Can a G lmer fit produce a convergence warning?

Can a G lmer fit produce a convergence warning?

[g]lmer fits may produce convergence warnings; these do not necessarily mean the fit is incorrect (see “Theoretical details” below). The following steps are recommended assessing and resolving convergence warnings (also see examples below):

How to avoid ” model failed to converge ” warnings?

If you used a marginally simpler model like: m1 = lmer(effect~duration+ (1+duration|sites) + (0+duration|season) + (1|season), data=dat1, REML = FALSE) you would experience no convergence issues.

How to solve failed convergence in lmer 1.0?

“Solving” the issue you experience in the sense of not receiving warnings about failed convergence is rather straightforward: you do not use the default BOBYQAoptimiser but instead you opt to use the Nelder-Meadoptimisation routine used by default in earlier 1.0.xprevious versions.

How to assess and resolve a convergence warning?

The following steps are recommended assessing and resolving convergence warnings (also see examples below): adjust stopping (convergence) tolerances for the nonlinear optimizer, using the optCtrl argument to [g]lmerControl (see “Convergence controls” below)

Which is the best control for minqa in glmer?

Changing ftol_abs and xtol_abs to stricter values (e.g. 1e-8) is a good first step for resolving convergence problems, at the cost of slowing down model fits. the controls for minqa::bobyqa (default for glmer first-stage optimization) are

Which is a better fit the glm or the lmer?

Similarly, in the full dataset, the GLM is a much better fit. Then, I next tried to add a random effect of trial. Lmer worked fine:

How to assess the convergence of an algorithm?

Assessing the convergence of such algorithms reliably is difficult. For example, evaluating the Karush-Kuhn-Tucker conditions (convergence criteria which reduce in simple cases to showing that the gradient is zero and the Hessian is positive definite) is challenging because of the difficulty of evaluating the gradient and Hessian.

What kind of Hessian is used for convergence checking?

At present the Hessian computations used for convergence checking (and for estimating standard errors of fixed-effect parameters for GLMMs) follow the ordinal package in using a naive but computationally cheap centered finite difference computation (with a fixed step size of 1e-4 ).

How scared should we be about convergence warnings in..?

Advice on how to interpret the convergence problems, how extreme they need be to really get us worried and possible ways to try manage them beyond those mentioned would be very helpful. Using: R version 3.1.0 (2014-04-10) and lme4_1.1-6 Be afraid. Be very afraid.

Is the convergence warning false positive in nloptwrap?

While this will of course be slow for large fits, we consider it the gold standard; if all optimizers converge to values that are practically equivalent, then we would consider the convergence warnings to be false positives. the controls for the nloptwrap optimizer (the default for lmer) are