Generalized linear mixed models (or GLMMs) are an extension of linear mixed models to allow response variables from different distributions, such as binary responses. Alternatively, you could think of GLMMs as an extension of generalized linear models (e.g., logistic regression) to include both fixed and random effects (hence mixed models). The general form of the model (in matrix notation) is:

y=Xβ+Zγ+ε

Some Reference for solving Generalized linear mixed effect model Using R

1. https://cran.r-project.org/web/packages/glmm/vignettes/intro.pdf

2. http://www4.ncsu.edu/~fisik/Fikret%20Isik%20-%20Lecture%20notes%20for%20statistics%20session%20-%20IUFRO%20Genetics%20of%20Host-Parasite%20Interactions%20in%20Forestry%20-%202011.pdf

3. http://avesbiodiv.mncn.csic.es/estadistica/curso2011/regm26.pdf

Thank you. But in my case here how can i organize my data. Which are random and which are fixed which are dependent variable

unless one sees your dataset

It is difficult to give standard answers to this kind of question, but to find the answer for your data is often fairly intuitive. You have to ask yourself what the interesting variables are - what is it you want to make inference about from your data? These are as a rule of thumb your fixed effects. My guess is that you want to evaluate, if the is an effect of the treatment (but since I do not know the hypothesis or design of the study I cannot give a definitive answer)? Second, ask what other factors that might influence the measured outcome, but are not "interesting" for your question. These are the "nuisance" variables that you put in your random effects.

Thank you all