使用mixed（）运行glmer后afex:：all_fit（）中出错_R_Glm_Lme4_Mixed Models_Mixed

使用mixed（）运行glmer后afex:：all_fit（）中出错

使用mixed（）运行glmer后afex:：all_fit（）中出错,r,glm,lme4,mixed-models,mixed,R,Glm,Lme4,Mixed Models,Mixed,我已经使用mixed（）成功运行了glmer模型。我花了一段时间才找到一个会收敛的模型，因为我有很多变量，但最终的模型如下所示： > head(data1) # A tibble: 6 x 8 Speaker data_type learned_next AOP_scaled length_scaled PAT_scaled PAQ_scaled freq_scaled <chr> <chr> <dbl> &

我已经使用

mixed（）

成功运行了glmer模型。我花了一段时间才找到一个会收敛的模型，因为我有很多变量，但最终的模型如下所示：

 > head(data1)
 # A tibble: 6 x 8
   Speaker data_type learned_next AOP_scaled length_scaled PAT_scaled PAQ_scaled freq_scaled
   <chr>   <chr>        <dbl>       <dbl>         <dbl>      <dbl>      <dbl>   <dbl>
 1 Alex    actual          0          -0.337        -2.34      -1.34      -0.345    -0.00436
 2 Alex    actual          0          -0.337        -0.989     -1.34      -0.345    -0.00436
 3 Alex    actual          0          -0.337        -2.34      -1.14      -0.345    -0.00436
 4 Alex    actual          0          -0.337        -0.989     -1.14      -0.345    -0.00436
 5 Alex    actual          0          -0.337        -2.34      -0.720     -0.345    -0.00436
 6 Alex    actual          0          -0.337        -0.989     -0.720     -0.345    -0.00436

 model_max <- mixed(learned_next ~ 
                PAQ_scaled * PAT_scaled * length_scaled * freq_scaled * AOP_scaled + 
                (1|Speaker), 
                family = binomial,
                data = subset(data1, data_type == "actual"), 
                method = "LRT",
                control=glmerControl(calc.derivs = FALSE,
                                     optimizer="bobyqa",
                                     optCtrl=list(maxfun=2e5)),  # specifiying optimizer to support convergence 
                                                                 # (does not converge without this)
                expand_re = TRUE)

但是当我运行

gmu时
 > model_max$full_model
 Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
  Family: binomial  ( logit )
 Formula: learned_next ~ PAQ_scaled * PAT_scaled * length_scaled * freq_scaled *      AOP_scaled + (1 | Speaker)
    Data: data
       AIC       BIC    logLik  deviance  df.resid 
 14431.354 14692.583 -7182.677 14365.354     20219 
 Random effects:
  Groups  Name        Std.Dev.
  Speaker (Intercept) 0.6917  
 Number of obs: 20252, groups:  Speaker, 5
 Fixed Effects:
                                                (Intercept)                                                  
 PAQ_scaled  
                                                  -0.805275                                                   
 -0.063157  
                                                 PAT_scaled                                               
 length_scaled  
                                                  -0.831367                                                    
 0.067195  
                                                freq_scaled                                                  
 AOP_scaled  
                                                   0.070104                                                   
 -0.774926  

 > bobyqa. : [ERROR]
 > Nelder_Mead. : [ERROR]
 > optimx.nlminb : [ERROR]
 > optimx.L-BFGS-B : [ERROR]
 > nloptwrap.NLOPT_LN_NELDERMEAD : [ERROR]
 > nloptwrap.NLOPT_LN_BOBYQA : [ERROR]
 > nmkbw. : [ERROR]