我需要帮助理解并跟进使用lme4中的glmer（）获得的交互。
数据来自语言处理实验，该实验研究三个分类变量（对照/ copula /性别）对二项式响应（优选或不优选）的影响。每个实验因素都有两个级别：对照（主体/对象）copula（ser / estar）性别（男性/女性）。
我运行以下模型：
model1= glmer(preferences~control*copula*gender+(1|participant), family=binomial, data=data2)
这些是我获得的结果：
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: preferences_narrow ~ control * copula * gender + (1 | participant)
Data: data2
AIC BIC logLik deviance df.resid
1208.6 1261.1 -595.3 1190.6 2517
Scaled residuals:
Min 1Q Median 3Q Max
-8.6567 0.1970 0.2337 0.2883 0.5371
Random effects:
Groups Name Variance Std.Dev.
participant (Intercept) 0.254 0.504
Number of obs: 2526, groups: participant, 105
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.5034 0.2147 11.660 < 2e-16 ***
controlsubject 0.4882 0.3172 1.539 0.12380
copulaser 0.4001 0.3237 1.236 0.21646
gendermasc -0.4524 0.2659 -1.701 0.08888 .
controlsubject:copulaser -1.0355 0.4526 -2.288 0.02215 *
controlsubject:gendermasc 0.5790 0.4430 1.307 0.19121
copulaser:gendermasc 1.7343 0.5819 2.980 0.00288 **
controlsubject:copulaser:gendermasc -1.3121 0.7540 -1.740 0.08181 .
---
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 -0.602
copulaser -0.588 0.401
gendermasc -0.724 0.488 0.479
cntrlsbjct:c 0.415 -0.701 -0.716 -0.342
cntrlsbjct:g 0.432 -0.716 -0.287 -0.599 0.502
cplsr:gndrm 0.332 -0.223 -0.556 -0.457 0.397 0.274
cntrlsbjc:: -0.252 0.421 0.430 0.352 -0.600 -0.588 -0.772
controlsubject:copulaser和copulaser:gendermasc有两个重要的相互作用。
我使用emmeans进行了第一次交互：
emmeans(model1, list(pairwise ~ control + copula), adjust = "tukey")
结果似乎表明，多重对比正在推动交互（当我为第二次交互做同样的事情时会发生类似情况）：
NOTE: Results may be misleading due to involvement in interactions
$`emmeans of control, copula`
control copula emmean SE df asymp.LCL asymp.UCL
object estar 2.277256 0.1497913 Inf 1.983670 2.570841
subject estar 3.054906 0.1912774 Inf 2.680009 3.429802
object ser 3.544448 0.2697754 Inf 3.015698 4.073198
subject ser 2.630568 0.1752365 Inf 2.287110 2.974025
Results are averaged over the levels of: gender
Results are given on the logit (not the response) scale.
Confidence level used: 0.95
$`pairwise differences of control, copula`
contrast estimate SE df z.ratio p.value
object,estar - subject,estar -0.7776499 0.2215235 Inf -3.510 0.0025
object,estar - object,ser -1.2671927 0.2910689 Inf -4.354 0.0001
object,estar - subject,ser -0.3533119 0.2088155 Inf -1.692 0.3279
subject,estar - object,ser -0.4895427 0.3138092 Inf -1.560 0.4017
subject,estar - subject,ser 0.4243380 0.2396903 Inf 1.770 0.2877
object,ser - subject,ser 0.9138807 0.3048589 Inf 2.998 0.0145
Results are averaged over the levels of: gender
Results are given on the log odds ratio (not the response) scale.
P value adjustment: tukey method for comparing a family of 4 estimates
然而，NOTE意味着什么？
NOTE: Results may be misleading due to involvement in interactions
这是跟进这些互动的好方法吗？
提前致谢！ :)
如注释中所示，所显示的估计值是对照，联会和性别组合的预测的平均值，平均为性别。同时，该模型包括性别与其他两个因素之间的相互作用，这表明这些平均值可能没有意义。您可以通过构建三向预测图来可视化：
emmip(model1, gender ~ control * copula)
如果预测与一个案例的比较截然不同，那么它们的平均值将是无意义的。但如果它们的比较几乎相同，那么平均它们就可以了。这就是警告的内容。
我猜你确实遇到过担心与性别的互动 - 在这种情况下你应该分开进行比较：
emmeans(model1, pairwise ~ control * copula | gender)