[R-sig-ME] MCMCglmm
Jarrod Hadfield
j.hadfield at ed.ac.uk
Thu Apr 8 16:10:21 CEST 2010
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
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>>
>> 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]]
>>
>>
>>
>> ------------------------------
>>
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>> 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
>> **************************************************
>>
>
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