Hi, I'm so sorry for the delay in the response, I was with a lot of work. With "variance components" I mean the partition of the total variance into the different factors that explain it. Our interest is to have a quantification of the portion of the variance explained by the different factors, both random and fixed. Translated to the biology of our data, this means to estimate genetic, genotype x environment variation, and environment variation of the total phenotypic variation for a given trait in a population. In particular, the objective is to compare this estimators between diferent populations analyzed separately. Additionaly, reading another topics of this mail list, I found that the classical model for testing the interaction and obtain the variance components would be a model like the following: m2 <- lmer ( variable ~ fixed factor 1 * fixed factor 2 + (1 | random factor) + (1 | fixed factor 1:random factor2) + (1 | fixed factor 2:random factor) + (1| fixed factor 1:fixed factor 2:random factor)) So, with this model, in the summary I can see the partition of the total variance of the random effects. Is this right? Finally, if I want the p-values of the random effects, I should analize the full and reduce models sequentially. Also, I found that another way to do it is with the 'ranova' function from the lmerTest package, but the results are very dissimilar. I don't know in wich analysis should I trust, I think that in this case the sequentially one is correct. Thank you for your time! Victoria. 2018-04-27 17:08 GMT-03:00 Ben Bolker <bbolker at gmail.com>:
On 2018-04-26 07:45 PM, Victoria Ortiz wrote:
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>*
I'm not quite sure what to suggest about the distribution. Since this looks left-skewed, you might try a power transformation with g > 1 (e.g. x^1.5) to shift it. (That would be applied to the data rather than the residuals, so might not work perfectly ...) For a rough idea, you could run a Box-Cox analysis on the residuals. Alternatively, if you can figure out a permutation approach that works (e.g. permutation within and between groups) that could give you a distribution-robust way to get a p-value.
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?
Can you clarify what you mean by "variance components"? Are you explicitly trying to partition variance, or are you just trying to make sure that you control for among-group variation? If your data will support it, I think it would be better to fit the unstructured variance-covariance matrix; if not, you could try one of the Bayesian methods (blme, MCMCglmm, brms, rstanarm ...) that would allow you to regularize/put a prior on the variance-covariance matrix.
Thanks in advance!
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