I'm using the glmer function from lme4 package to model a binomial phenomena in a human DNA dataset. An allele can be missing (1) or not (0). The dataset is created with 10 different samples. Each sample was diluted at 6 different concentrations. And each of this concentration was analysed 15 times (15 replicates).
I think I have nested data, and I have a hard time to figure out how to choose the random intercept.
Given the analysis process, I'm expected correlation coming from the 15 replicates, so I wrote my random intercept as: (1|VariableN). The VariableN contains the name of the sample and its DNA quantity (ex PersX_5pg). So I will have one intercept for each sample at a given DNA quantity.
But this random intercept will not account for possible correlation between the DNA quantity, as the samples are diluted in a row. So I have an another more general random intercept: (1|VariableX). The VariableX contains only the name of the sample (ex: PersX).
-> does (1|VariableX) imply both correlations between the DNA quantity and between the replicates?
-> For the same fixed effects, the AIC value is slightly lower for the random intercept (1|VariableN), but can I only rely on the AIC value?
Thanks in advance for your help.
---------EDIT--------- The question is about the presence or the absence of certain allele at different DNA concentration. As the quantity of DNA is decreasing, this phenomena is increasing, we are "loosing" allele.
The model includes a proxy for the DNA quantity based on the peak height of the allele, called H, the size of the different markers analysed and a random intercept.
The DNA samples didn't have the same concentration before dilution, but after quantification, we could adjust the dilution and get 6 concentrations approximately similar for the 10 persons.