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@AhmadMkhatib's answer about what your results mean is spot-on. You have non-trivial person-level variability in the slope of your main predictor. What do you do with that? Many researchers with data …

The reason you are losing observations has nothing to do with the outcome and everything to do with the predictors. All rows in which a predictor is missing are excluded from the analysis. To verify t …

Let's start with your model: lmer(Ectomyc ~ Stand_type + (1 + Stand_type | Location) + (1 | Location:Plot)) This model examines the effect of Stand_type on Ectomyc as both a fixed ("non-varying") pop …

One issue in your code is that the way you have tx and menscat variables created, they end up perfectly correlated. I adjusted that bit of code, below, with a few other changes: n_visits <- 2 data < …

The "better" estimate question is worth addressing separately from the precision questions. An excellent paper by Lüdtke and colleagues (2009) in Contemporary Educational Psychology explains why one m …

The answer depends a bit on how your country and id variables are coded. Is it the case that: a) In each country the numbering for individual ids starts at 1? or b) In country 1 you have ids 1-10 a …

To create a person-mean centered variable, you need to first create a person mean for each individual and then subtract each person's mean from their score at a given occasion. You can use functions w …

To help make your original regression results more interpretable, I suggest that you code timepoint such that the first occasion is given a value of 0. This is because in regression models, the interc …

This is a great question that hits at one of the superpowers of multilevel or mixed effects models, in my opinion. In your example, A, B, and C all vary within groups but, very likely, groups vary in …

With data such as yours, it is quite common to use mixed effects models, so you are on the right track. However, the way you treat species in your model(s) is not something I have seen before. Please …

Keep in mind that the parameter estimate for PC1 in the lmer model summary is conditional any variables that PC1 is interacted with. In this case, you interacted PC1 with DominancePatternCod and WTW. …

I am going to work from the simpler model in which you do not have a random (or varying) slope for x and instead just have a set of random (or varying) intercepts: m1 <- lmer(dv ~ 1 + (1|f) + (1|g) + …

I believe that you are fine to use the approach you outlined in your question. There is more between id variance at time==0 (the random intercept) than there is in the rate of change (random slope for …

The model you propose has random slopes at the classroom and school levels for all student-level predictors. This may be a valid model for some data, but it would not be advisable for this particular …

For Model 1, the author specified only a single random intercept for id and then a series of fixed intercepts and slopes corresponding to treatment, age group, and the interaction of age group and tre …