In my study growth of plants was measured in different years on different plots (all plants were measured in all years).
The question I'd like to answer with my model is: Which factors influence growth and how?
I do not want to make predictions on growth, I am simply interested in the effects of the predictors. I am also not interested in overall fixed effects of year or plot themselves.
My predictors can be grouped in three categories:
- Predictors which characterise the sampled plants (these are continuous or categorical): A1, A2, A3 (4 levels)
- Predictors which characterise the plot (these are continuous): B1, B2
- Predictors which characterise the conditions in the years of sampling (these are continuous): C1, C2
I am so far using a GLMM with the different predictors as fixed effects and plant ID as a random effect.
growth ~ A1 + A2 + A3 + B1 + B2 + C1 + C2 + (1|Plant ID)
However, I am wondering if I have to include year of sampling and plot as additional random effects? This would especially change the standard errors of predictor A3 a lot. The categorical predictor A3 is a bit special as it has four levels which change between years but within each year each plant across all plots has the same level.
growth ~ A1 + A2 + A3 + B1 + B2 + C1 + C2 + (1|Plant ID) + (1|Year)
.. which is the same as:
growth ~ A1 + A2 + A3 + B1 + B2 + C1 + C2 + (1|Plot:Plant ID) + (1|Year)
.. but different from:
growth ~ A1 + A2 + A3 + B1 + B2 + C1 + C2 + (1|Plant ID) + (1|Year) + (1|Plot)
Do you have any advice for me on how to correctly specify the random effects?
Why does adding
Year as random effects increase the standard errors of the estimates of (some) fixed effects?