So I have read many textbooks and so many R tutorials that I am going crazy here. How do you decide on which model to use? I really hope this comes with experience but with the amount of modern techniques coming out and evidence for and against transformations, etc., how is anyone supposed to actually create a model that produces the correct result?

All I want to know is if there is a significant difference between the number of points in a plot covered with wood between two treatments (Low and High elephant impact). I would also like to know if any of the effects are significant. Each site has 5 plots (1,2,3,4,5). The number of points covered with wood were counted in each plot in 2013 and then again in 2014 and 2015. Therefore I have repeated measures.

My response variable is `Number`

= number of points covered with wood

My fixed effects or predictor variable are `Year`

(2013,2014,2015) and `Site`

(High and Low)

To account for the repeated measure, `Year`

and `Site`

are also my random effects. Or should this actually be `Plot`

(1,2,3,4,5)?

The first option is to use a GLMM, as I have both random and fixed effects; because I have count data, I selected the Poisson family:

`model<-glmer(Number~Year*Treatment+(1|Year:Treatment),data=data,family=poisson) `

Firstly, can `Year`

and `Treatment`

act as both fixed and random effects in the same model? I haven't included plot as I'm assuming the repeated measure is actually YearL is that correct?

Secondly, if my data is not normally distributed, should I log-transform it and then run the GLMM?

Or should I rather leave it untransformed and use a linear mixed effects model (LME) instead?

`model1<-lmer(Number~Year*Treatment+(1+Year|Treatment),data=data,REML=FALSE) `

For the LME, should I stipulate a distribution? Or does it automatically use the Gaussian distribution (Normal distribution)?

Again, can `Year`

and `Treatment`

be both fixed and random effects?

Could this actually be non-linear?

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#### Best Answer

If you have count data as the response variable then you should be using a glmm. A poisson model is appropriate so long as it is not over-dispersed or zero-inflated, in which case you will need to consider other glmms.

If I understood the description correctly then have 3 repeated measures in 2 sites where each site has 5 plots. So plots are nested within sites, but you don't have enough sites, or plots, to treat them as nested with the usual syntax `(1|site/plot)`

, so instead you could use the combination of site and plot as the grouping factor `(1|site:plot)`

. Treatment is clearly a fixed effect and there is no justification for treating it as random. There are only 3 years, so this can be treated as fixed too.

So I would suggest a model such as:

`glmer(Number~Year*Treatment+(1|site:plot),data=data,family=poisson) `

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