010, 13:38
> Hi,
>
> In order to update the covariance matrix it is much easier
> if every
> combination of phase and marker exist. MCMCglmm generates
> any missing
> combinations and treats the unknown responses as missing
> data. It is
> just a computational strategy and the warning message can
> be ignored
> (I may suppress it in future versions).
>
> The error message is unrelated. I presume phase is a factor
> (?) with n
> levels. At some iteration the nXn covariance matrix
> associated with
> us(0+phase):marker becomes singular (or close to). If n=2
> the error
> message implies that a variance has hit zero, or a
> correlation has hit
> -1 and 1. If n>2 then this implies that one (or more)
> eigenvalues of
> the covariance matrix has hit zero. Numerical
> problems arise when
> these conditions occur so MCMCglmm terminates. In
> your analysis you
> have used the default flat priors, but if a proper prior is
> specified
> these conditions do not generally arise. You can choose
> from the
> standard inverse-Wishart prior or from the parameter
> expanded non-
> central F prior (see Gelman 2006 Bayesian Analysis 1
> 515-533, or the
> CourseNotes). The latter is particularly useful if the
> variances are
> close to zero because you can get dramatic improvements in
> mixing.
>
> Cheers,
>
> Jarrod
>
> On 9 Apr 2010, at 13:03, Iasonas Lamprianou wrote:
>
> > Dear Jarrod Hadfield
> > I followed your advice but got this message:
> >
> > m5_06.mcmc<- MCMCglmm(score ~ 1,
> random=~us(0+phase):marker+candidate
> > +batch, data=mg2006_sub)
> >
> > Warning in MCMCglmm(score ~ 1, random = ~us(0 +
> phase):marker +
> > candidate + :
> > some combinations in us(0 + phase):marker do not
> exist and 118
> > missing records have been generated
> > Error in MCMCglmm(score ~ 1, random = ~us(0 +
> phase):marker +
> > candidate + :
> > ill-conditioned G/R structure: use proper priors
> if you haven't or
> > rescale data if you have
> >
> >
> >
> > could you please help me?
> >
> > 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 Thu, 8/4/10, Jarrod Hadfield <j.hadfield at ed.ac.uk>
> wrote:
> >
> >> From: Jarrod Hadfield <j.hadfield at ed.ac.uk>
> >> Subject: Re: [R-sig-ME] MCMCglmm
> >> To: "Iasonas Lamprianou" <lamprianou at yahoo.com>
> >> Cc: r-sig-mixed-models at r-project.org
> >> Date: Thursday, 8 April, 2010, 15:10
> >> Dear Jason,
> >>
> >> These should work:
> >>
> >> m4.mcmc <- MCMCglmm(score ~ 1,
> >> random=~marker+marker:day+candidate
> >> +batch, data=mg2006)
> >> m5 .mcmc<- MCMCglmm(score ~ 1,
> >> random=~us(1+day):marker+candidate
> >> +batch, data=mg2006)
> >>
> >> You may need to code day as a factor for m4, and
> as numeric
> >> for m5
> >> depending on the model you actually want to fit.
> >>
> >> Cheers,
> >>
> >> Jarrod
> >>
> >>
> >> On 8 Apr 2010, at 14:58, Iasonas Lamprianou
> wrote:
> >>
> >>> Dear all,
> >>> I recently experimented with MCMCglmm and I
> loved
> >> (really loved) the
> >>> fact that it will give me confidence intervals
> for the
> >> variance of
> >>> the random effects. It seems that MCMC is a
> reasonable
> >> method to do
> >>> so, in contrast to REML which seems to have
> problems
> >> on this front.
> >>> However, MCMCglmm is painfully slower than
> lmer which
> >> is more
> >>> familiar to me. The good news is that the
> point
> >> estimates of lmer
> >>> are near the centre of the confidence
> intervals by
> >> MCMCglmm.
> >>>
> >>> I reduced my sample size a bit and managed to
> fit
> >> those two models
> >>> with lmer (the second would not fit because it
> needed
> >> 1.5GB or RAM).
> >>> Both seem to have a reasonable fit (at least
> at first
> >> look).
> >>>
> >>> m4 <- lmer(score ~
> >> 1+(1|marker/day)+(1|candidate)+(1|batch), mg2006)
> >>>
> >>> m5 <- lmer(score ~
> >> 1+(1+day|marker)+(1|candidate)+(1|batch), mg2006)
> >>>
> >>> I would like to run these two models above
> with
> >> MCMCglmm. Does
> >>> anyone know how to do it?
> >>>
> >>> Thank you for the help
> >>>
> >>> 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 Thu, 8/4/10, 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
> 40, Issue
> >> 15
> >>>> To: r-sig-mixed-models at r-project.org
> >>>> Date: Thursday, 8 April, 2010, 12:13
> >>>> 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
> >> Web, visit
> >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>> or, via email, send a message with subject
> or body
> >> 'help'
> >>>> to
> >>>> r-sig-mixed-models-request at r-project.org
> >>>>
> >>>> You can reach the person managing the list
> at
> >>>> r-sig-mixed-models-owner at r-project.org
> >>>>
> >>>> When replying, please edit your Subject
> line so it
> >> is more
> >>>> specific
> >>>> than "Re: Contents of R-sig-mixed-models
> >> digest..."
> >>>>
> >>>>
> >>>> Today's Topics:
> >>>>
> >>>> 1. Re: Multi-level
> models Odds ratio
> >> (E
> >>>> T)
> >>>> 2. Re: Multi-level
> models Odds ratio
> >>>> (ONKELINX, Thierry)
> >>>> 3. Re: Multi-level
> models Odds ratio
> >> (E
> >>>> T)
> >>>> 4. Re: Multi-level
> models Odds ratio
> >>>> (Andy Fugard (Work))
> >>>> 5. Re: Multi-level
> models Odds ratio
> >> (E
> >>>> T)
> >>>>
> >>>>
> >>>>
> >>
> ----------------------------------------------------------------------
> >>>>
> >>>> Message: 1
> >>>> Date: Thu, 8 Apr 2010 11:27:49 +0100
> >>>> From: E T <2nuzzbot at gmail.com>
> >>>> To: Daniel Ezra Johnson <danielezrajohnson at gmail.com>
> >>>> Cc: "r-sig-mixed-models at r-project.org"
> >>>> <r-sig-mixed-models at r-project.org>
> >>>> Subject: Re: [R-sig-ME] Multi-level models
> Odds
> >> ratio
> >>>> Message-ID:
> >>>> <l2w706f8d1f1004080327re8708f46mc7d334f41ae19a10 at mail.gmail.com
>
> >>>> >
> >>>> Content-Type: text/plain
> >>>>
> >>>> odds.ratios = exp(coefs(model))
> >>>>
> >>>> Thanks, however unfortunately when I try
> the above
> >> command
> >>>> I receive the
> >>>> following error:
> >>>>
> >>>> Error: could not find function "coefs"
> >>>>
> >>>> Regards
> >>>>
> >>>> Et
> >>>>
> >>>>
> >>>>
> >>>> On Wed, Apr 7, 2010 at 5:47 PM, Daniel
> Ezra
> >> Johnson <
> >>>> danielezrajohnson at gmail.com>
> >>>> wrote:
> >>>>
> >>>>> something like odds.ratios =
> >> exp(coefs(model))
> >>>>>
> >>>>>
> >>>>> On Apr 7, 2010, at 12:28 PM, E T
> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>>
> >>>>> Hi all,
> >>>>>>
> >>>>>> Apologies for the simplicity of
> my
> >> question....
> >>>> however any advice is
> >>>>>> greatly appreciated. Thanks
> >>>>>>
> >>>>>> Is there a specific command
> available to
> >> obtain
> >>>> the odds ratios produced
> >>>>>> from a multilevel logistic model?
> >>>>>>
> >>>>>> I have estimated a multi-level
> logistic
> >> model
> >>>> using the lme4 package. I
> >>>>>> can
> >>>>>> obtain results using the
> 'summary'
> >> command,
> >>>> however I would like to obtain
> >>>>>> the computed odds ratios.
> >>>>>> (Similar to the output that can
> be
> >> produced for
> >>>> logistic GLM using the
> >>>>>> logistic.display command from the
> epicalc
> >>>> package).
> >>>>>>
> >>>>>>
> [[alternative HTML version
> >>>> deleted]]
> >>>>>>
> >>>>>>
> >> _______________________________________________
> >>>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>>>
> >>>>>
> >>>>
> >>>> [[alternative HTML
> version
> >> deleted]]
> >>>>
> >>>>
> >>>>
> >>>> ------------------------------
> >>>>
> >>>> Message: 2
> >>>> Date: Thu, 8 Apr 2010 12:32:01 +0200
> >>>> From: "ONKELINX, Thierry" <Thierry.ONKELINX at inbo.be>
> >>>> To: "E T" <2nuzzbot at gmail.com>,
> >>>> "Daniel Ezra Johnson"
> >>>> <danielezrajohnson at gmail.com>
> >>>> Cc: r-sig-mixed-models at r-project.org
> >>>> Subject: Re: [R-sig-ME] Multi-level models
> Odds
> >> ratio
> >>>> Message-ID:
> >>>> <2E9C414912813E4EB981326983E0A104071B69A6 at inboexch.inbo.be>
> >>>> Content-Type: text/plain;
> >>>> charset="us-ascii"
> >>>>
> >>>> It should be
> >>>>
> >>>> exp(coef(model))
> >>>>
> >>>> Without the "s"
> >>>>
> >>>> HTH,
> >>>>
> >>>> Thierry
> >>>>
> >>
> ------------------------------------------------------------------------
> >>>> ----
> >>>> ir. Thierry Onkelinx
> >>>> Instituut voor natuur- en bosonderzoek
> >>>> team Biometrie & Kwaliteitszorg
> >>>> Gaverstraat 4
> >>>> 9500 Geraardsbergen
> >>>> Belgium
> >>>>
> >>>> Research Institute for Nature and Forest
> >>>> team Biometrics & Quality Assurance
> >>>> Gaverstraat 4
> >>>> 9500 Geraardsbergen
> >>>> Belgium
> >>>>
> >>>> tel. + 32 54/436 185
> >>>> Thierry.Onkelinx at inbo.be
> >>>> www.inbo.be
> >>>>
> >>>> To call in the statistician after the
> experiment
> >> is done
> >>>> may be no more
> >>>> than asking him to perform a post-mortem
> >> examination: he
> >>>> may be able to
> >>>> say what the experiment died of.
> >>>> ~ Sir Ronald Aylmer Fisher
> >>>>
> >>>> The plural of anecdote is not data.
> >>>> ~ Roger Brinner
> >>>>
> >>>> The combination of some data and an aching
> desire
> >> for an
> >>>> answer does not
> >>>> ensure that a reasonable answer can be
> extracted
> >> from a
> >>>> given body of
> >>>> data.
> >>>> ~ John Tukey
> >>>>
> >>>>
> >>>>> -----Oorspronkelijk bericht-----
> >>>>> Van: r-sig-mixed-models-bounces at r-project.org
> >>>>
> >>>>> [mailto:r-sig-mixed-models-bounces at r-project.org]
> >>>> Namens E T
> >>>>> Verzonden: donderdag 8 april 2010
> 12:28
> >>>>> Aan: Daniel Ezra Johnson
> >>>>> CC: r-sig-mixed-models at r-project.org
> >>>>> Onderwerp: Re: [R-sig-ME] Multi-level
> models
> >> Odds
> >>>> ratio
> >>>>>
> >>>>> odds.ratios = exp(coefs(model))
> >>>>>
> >>>>> Thanks, however unfortunately when I
> try the
> >> above
> >>>> command I
> >>>>> receive the following error:
> >>>>>
> >>>>> Error: could not find function
> "coefs"
> >>>>>
> >>>>> Regards
> >>>>>
> >>>>> Et
> >>>>>
> >>>>>
> >>>>>
> >>>>> On Wed, Apr 7, 2010 at 5:47 PM, Daniel
> Ezra
> >> Johnson
> >>>> <
> >>>>> danielezrajohnson at gmail.com>
> >>>> wrote:
> >>>>>
> >>>>>> something like odds.ratios =
> >> exp(coefs(model))
> >>>>>>
> >>>>>>
> >>>>>> On Apr 7, 2010, at 12:28 PM, E T
> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>>>
> >>>>>> Hi all,
> >>>>>>>
> >>>>>>> Apologies for the simplicity
> of my
> >>>> question.... however
> >>>>> any advice is
> >>>>>>> greatly appreciated. Thanks
> >>>>>>>
> >>>>>>> Is there a specific command
> available
> >> to
> >>>> obtain the odds ratios
> >>>>>>> produced from a multilevel
> logistic
> >> model?
> >>>>>>>
> >>>>>>> I have estimated a
> multi-level
> >> logistic model
> >>>> using the
> >>>>> lme4 package.
> >>>>>>> I can obtain results using
> the
> >> 'summary'
> >>>> command, however I would
> >>>>>>> like to obtain the computed
> odds
> >> ratios.
> >>>>>>> (Similar to the output that
> can be
> >> produced
> >>>> for logistic GLM using
> >>>>>>> the logistic.display command
> from the
> >> epicalc
> >>>> package).
> >>>>>>>
> >>>>>>>
> [[alternative HTML
> >> version
> >>>> deleted]]
> >>>>>>>
> >>>>>>>
> >>>>
> _______________________________________________
> >>>>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>> [[alternative
> HTML
> >> version
> >>>> deleted]]
> >>>>>
> >>>>>
> >> _______________________________________________
> >>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>>
> >>>>
> >>>> Druk dit bericht a.u.b. niet onnodig af.
> >>>> Please do not print this message
> unnecessarily.
> >>>>
> >>>> Dit bericht en eventuele bijlagen geven
> enkel de
> >> visie van
> >>>> de schrijver weer
> >>>> en binden het INBO onder geen enkel
> beding, zolang
> >> dit
> >>>> bericht niet bevestigd is
> >>>> door een geldig ondertekend document. The
> views
> >> expressed
> >>>> in this message
> >>>> and any annex are purely those of the
> writer and
> >> may not be
> >>>> regarded as stating
> >>>> an official position of INBO, as long as
> the
> >> message is not
> >>>> confirmed by a duly
> >>>> signed document.
> >>>>
> >>>>
> >>>>
> >>>> ------------------------------
> >>>>
> >>>> Message: 3
> >>>> Date: Thu, 8 Apr 2010 11:35:03 +0100
> >>>> From: E T <2nuzzbot at gmail.com>
> >>>> To: Daniel Ezra Johnson <danielezrajohnson at gmail.com>
> >>>> Cc: "r-sig-mixed-models at r-project.org"
> >>>> <r-sig-mixed-models at r-project.org>
> >>>> Subject: Re: [R-sig-ME] Multi-level models
> Odds
> >> ratio
> >>>> Message-ID:
> >>>> <i2y706f8d1f1004080335q84f61b78u8c7b656b67a08a8e at mail.gmail.com
>
> >>>> >
> >>>> Content-Type: text/plain
> >>>>
> >>>> If I use the command coef(model) this
> extracts
> >> the
> >>>> coefficients in the
> >>>> model, however if I try exp(coef(model)) I
> receive
> >> an
> >>>> error:
> >>>>
> >>>> Error in exp(coef(model)) : Non-numeric
> argument
> >> to
> >>>> mathematical function
> >>>>
> >>>> I could manually get the exp of each
> factor in my
> >>>> model..... but as I have a
> >>>> large model (and also have numerous other
> models
> >> to
> >>>> produce), I was
> >>>> wondering if there was an automated
> method
> >>>>
> >>>> Regards
> >>>>
> >>>> Et
> >>>>
> >>>> On Thu, Apr 8, 2010 at 11:27 AM, E T
> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>
> >>>>> odds.ratios = exp(coefs(model))
> >>>>>
> >>>>> Thanks, however unfortunately when I
> try the
> >> above
> >>>> command I receive the
> >>>>> following error:
> >>>>>
> >>>>> Error: could not find function
> "coefs"
> >>>>>
> >>>>> Regards
> >>>>>
> >>>>> Et
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>> On Wed, Apr 7, 2010 at 5:47 PM, Daniel
> Ezra
> >> Johnson
> >>>> <
> >>>>> danielezrajohnson at gmail.com>
> >>>> wrote:
> >>>>>
> >>>>>> something like odds.ratios =
> >> exp(coefs(model))
> >>>>>>
> >>>>>>
> >>>>>> On Apr 7, 2010, at 12:28 PM, E T
> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>>>
> >>>>>> Hi all,
> >>>>>>>
> >>>>>>> Apologies for the simplicity
> of my
> >>>> question.... however any advice is
> >>>>>>> greatly appreciated. Thanks
> >>>>>>>
> >>>>>>> Is there a specific command
> available
> >> to
> >>>> obtain the odds ratios produced
> >>>>>>> from a multilevel logistic
> model?
> >>>>>>>
> >>>>>>> I have estimated a
> multi-level
> >> logistic model
> >>>> using the lme4 package. I
> >>>>>>> can
> >>>>>>> obtain results using the
> 'summary'
> >> command,
> >>>> however I would like to
> >>>>>>> obtain
> >>>>>>> the computed odds ratios.
> >>>>>>> (Similar to the output that
> can be
> >> produced
> >>>> for logistic GLM using the
> >>>>>>> logistic.display command from
> the
> >> epicalc
> >>>> package).
> >>>>>>>
> >>>>>>>
> [[alternative HTML
> >> version
> >>>> deleted]]
> >>>>>>>
> >>>>>>>
> >>>>
> _______________________________________________
> >>>>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>> [[alternative HTML
> version
> >> deleted]]
> >>>>
> >>>>
> >>>>
> >>>> ------------------------------
> >>>>
> >>>> Message: 4
> >>>> Date: Thu, 08 Apr 2010 12:48:30 +0200
> >>>> From: "Andy Fugard (Work)" <andy.fugard at sbg.ac.at>
> >>>> To: E T <2nuzzbot at gmail.com>
> >>>> Cc: "r-sig-mixed-models at r-project.org"
> >>>> <r-sig-mixed-models at r-project.org>
> >>>> Subject: Re: [R-sig-ME] Multi-level models
> Odds
> >> ratio
> >>>> Message-ID: <4BBDB47E.8030305 at sbg.ac.at>
> >>>> Content-Type: text/plain;
> charset=ISO-8859-1
> >>>>
> >>>> Here's another example, borrowed from the
> help for
> >> "lmer":
> >>>>
> >>>>> gm1 <- glmer(cbind(incidence, size
> -
> >> incidence) ~
> >>>> period + (1 | herd),
> >>>>
> >> family =
> >>>> binomial, data = cbpp)
> >>>>
> >>>> As you say, coef works:
> >>>>
> >>>>> coef(gm1)
> >>>> $herd
> >>>> (Intercept)
> >>>>
> >>
> period2 period3 period4
> >>>> 1 -0.8085096 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 2 -1.6974292 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 3 -0.9922697 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 4 -1.3592525 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 5 -1.5885461 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 6 -1.7987950 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 7 -0.5091313 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 8 -0.7991613 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 9 -1.6361848 -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>> 10 -1.9394614 -0.9923347 -1.128675
> >> -1.580374
> >>>> 11 -1.4831632 -0.9923347 -1.128675
> >> -1.580374
> >>>> 12 -1.4633469 -0.9923347 -1.128675
> >> -1.580374
> >>>> 13 -2.0884474 -0.9923347 -1.128675
> >> -1.580374
> >>>> 14 -0.4278151 -0.9923347 -1.128675
> >> -1.580374
> >>>> 15 -1.9290041 -0.9923347 -1.128675
> >> -1.580374
> >>>>
> >>>> But note the "$herd" bit. Since this
> model
> >> has a
> >>>> varying intercept by
> >>>> herd, you get a column in the resulting
> data frame
> >> called
> >>>> "herd".
> >>>>
> >>>> So you could try, for this example:
> >>>>
> >>>>> exp(coef(gm1)$herd)
> >>>>
> >>
> (Intercept) period2 period3 period4
> >>>> 1 0.4455216 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 2 0.1831538 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 3 0.3707343 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 4 0.2568527 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 5 0.2042223 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 6 0.1654982 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 7 0.6010174 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 8 0.4497060 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 9 0.1947215 0.3707102
> 0.3234614
> >> 0.2058981
> >>>> 10 0.1437814 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>> 11 0.2269188 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>> 12 0.2314603 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>> 13 0.1238793 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>> 14 0.6519320 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>> 15 0.1452928 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>
> >>>> Since the slopes don't vary by herd, you
> might
> >> also want
> >>>> just the fixed
> >>>> effects:
> >>>>
> >>>>> exp(fixef(gm1))
> >>>> (Intercept)
> period2
> >>>> period3
> >> period4
> >>>>
> >>>>
> >>
> 0.2469585 0.3707102 0.3234614 0.2058981
> >>>>
> >>>> HTH,
> >>>>
> >>>> Andy
> >>>>
> >>>>
> >>>> E T wrote:
> >>>>> If I use the command coef(model) this
> extracts
> >> the
> >>>> coefficients in the
> >>>>> model, however if I try
> exp(coef(model)) I
> >> receive an
> >>>> error:
> >>>>>
> >>>>> Error in exp(coef(model)) :
> Non-numeric
> >> argument to
> >>>> mathematical function
> >>>>>
> >>>>> I could manually get the exp of each
> factor in
> >> my
> >>>> model..... but as I have a
> >>>>> large model (and also have numerous
> other
> >> models to
> >>>> produce), I was
> >>>>> wondering if there was an automated
> method
> >>>>>
> >>>>> Regards
> >>>>>
> >>>>> Et
> >>>>>
> >>>>> On Thu, Apr 8, 2010 at 11:27 AM, E T
> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>>
> >>>>>> odds.ratios = exp(coefs(model))
> >>>>>>
> >>>>>> Thanks, however unfortunately when
> I try
> >> the above
> >>>> command I receive the
> >>>>>> following error:
> >>>>>>
> >>>>>> Error: could not find function
> "coefs"
> >>>>>>
> >>>>>> Regards
> >>>>>>
> >>>>>> Et
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>> On Wed, Apr 7, 2010 at 5:47 PM,
> Daniel
> >> Ezra
> >>>> Johnson <
> >>>>>> danielezrajohnson at gmail.com>
> >>>> wrote:
> >>>>>>
> >>>>>>> something like odds.ratios =
> >>>> exp(coefs(model))
> >>>>>>>
> >>>>>>>
> >>>>>>> On Apr 7, 2010, at 12:28 PM, E
> T
> >> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>>>>
> >>>>>>> Hi all,
> >>>>>>>> Apologies for the
> simplicity of
> >> my
> >>>> question.... however any advice is
> >>>>>>>> greatly appreciated.
> Thanks
> >>>>>>>>
> >>>>>>>> Is there a specific
> command
> >> available to
> >>>> obtain the odds ratios produced
> >>>>>>>> from a multilevel logistic
> model?
> >>>>>>>>
> >>>>>>>> I have estimated a
> multi-level
> >> logistic
> >>>> model using the lme4 package. I
> >>>>>>>> can
> >>>>>>>> obtain results using the
> >> 'summary'
> >>>> command, however I would like to
> >>>>>>>> obtain
> >>>>>>>> the computed odds ratios.
> >>>>>>>> (Similar to the output
> that can
> >> be
> >>>> produced for logistic GLM using the
> >>>>>>>> logistic.display command
> from the
> >> epicalc
> >>>> package).
> >>>>>>>>
> >>>>>>>>
> [[alternative HTML
> >>>> version deleted]]
> >>>>>>>>
> >>>>>>>>
> >>>>
> _______________________________________________
> >>>>>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>>>>>
> >>>>>
> >>>>> [[alternative
> HTML
> >> version
> >>>> deleted]]
> >>>>>
> >>>>>
> >> _______________________________________________
> >>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>
> >>>>
> >>>> --
> >>>> Andy Fugard, Postdoctoral researcher, ESF
> LogICCC
> >> project
> >>>> "Modeling human inference within the
> framework of
> >>>> probability logic"
> >>>> Department of Psychology, University of
> Salzburg,
> >> Austria
> >>>> http://www.andyfugard.info
> >>>>
> >>>>
> >>>>
> >>>> ------------------------------
> >>>>
> >>>> Message: 5
> >>>> Date: Thu, 8 Apr 2010 12:13:23 +0100
> >>>> From: E T <2nuzzbot at gmail.com>
> >>>> To: "Andy Fugard (Work)" <andy.fugard at sbg.ac.at>
> >>>> Cc: "r-sig-mixed-models at r-project.org"
> >>>> <r-sig-mixed-models at r-project.org>
> >>>> Subject: Re: [R-sig-ME] Multi-level models
> Odds
> >> ratio
> >>>> Message-ID:
> >>>> <p2p706f8d1f1004080413k3014fe10q444382c927c2f90e at mail.gmail.com
>
> >>>> >
> >>>> Content-Type: text/plain
> >>>>
> >>>> exp(coef(model)$group)
> >>>>
> >>>> exp(fixef(model))
> >>>>
> >>>> Thanks.... yes this worked successfully
> :o)
> >>>>
> >>>> Et
> >>>>
> >>>> On Thu, Apr 8, 2010 at 11:48 AM, Andy
> Fugard
> >> (Work)
> >>>> <andy.fugard at sbg.ac.at>wrote:
> >>>>
> >>>>> Here's another example, borrowed from
> the help
> >> for
> >>>> "lmer":
> >>>>>
> >>>>>> gm1 <- glmer(cbind(incidence,
> size -
> >>>> incidence) ~ period + (1 | herd),
> >>>>>
> >> family
> >>>> = binomial, data = cbpp)
> >>>>>
> >>>>> As you say, coef works:
> >>>>>
> >>>>>> coef(gm1)
> >>>>> $herd
> >>>>> (Intercept)
> >>>>
> >>
> period2 period3 period4
> >>>>> 1 -0.8085096
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 2 -1.6974292
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 3 -0.9922697
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 4 -1.3592525
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 5 -1.5885461
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 6 -1.7987950
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 7 -0.5091313
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 8 -0.7991613
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 9 -1.6361848
> -0.9923347
> >> -1.128675
> >>>> -1.580374
> >>>>> 10 -1.9394614 -0.9923347
> -1.128675
> >> -1.580374
> >>>>> 11 -1.4831632 -0.9923347
> -1.128675
> >> -1.580374
> >>>>> 12 -1.4633469 -0.9923347
> -1.128675
> >> -1.580374
> >>>>> 13 -2.0884474 -0.9923347
> -1.128675
> >> -1.580374
> >>>>> 14 -0.4278151 -0.9923347
> -1.128675
> >> -1.580374
> >>>>> 15 -1.9290041 -0.9923347
> -1.128675
> >> -1.580374
> >>>>>
> >>>>> But note the "$herd" bit. Since
> this
> >> model has a
> >>>> varying intercept by
> >>>>> herd, you get a column in the
> resulting data
> >> frame
> >>>> called "herd".
> >>>>>
> >>>>> So you could try, for this example:
> >>>>>
> >>>>>> exp(coef(gm1)$herd)
> >>>>>
> >>
> (Intercept) period2 period3 period4
> >>>>> 1 0.4455216 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 2 0.1831538 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 3 0.3707343 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 4 0.2568527 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 5 0.2042223 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 6 0.1654982 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 7 0.6010174 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 8 0.4497060 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 9 0.1947215 0.3707102
> 0.3234614
> >>>> 0.2058981
> >>>>> 10 0.1437814
> 0.3707102
> >> 0.3234614
> >>>> 0.2058981
> >>>>> 11 0.2269188
> 0.3707102
> >> 0.3234614
> >>>> 0.2058981
> >>>>> 12 0.2314603
> 0.3707102
> >> 0.3234614
> >>>> 0.2058981
> >>>>> 13 0.1238793
> 0.3707102
> >> 0.3234614
> >>>> 0.2058981
> >>>>> 14 0.6519320
> 0.3707102
> >> 0.3234614
> >>>> 0.2058981
> >>>>> 15 0.1452928
> 0.3707102
> >> 0.3234614
> >>>> 0.2058981
> >>>>>
> >>>>> Since the slopes don't vary by herd,
> you might
> >> also
> >>>> want just the fixed
> >>>>> effects:
> >>>>>
> >>>>>> exp(fixef(gm1))
> >>>>> (Intercept)
> period2
> >>>> period3
> >> period4
> >>>>>
> >>>>
> >>
> 0.2469585 0.3707102 0.3234614 0.2058981
> >>>>>
> >>>>> HTH,
> >>>>>
> >>>>> Andy
> >>>>>
> >>>>>
> >>>>> E T wrote:
> >>>>>> If I use the command coef(model)
> this
> >> extracts
> >>>> the coefficients in the
> >>>>>> model, however if I try
> exp(coef(model))
> >> I
> >>>> receive an error:
> >>>>>>
> >>>>>> Error in exp(coef(model)) :
> Non-numeric
> >> argument
> >>>> to mathematical function
> >>>>>>
> >>>>>> I could manually get the exp of
> each
> >> factor in my
> >>>> model..... but as I
> >>>>> have a
> >>>>>> large model (and also have
> numerous other
> >> models
> >>>> to produce), I was
> >>>>>> wondering if there was an
> automated
> >> method
> >>>>>>
> >>>>>> Regards
> >>>>>>
> >>>>>> Et
> >>>>>>
> >>>>>> On Thu, Apr 8, 2010 at 11:27 AM, E
> T
> >> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>>>
> >>>>>>> odds.ratios =
> exp(coefs(model))
> >>>>>>>
> >>>>>>> Thanks, however unfortunately
> when I
> >> try the
> >>>> above command I receive the
> >>>>>>> following error:
> >>>>>>>
> >>>>>>> Error: could not find
> function
> >> "coefs"
> >>>>>>>
> >>>>>>> Regards
> >>>>>>>
> >>>>>>> Et
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>> On Wed, Apr 7, 2010 at 5:47
> PM, Daniel
> >> Ezra
> >>>> Johnson <
> >>>>>>> danielezrajohnson at gmail.com>
> >>>> wrote:
> >>>>>>>
> >>>>>>>> something like odds.ratios
> =
> >>>> exp(coefs(model))
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On Apr 7, 2010, at 12:28
> PM, E T
> >> <2nuzzbot at gmail.com>
> >>>> wrote:
> >>>>>>>>
> >>>>>>>> Hi all,
> >>>>>>>>> Apologies for the
> simplicity
> >> of my
> >>>> question.... however any advice is
> >>>>>>>>> greatly appreciated.
> Thanks
> >>>>>>>>>
> >>>>>>>>> Is there a specific
> command
> >> available
> >>>> to obtain the odds ratios
> >>>>> produced
> >>>>>>>>> from a multilevel
> logistic
> >> model?
> >>>>>>>>>
> >>>>>>>>> I have estimated a
> >> multi-level
> >>>> logistic model using the lme4 package.
> >>>>> I
> >>>>>>>>> can
> >>>>>>>>> obtain results using
> the
> >> 'summary'
> >>>> command, however I would like to
> >>>>>>>>> obtain
> >>>>>>>>> the computed odds
> ratios.
> >>>>>>>>> (Similar to the output
> that
> >> can be
> >>>> produced for logistic GLM using the
> >>>>>>>>> logistic.display
> command from
> >> the
> >>>> epicalc package).
> >>>>>>>>>
> >>>>>>>>>
> [[alternative
> >> HTML
> >>>> version deleted]]
> >>>>>>>>>
> >>>>>>>>>
> >>>>
> _______________________________________________
> >>>>>>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>>>>>>
> >>>>>>
> >>>>>>
> [[alternative
> >> HTML
> >>>> version deleted]]
> >>>>>>
> >>>>>>
> >> _______________________________________________
> >>>>>> R-sig-mixed-models at r-project.org
> >>>> mailing list
> >>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>>>
> >>>>>
> >>>>> --
> >>>>> Andy Fugard, Postdoctoral researcher,
> ESF
> >> LogICCC
> >>>> project
> >>>>> "Modeling human inference within the
> framework
> >> of
> >>>> probability logic"
> >>>>> Department of Psychology, University
> of
> >> Salzburg,
> >>>> Austria
> >>>>> http://www.andyfugard.info
> >>>>>
> >>>>
> >>>> [[alternative HTML
> version
> >> deleted]]
> >>>>
> >>>>
> >>>>
> >>>> ------------------------------
> >>>>
> >>>>
> _______________________________________________
> >>>> 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 40,
> Issue
> >> 15
> >>>>
> >>
> **************************************************
> >>>>
> >>>
> >>>
> >>>
> >>>
> >>>
> _______________________________________________
> >>> R-sig-mixed-models at r-project.org
> >> mailing list
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>>
> >>
> >>
> >> --
> >> The University of Edinburgh is a charitable body,
> >> registered in
> >> Scotland, with registration number SC005336.
> >>
> >>
> >
> >
> >
> >
>
>
> --
> The University of Edinburgh is a charitable body,
> registered in
> Scotland, with registration number SC005336.
>
>
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