[R-sig-ME] Question about continuous distributions in GLMM

[Victoria Ortiz]

I write to ask a simple question about quantitative continuous variables
distributions. We have data for morphological traits in insects but they do
not fit any distribution in GLMM. The design has two fixed variables and a
random one. We are interested in the variance components of the random
variable and its interactions. We tried normal (lm4), gamma (glmer),
lognormal (GLMMPQL), tweedie (GLMMTMB) and compound poison (CPLM). There is
no good fit for any case. In fact, the better model using AIC is normal. The
residuals vs. predicted graphic and the Q-Q plot have the following
form: *https://github.com/vicrotas/Repositorio-de-Vicka/issues/1
<https://github.com/vicrotas/Repositorio-de-Vicka/issues/1>*



Given that the fit to normal distribution is not good, we want to know if
there is any other distribution we could try. What else we can do in this
scenario?



On the other hand, to estimate the variance components we used the
following in lmer:



m1 <- lmer ( variable ~ fixed factor  1 * fixed factor 2 + (fixed factor 1
* fixed factor 2 | | random factor))



The specific question is if the double bar ('| |') is a good way to
estimate the variance components or if there is another way to do it?



Thanks in advance!

	[[alternative HTML version deleted]]