Hi Thierry,
I have tausends of transactions of customers purchases. I guess it should
be fine.
Siham
Le 09.03.2015 ? 04:22, Thierry Onkelinx <thierry.onkelinx at inbo.be> a
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
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
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
~ 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 ...