Dear lmer() friends, I am trying to estimate a model with a random intercept, and 2 random slopes. I believe that my betas (slopes) do not follow a normal distribution, but rather a bimodal distribution. The reason for this that there are two possible mechanisms that influence the evolution of this variable, one with a negative influence and another one with a positive influence. This is why I need to use a bimodal distribution for my slopes to avoid the fact that both effects right now cancel out. Does anyone of you has already done this or has an idea how to concretely implement this using lmer()? Thanks a lot for your support, this will help me a lot! Many regards, Siham
lmer with binomial distribution of random effects
4 messages · El Kihal, Siham, Ben Bolker, Thierry Onkelinx +1 more
2 days later
El Kihal, Siham <Siham.ElKihal at ...> writes:
Dear lmer() friends, I am trying to estimate a model with a random intercept, and 2 random slopes. I believe that my betas (slopes) do not follow a normal distribution, but rather a bimodal distribution. The reason for this that there are two possible mechanisms that influence the evolution of this variable, one with a negative influence and another one with a positive influence. This is why I need to use a bimodal distribution for my slopes to avoid the fact that both effects right now cancel out. Does anyone of you has already done this or has an idea how to concretely implement this using lmer()?
This sounds like a latent mixture model problem. lme4 doesn't do this; you *might* be able to implement an expectation-maximization wrapper around lme4 that would do it, but it wouldn't be entirely trivial. If I had to do this I would probably turn to JAGS/BUGS. Looking forward to other answers from the list ...
Dear Siham, I would take a step back first. Do you have enough data to fit such a complex model? Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium 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 2015-03-09 2:15 GMT+01:00 Ben Bolker <bbolker at gmail.com>:
El Kihal, Siham <Siham.ElKihal at ...> writes:
Dear lmer() friends, I am trying to estimate a model with a random intercept, and 2 random slopes. I believe that my betas (slopes) do not follow a normal distribution, but rather a bimodal distribution. The reason for this that there are two possible mechanisms that influence the evolution of this variable, one with a negative influence and another one with a positive influence. This is why I need to use a bimodal distribution for my slopes to avoid the fact that both effects right now cancel out. Does anyone of you has already done this or has an idea how to concretely implement this using lmer()?
This sounds like a latent mixture model problem. lme4 doesn't do this; you *might* be able to implement an expectation-maximization wrapper around lme4 that would do it, but it wouldn't be entirely trivial. If I had to do this I would probably turn to JAGS/BUGS. Looking forward to other answers from the list ...
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1 day later
The lcmm package looks promising: http://finzi.psych.upenn.edu/R/library/lcmm/html/lcmm-package.html And it seems there may be relevant stuff in DPpackage: http://finzi.psych.upenn.edu/R/library/DPpackage/html/00Index.html Best, Wolfgang
-----Original Message----- From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r- project.org] On Behalf Of Ben Bolker Sent: Monday, March 09, 2015 02:16 To: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] lmer with binomial distribution of random effects El Kihal, Siham <Siham.ElKihal at ...> writes:
Dear lmer() friends, I am trying to estimate a model with a random intercept, and 2 random slopes. I believe that my betas (slopes) do not follow a normal distribution, but rather a bimodal distribution. The reason for this that there are two possible mechanisms that influence the evolution of this variable, one with a negative influence and another one with a positive influence. This is why I need to use a bimodal distribution for my slopes to avoid the fact that both effects right now cancel out. Does anyone of you has already done this or has an idea how to concretely implement this using lmer()?
This sounds like a latent mixture model problem. lme4 doesn't do this; you *might* be able to implement an expectation-maximization wrapper around lme4 that would do it, but it wouldn't be entirely trivial. If I had to do this I would probably turn to JAGS/BUGS. Looking forward to other answers from the list ...
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