I have a bimodal length-frequency distribution for the females of a species with a one-year life span. This pattern is not observed in the males.
I suspect that the bimodality is due to different hatching times and the associated environmental conditions. I would like to separate these distributions and see if the environmental variables can explain the variability of the population.
From reading this question, I thought that I can do this using finite mixture modelling, more specifically the mixtools package in R and the normalmixEM function.
Sample of my data:
Year Period Sex Length.int 1 2000 E F 28 2 2000 E F 26 3 2000 E F 21 4 2000 E F 25 5 2000 E F 23 6 2000 E F 24
The output of the model:
summary of normalmixEM object: comp 1 comp 2 lambda 0.553918 0.446082 mu 24.039199 29.286508 sigma 1.515261 2.027977 loglik at estimate: -1511.065
My question is, how do I use the parameters from the output to actually split the bimodal distribution into two unimodal distributions?
Is this what I should be doing in the first place? Is it statistically sound? Some of the comments here lead me to think that it is not necessary to be performing this splitting, especially since I will most likely be using a regression model. However, I have data from the last twenty years, and I want to try to explain the variation in length of this species, both within and between different years. In this case does it matter if my length variable is bimodal for some of the years I will be comparing to one another?