我需要帮助，使用lme4中的glmer（）来了解同一模型的两个输出之间的差异。
数据来自语言处理实验，该实验研究了三个分类变量（控制/ copula /性别）对二项式响应（优选或不推荐）的影响。每个实验因素/预测因素都有两个级别：对照（受试者/对象），copula（ser / estar）性别（男性/女性）。每个项目的响应总数略有不同，因为必须丢弃其中一些。
这是数据的样子：
> head(data2)
participant list item presentation type control control_verb copula gender response preferences preferences_narrow rt adjective
2 1 1 1 2 a subject prometer ser masc masc_adj preferred preferred 32.91 Ordenado
3 1 1 10 3 b subject declarar estar masc masc_adj preferred preferred 18.07 Arruinado
5 1 1 21 5 b object mandar estar fem fem_adj preferred preferred 15.25 Callada
8 1 1 8 8 d subject manifestar estar fem fem_adj preferred preferred 15.55 Cansada
9 1 1 9 9 a subject garantizar ser masc masc_adj preferred preferred 63.59 Ordenado
11 1 1 25 11 b object prohibir estar fem fem_adj preferred preferred 40.06 Sentada
我运行此模型
`model1= glmer(preferences~control*copula*gender+(1|participant) +(1|item), family=binomial, data`=data2)
这是当R按字母顺序获取响应/预测变量时的输出：
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: preferences_narrow ~ control * copula * gender + (1 | participant) + (1 | item)
Data: data2
AIC BIC logLik deviance df.resid
1126.6 1184.9 -553.3 1106.6 2515
Scaled residuals:
Min 1Q Median 3Q Max
-8.6537 0.1519 0.2031 0.2696 1.1485
Random effects:
Groups Name Variance Std.Dev.
participant (Intercept) 0.3371 0.5806
item (Intercept) 0.5004 0.7074
Number of obs: 2525, groups: participant, 105; item, 28
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.11880 0.32552 9.581 < 2e-16 ***
controlsubject 0.11594 0.44584 0.260 0.794832
copulaser 0.09089 0.35782 0.254 0.799495
gendermasc -0.82386 0.30586 -2.694 0.007069 **
controlsubject:copulaser -0.77092 0.48415 -1.592 0.111311
controlsubject:gendermasc 0.93221 0.47276 1.972 0.048629 *
copulaser:gendermasc 2.14234 0.61348 3.492 0.000479 ***
controlsubject:copulaser:gendermasc -1.72999 0.78585 -2.201 0.027706 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cntrls coplsr gndrms cntrlsbjct:c cntrlsbjct:g cplsr:
contrlsbjct -0.672
copulaser -0.497 0.370
gendermasc -0.605 0.441 0.540
cntrlsbjct:c 0.361 -0.563 -0.743 -0.402
cntrlsbjct:g 0.387 -0.579 -0.352 -0.646 0.533
cplsr:gndrm 0.307 -0.225 -0.586 -0.500 0.436 0.326
cntrlsbjc:: -0.235 0.355 0.463 0.393 -0.621 -0.608 -0.785
但是，当我重新调整（例如）响应变量时，如下所示：
data2$preferences_narrow = relevel(data2$preferences_narrow, ref="preferred")
现在模型无法收敛：
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00211263 (tol = 0.001, component 1)
> summary(model1)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: preferences_narrow ~ control * copula * gender + (1 | participant) + (1 | item)
Data: data2
AIC BIC logLik deviance df.resid
1126.6 1184.9 -553.3 1106.6 2515
Scaled residuals:
Min 1Q Median 3Q Max
-1.1486 -0.2695 -0.2031 -0.1519 8.6561
Random effects:
Groups Name Variance Std.Dev.
participant (Intercept) 0.3371 0.5806
item (Intercept) 0.5005 0.7075
Number of obs: 2525, groups: participant, 105; item, 28
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.11967 0.32560 -9.581 < 2e-16 ***
controlsubject -0.11504 0.44591 -0.258 0.796421
copulaser -0.08988 0.35784 -0.251 0.801673
gendermasc 0.82460 0.30591 2.696 0.007027 **
controlsubject:copulaser 0.76970 0.48418 1.590 0.111900
controlsubject:gendermasc -0.93321 0.47282 -1.974 0.048414 *
copulaser:gendermasc -2.14404 0.61360 -3.494 0.000476 ***
controlsubject:copulaser:gendermasc 1.73218 0.78598 2.204 0.027535 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cntrls coplsr gndrms cntrlsbjct:c cntrlsbjct:g cplsr:
contrlsbjct -0.672
copulaser -0.497 0.370
gendermasc -0.606 0.441 0.540
cntrlsbjct:c 0.361 -0.563 -0.743 -0.402
cntrlsbjct:g 0.387 -0.579 -0.353 -0.646 0.533
cplsr:gndrm 0.307 -0.225 -0.586 -0.500 0.436 0.326
cntrlsbjc:: -0.236 0.355 0.463 0.393 -0.621 -0.608 -0.785
convergence code: 0
Model failed to converge with max|grad| = 0.00211263 (tol = 0.001, component 1)
我不了解这种行为...如果我重新调整其他变量的大小，结果将发生很大变化。
此外，我可以解释在存在交互作用时控制的主要作用吗？我如何跟进模型的交互作用以调查它们的来源？
我尝试过：
#Follow up control*gender
emmeans(model1, list(pairwise ~ control*gender), adjust = "bonf")
#Follow up copula*gender
emmeans(model1, list(pairwise ~ copula*gender), adjust = "bonf")
#Follow up control*copula*gender
emmeans(model1, list(pairwise ~ control*copula*gender), adjust = "bonf")
谢谢：）