[R-sig-ME] R-sig-mixed-models Digest, Vol 66, Issue 11
Malcolm Fairbrother
m.fairbrother at bristol.ac.uk
Thu Jun 7 13:49:17 CEST 2012
Dear Teresa,
Would the "ordinal" package have what you're looking for?
http://cran.r-project.org/web/packages/ordinal/
Un saludo,
Malcolm
> Date: Thu, 07 Jun 2012 10:31:40 +0200
> From: "MORAN LOPEZ, TERESA" <tmoranlopez at mncn.csic.es>
> To: Chris Howden <chris at trickysolutions.com.au>
> Cc: "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>
> Subject: Re: [R-sig-ME] Running multinomial models with random effects
> Message-ID: <20120607103140.16232bmjomxwcx64 at webmail.csic.es>
> Content-Type: text/plain
>
> Thanks a lot Chris,
> I will try that one and see what happens. However, if someone knows how to run multinomials with random effects I will be very greatful!
>
> Quoting Chris Howden:
>
>> I'm not neccsarily advocating this. But another way is to use 3
>> logistic models with random effects. One for wether they choose big
>> acorn, one for small, one for both. Then compare the parameters of all
>> 3 to see if there are any differences.
>>
>> Chris Howden
>> Founding Partner
>> Tricky Solutions
>> Tricky Solutions 4 Tricky Problems
>> Evidence Based Strategic Development, IP Commercialisation and
>> Innovation, Data Analysis, Modelling and Training
>>
>> (mobile) 0410 689 945
>> (fax / office)
>> chris at trickysolutions.com.au
>>
>> Disclaimer: The information in this email and any attachments to it are
>> confidential and may contain legally privileged information. If you are not
>> the named or intended recipient, please delete this communication and
>> contact us immediately. Please note you are not authorised to copy,
>> use or disclose this communication or any attachments without our
>> consent. Although this email has been checked by anti-virus software,
>> there is a risk that email messages may be corrupted or infected by
>> viruses or other
>> interferences. No responsibility is accepted for such interference. Unless
>> expressly stated, the views of the writer are not those of the
>> company. Tricky Solutions always does our best to provide accurate
>> forecasts and analyses based on the data supplied, however it is
>> possible that some important predictors were not included in the data
>> sent to us. Information provided by us should not be solely relied
>> upon when making decisions and clients should use their own judgement.
>>
>> On 07/06/2012, at 3:00, "MORAN LOPEZ, TERESA"
>> <tmoranlopez at mncn.csic.es> wrote:
>>
>>> Dear all,
>>> I am a phD student working on animal behavior under different
>>> predation risk. We have conducted an experiment in which acorn
>>> selection by jays was evaluated in savanna vs forest-type
>>> landscapes. We placed 8 feeders in two different forests and 8
>>> feeders in two different savannas.
>>> My response variable is factorial, acorn choice (big, small, both).
>>> In my design I have spatial pseudorreplication within Areas. I have
>>> not found multinom function which allows fitting random effects.
>>> However, MCMCglmm package allows incorporating them using Bayesian
>>> Methods. I am new using Bayesian Methods so it has taken me a lot of
>>> effort to partially understand these models implementation. However,
>>> once I have run my model I am not sure about output interpretation
>>> and most importantly how to convert fix effect parameters into
>>> probabilities.
>>>
>>> I have tried three different things: to convert my data in binomial
>>> (ignoring infrequent acorn choice “both”) and run mixed model lmer.
>>> Pros: I can include random effects , Cons: I lose an infrequent but
>>> informative level of the response.
>>>
>>> Keep categorical responses and run multinom function without random
>>> effects. Pros: I can fit my response variable as categorical and
>>> keep all levels. Cons: I am ignoring spatial autocorrelation
>>> (however, when running binomial models acorn choice variation
>>> between areas was much more lower than between risk levels).
>>> +
>>> Run MCMCglmm model with random effects. Pros: I can keep both
>>> spatial autocorrelation and all levels of the response factor. Cons:
>>> Not sure about model implementation and interpretation. Low sample
>>> size.
>>>
>>>
>>> Which option is the most accurate?
>>>
>>> Thanks a lot! I am really stucked with my data.
>>>
>>> Here I post my MCMCglmm model which is the one I have more doubts
>>>
>>> #head(choice)-dataset with choice as factor (b=big, s=small, bt=both)
>>> RISK Area CHOICE
>>> LOW Sopie b
>>> LOW Sopie b
>>> LOW Sopie b
>>> LOW Anchurones s
>>> LOW Anchurones bt
>>> LOW Anchurones b
>>>
>>> I have read package tutorial and Hadfield notes (though I have not
>>> read all the chapter of Hadfield course notes). Besides I have used
>>> information posted Jaeger lab blog:
>>> http://hlplab.wordpress.com/2009/05/07/multinomial-random-effects-models-in-r/
>>>
>>> I am not very sure about random effects priors but I believe the
>>> rest of them are ok.
>>> k <- length(levels(choice$CHOICE))
>>> I<- diag(k-1)
>>> J <- matrix(rep(1, (k-1)^2), c(k-1, k-1))
>>> #Constraints to variance-covariance matrix
>>>
>>> prior = list(R = list(fix=1, V =(1/k) * (I + J), n=k), G = list(G1 =
>>> list(V =diag(2), n=2)))
>>>
>>> #R priors, Hadfield manual suggests that structure. Are they right?
>>> NOT SURE ABOUT RANDOM PRIORS.
>>>
>>> M<- MCMCglmm(CHOICE~ -1+ trait + RISK,
>>> random = ~ us(trait):Area,
>>> rcov = ~ us(trait):units,
>>> prior = prior,
>>> family = "categorical",
>>> data = choice)
>>>
>>> #-1+trait following Hadfield suggestions in order to estimate
>>> interception if every outcome.
>>> us(trait):----- since we are not sure of independence assumptions.
>>> Is model function allright?
>>>
>>> My model runs but I find it very difficult to interpret my summary
>>>
>>> summary(M)
>>>
>>> Iterations = 3001:12991
>>> Thinning interval = 10
>>> Sample size = 1000
>>>
>>> DIC: 44.21802
>>>
>>> G-structure: ~us(trait):Area
>>>
>>> post.mean l-95% CI u-95% CI eff.samp
>>> CHOICE.bt:CHOICE.bt.Area 14.622 0.1881 64.46 7.278
>>> CHOICE.s:CHOICE.bt.Area 3.274 -11.1431 19.29 116.593
>>> CHOICE.bt:CHOICE.s.Area 3.274 -11.1431 19.29 116.593
>>> CHOICE.s:CHOICE.s.Area 5.071 0.1416 15.82 127.395
>>>
>>> R-structure: ~us(trait):units
>>>
>>> post.mean l-95% CI u-95% CI eff.samp
>>> CHOICE.bt:CHOICE.bt.units 0.6667 0.6667 0.6667 0
>>> CHOICE.s:CHOICE.bt.units 0.3333 0.3333 0.3333 0
>>> CHOICE.bt:CHOICE.s.units 0.3333 0.3333 0.3333 0
>>> CHOICE.s:CHOICE.s.units 0.6667 0.6667 0.6667 0
>>>
>>> Location effects: CHOICE ~ -1 + trait + RISK
>>>
>>> post.mean l-95% CI u-95% CI eff.samp pMCMC
>>> traitCHOICE.bt -4.5042 -11.8926 0.3824 15.18 0.052 .
>>> traitCHOICE.s -2.0187 -6.3370 1.3569 84.55 0.200
>>> RISKRAÑA 1.9095 -2.3541 8.2421 56.13 0.336
>>> ---
>>> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>>
>>> posterior.mode(M$Sol)
>>>
>>> traitCHOICE.bt traitCHOICE.s RISKRAÑA
>>> -2.594132 -1.836527 1.554387
>>>
>>> #Since there is not any intercept I can´t apply plogis() in order to
>>> get probabilities of choice in different areas.
>>>
>>>
>>> Sorry that I have that many questions and thanks a lot!!!
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
More information about the R-sig-mixed-models
mailing list