[David Sidhu]

Thanks very much!

[David Sidhu]

Thanks very much for the reply! I was wondering if you could just comment on parametric bootstrapping (and potentially LRTs, though I don’t seem to have the number of levels in my random effects to make this work) vs. the p values that glmer() generates? I believe that these are based on Wald tests. I seem to be getting convergence errors when using afex that don’t occur when just using lme4 and glmer(). Does this mean that the results of glmer() shouldn’t be trusted?

Note that this is after setting:

control = glmerControl(optCtrl = list(maxfun = 1e6))

as well as trying

all_fit = TRUE

I get two types of convergence errors:

Model failed to converge with max|grad| = 0.00533471 (tol = 0.001, component 1)

and

unable to evaluate scaled gradientModel failed to converge: degenerate Hessian with 1 negative eigenvalues

[David Sidhu]

I’ll add that I don’t have > 40 levels of my random effects, so I assume I should use parametric bootstrapping? Is the afex package, used in this way, the best option available to me?

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