lme4: adding covariates and interpreting output I am new to mixed models and lme4 package. I think my data is too complicated for me to understand things using it... what I want is for someone to please explain this to me in layman's terms using a simple example (if one exists).
My data: I have a lot of data (multiple experiments), but generally speaking... response variables = continuous, normally distributed (some are not, I will get to those later), explanatory variables = mostly discrete. Random effects = group/individual (I "nest" these).
First, in model setup, I need to add mass as a covariate for one of my models. I don't know how to do that, so a basic example (with code) of how one adds a covariate to a lmm (with lme4 package) would be great. I read their help manual on this but it wasn't very helpful to me.
Second, I get the output from lme4 and I have no idea what it is telling me. When I rerun lme4 things with the afex package for the oh-so-controversial p-values, it tells me everything is significant but I still don't know what that means.
Any help or any direction toward a YouTube video, website tutorial, etc. that can explain this to a complete idiot (which is what I feel like I am at the moment) would be greatly appreciated.
Thank you!


I need to add mass as a covariate for one of my models. I don't know how to do that

If your model looks something like
y ~ x1 + (1 | G) 

where x1 is a fixed effect and G is a grouping variable for which you want to fit random intercepts, then if mass is the name of the mass variable you would fit:
y ~ x1 + mass + (1 | G) 


When I rerun lme4 things with the afex package for the oh-so-controversial p-values, it tells me everything is significant but I still don't know what that means.

Each p-value refers to a statistical test of a null hypothesis - for example, a test of whether a regression coefficient is zero or not. The p-values tell you the probability of observing your data, or data more extreme, if (and only if) the null hypothesis is true. If this doesn't make sense to you, then you are not alone, and that is part of the reason for the controversy surrounding p-values. Another issue regarding mixed models in particular is that the p-values are actually just approximations, because the number of degrees of freedom for the relevant test is not known and can only be approximated (eg Satterthwaite's or Kenward-Roger's method), so the idea of using p-values with respect to some arbitrary cut-off value for "significance", is folly. I suggest erring on the side of caution and don't use p-values.  Or if you really want a probability that makes sense, then you can obtain probabilities that the null hypotheses are true (which is typically what most people would like to know, rather than what a p-value tells them), then you can adopt a fully Bayesian approach using a package like brms.
Once final point on your model. You mention that you fit nested random effects for individuals within groups. Depending on how many groups you have, this probably does not make sense. Eg if it's a treatment group where participants are nested within 1 of 2 groups, then the group variable should be a fixed effect, not random. 2 is too few levels to fit random intercepts.
