使用mixed()拟合收敛的glmer模型后调用afex::all_fit()出现全优化器错误的技术求助

I’ve run into similar headaches with overcomplicated mixed-effects models and all_fit() before, so let’s break down why this might be happening and how to fix it:

Core Issue: Model Complexity + Optimization Constraints #

You’re fitting a 5-way full interaction binomial mixed model—that’s 31 fixed-effect parameters (5 main effects + 10 two-way + 10 three-way + 5 four-way + 1 five-way) plus a random intercept. This high-dimensional parameter space is extremely tough for most optimizers to navigate, even if bobyqa (a robust optimizer built for tricky spaces) managed to converge.

Here are the most likely reasons all optimizers are throwing errors:

1. Disabled Derivative Calculations Block Gradient-Based Optimizers
   Your original model uses calc.derivs = FALSE in glmerControl(). Many optimizers (like L-BFGS-B or nlminb) rely on gradient/derivative information to find optimal parameters. When you pass this model to all_fit(), it inherits this setting—so any optimizer that needs derivatives can’t run, hence the errors.

2. Optimizer Limitations with High-Dimensional Spaces
   Optimizers like Nelder-Mead or nlminb aren’t designed for the extreme complexity of a 5-way interaction model. They can easily get stuck in local minima, fail to find feasible parameter values, or exceed iteration limits even with your increased maxfun.

3. Initial Value Sensitivity
   all_fit() uses your original bobyqa fit as the starting point for other optimizers. Some methods (like nmkbw) are highly sensitive to initial values, and the parameters found by bobyqa might be in a region of the parameter space that other optimizers can’t work with.

Fixes to Try #

1. Re-enable Derivative Calculations for all_fit()

Override the calc.derivs = FALSE setting when calling all_fit() to let gradient-based optimizers do their job:

gm_all <- afex::all_fit(
  model_max$full_model,
  control = glmerControl(
    calc.derivs = TRUE,
    optCtrl = list(maxfun = 2e5)
  )
)

This might slow things down, but it should resolve errors for optimizers that depend on derivatives.

2. Simplify Your Model (Critical for Interpretability Too)

A 5-way interaction model is not only hard to optimize—it’s nearly impossible to interpret meaningfully. Start trimming non-significant interactions to reduce the parameter space:

• Use the LRT results from your mixed() fit to drop the highest-order interactions first (e.g., the 5-way term) if it’s not significant.
• Repeat this process for four-way, then three-way interactions until you have a parsimonious model that still captures your key effects.
  A simpler model will be far more stable across different optimizers.

3. Test Optimizers One at a Time

Instead of running all optimizers at once, test individual ones to get more specific error messages:

# Test nlminb with derivatives enabled
fit_nlminb <- glmer(
  learned_next ~ PAQ_scaled * PAT_scaled * length_scaled * freq_scaled * AOP_scaled + (1|Speaker),
  family = binomial,
  data = subset(data1, data_type == "actual"),
  control = glmerControl(
    calc.derivs = TRUE,
    optimizer = "nlminb",
    optCtrl = list(maxfun = 2e5)
  )
)

The error output from a single optimizer will give you clearer clues (e.g., whether it’s hitting iteration limits, or if there’s a parameter boundary issue).

4. Check for Complete Separation in Your Data

Binomial models can run into convergence issues if there’s complete separation (i.e., one combination of predictors perfectly predicts the outcome). Use tools like brglm2::checkSeparation() to detect this:

library(brglm2)
checkSeparation(model_max$full_model)

If separation exists, you might need to adjust your predictors (e.g., combine categories, remove problematic variables) or use penalized estimation.

A Practical Note #

If your original bobyqa fit has stable parameter estimates (check that random effect SD is reasonable, and fixed effect confidence intervals don’t span extreme values), you don’t necessarily need to validate with all optimizers. bobyqa is a reliable choice for complex glmer models, and reporting your optimizer settings (as you’ve done) is sufficient for transparency.

内容的提问来源于stack exchange，提问作者Catherine Laing