我在这个论坛上看到过有关此事的问题，我也曾在之前的帖子中自问过，但我仍然无法解决我的问题。因此，我再次尝试，尽可能详细地制定问题，尽可能详细地提供信息。
我的数据集具有二项式因变量，3个分类固定效果和2个分类随机效应（项目和主题）。我正在使用glmer的混合效果模型。这是我在R中输入的内容：
modelall<- glmer(moodR ~ group*context*condition + (1|subject) + ``(1|item), data=RprodHSNS, family="binomial")`
我得到2个警告：
Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.02081 (tol = 0.001, component 11)
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?`
我的总结如下：
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: moodR ~ group * context * condition + (1 | subject) + (1 | item)
Data: RprodHSNS`
AIC BIC logLik deviance df.resid
1400.0 1479.8 -686.0 1372.0 2195 `
Scaled residuals:
Min 1Q Median 3Q Max
-8.0346 -0.2827 -0.0152 0.2038 20.6578 `
Random effects:
Groups Name Variance Std.Dev.
item (Intercept) 1.475 1.215
subject (Intercept) 1.900 1.378
Number of obs: 2209, groups: item, 54; subject, 45
Fixed effects:`
Estimate Std. Error z value Pr(>|z|)`
(Intercept) -0.61448 42.93639 -0.014 0.988582
group1 -1.29254 42.93612 -0.030 0.975984
context1 0.09359 42.93587 0.002 0.998261
context2 -0.77262 0.22894 -3.375 0.000739***
condition1 4.99219 46.32672 0.108 0.914186
group1:context1 -0.17781 42.93585 -0.004 0.996696
group1:context2 -0.10551 0.09925 -1.063 0.287741
group1:condition1 -3.07516 46.32653 -0.066 0.947075
context1:condition1 -3.47541 46.32648 -0.075 0.940199
context2:condition1 -0.07293 0.22802 -0.320 0.749087
group1:context1:condition1 2.47882 46.32656 0.054 0.957328
group1:context2:condition1 0.30360 0.09900 3.067 0.002165 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) group1 cntxt1 cntxt2 cndtn1 grp1:cnt1 grp1:2 grp1:cnd1 cnt1:1 cnt2:1 g1:1:1
group1 -1.000
context1 -1.000 1.000
context2 0.001 0.000 -0.001
condition1 -0.297 0.297 0.297 0.000
grp1:cntxt1 1.000 -1.000 -1.000 0.001 -0.297
grp1:cntxt2 0.001 0.000 0.000 -0.123 0.000 0.000
grp1:cndtn1 0.297 -0.297 -0.297 -0.001 -1.000 0.297 0.000
cntxt1:cnd1 0.297 -0.297 -0.297 -0.001 -1.000 0.297 0.001 1.000
cntxt2:cnd1 0.000 0.000 -0.001 0.011 0.001 0.000 -0.197 -0.001 -0.001
grp1:cnt1:1 -0.297 0.297 0.297 0.001 1.000 -0.297 -0.001 -1.000 -1.000 0.001
grp1:cnt2:1 0.000 0.000 0.001 -0.198 0.000 -0.001 0.252 0.000 0.001 -0.136 0.000
极高的p值，似乎不可能。
在上一篇文章中，我读到其中一个问题可以通过在命令中插入以下内容来增加迭代次数来修复：glmerControl（optimizer =＆＃34; bobyqa＆＃34;，optCtrl = list（maxfun = 100000））
这就是我所做的：
modelall<- glmer(moodR ~ group*context*condition + (1|subject) + (1|item), data=RprodHSNS, family="binomial", glmerControl(optimizer="bobyqa", optCtrl = list(maxfun = 100000)))
现在，第二个警告消失了，但第一个警告仍在那里：
> Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.005384 (tol = 0.001, component 7)
摘要看起来仍然很奇怪：
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: moodR ~ group * context * condition + (1 | subject) + (1 | item)
Data: RprodHSNS
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))`
AIC BIC logLik deviance df.resid
1400.0 1479.8 -686.0 1372.0 2195
Scaled residuals:
Min 1Q Median 3Q Max
-8.0334 -0.2827 -0.0152 0.2038 20.6610
Random effects:
Groups Name Variance Std.Dev.
item (Intercept) 1.474 1.214
subject (Intercept) 1.901 1.379
Number of obs: 2209, groups: item, 54; subject, 45
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.64869 26.29368 -0.025 0.980317
group1 -1.25835 26.29352 -0.048 0.961830
context1 0.12772 26.29316 0.005 0.996124
context2 -0.77265 0.22886 -3.376 0.000735 ***
condition1 4.97325 22.80050 0.218 0.827335
group1:context1 -0.21198 26.29303 -0.008 0.993567
group1:context2 -0.10552 0.09924 -1.063 0.287681
group1:condition1 -3.05629 22.80004 -0.134 0.893365
context1:condition1 -3.45656 22.80017 -0.152 0.879500
context2:condition1 -0.07305 0.22794 -0.320 0.748612
group1:context1:condition1 2.45996 22.80001 0.108 0.914081
group1:context2:condition1 0.30347 0.09899 3.066 0.002172 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) group1 cntxt1 cntxt2 cndtn1 grp1:cnt1 grp1:2 grp1:cnd1 cnt1:1 cnt2:1 g1:1:1
group1 -1.000
context1 -1.000 1.000
context2 0.000 0.000 0.000
condition1 0.123 -0.123 -0.123 -0.001
grp1:cntxt1 1.000 -1.000 -1.000 0.001 0.123
grp1:cntxt2 0.001 0.000 0.000 -0.123 0.001 0.000
grp1:cndtn1 -0.123 0.123 0.123 0.000 -1.000 -0.123 -0.001
cntxt1:cnd1 -0.123 0.123 0.123 0.000 -1.000 -0.123 0.000 1.000
cntxt2:cnd1 0.000 0.000 0.000 0.011 -0.001 0.000 -0.197 0.001 0.001
grp1:cnt1:1 0.123 -0.123 -0.123 0.000 1.000 0.123 0.000 -1.000 -1.000 -0.001
grp1:cnt2:1 0.000 -0.001 0.001 -0.198 0.001 -0.001 0.252 -0.001 0.000 -0.136 0.000
我能做些什么来解决这个问题？或者任何人都能告诉我这个警告甚至意味着什么？ （以一种像我这样的R-newbie可以理解的方式）非常感谢任何帮助！