我已经构建了一个混合效应回归模型，以研究三个分类预测变量（S_condition，C_condition和E_condition）之间的相互作用，每个预测变量分别具有三个级别（S1，S2，S3，C1，C2，C3，E1，E2， E3）–预测连续的DV（信任）。按主题（也具有随机斜率）和声明有随机效应。
model3 <- lmer(trust ~ S_condition*C_condition*E_condition + (1+stance|subject) + (1|claim), data = dataC, REML=FALSE)
此模型输出的固定效果如下。
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.33582 0.38341 138.93163 -0.876 0.3826
S_conditionS2 -0.28344 0.47676 2683.40160 -0.595 0.5522
S_conditionS3 -0.46068 0.47957 2679.28922 -0.961 0.3368
C_conditionC2 0.25793 0.47493 2649.02712 0.543 0.5871
C_conditionC3 0.05433 0.47507 2649.41999 0.114 0.9090
E_conditionE2 -0.02748 0.47476 2648.58893 -0.058 0.9539
E_conditionE3 0.14434 0.47552 2650.55022 0.304 0.7615
S_conditionS2:C_conditionC2 -0.02042 0.66883 2649.93697 -0.031 0.9756
S_conditionS3:C_conditionC2 0.69522 0.67363 2649.56439 1.032 0.3021
S_conditionS2:C_conditionC3 0.85942 0.66985 2651.65264 1.283 0.1996
S_conditionS3:C_conditionC3 0.88873 0.67362 2649.55228 1.319 0.1872
S_conditionS2:E_conditionE2 0.08978 0.66830 2648.93336 0.134 0.8931
S_conditionS3:E_conditionE2 0.63116 0.67342 2649.17937 0.937 0.3487
S_conditionS2:E_conditionE3 0.72908 0.66942 2650.95145 1.089 0.2762
S_conditionS3:E_conditionE3 0.26589 0.67389 2650.04088 0.395 0.6932
C_conditionC2:E_conditionE2 0.47762 0.67135 2648.46205 0.711 0.4769
C_conditionC3:E_conditionE2 0.67541 0.67135 2648.44933 1.006 0.3145
C_conditionC2:E_conditionE3 0.02980 0.67182 2649.36016 0.044 0.9646
C_conditionC3:E_conditionE3 0.59804 0.67206 2649.80941 0.890 0.3736
S_conditionS2:C_conditionC2:E_conditionE2 -0.05959 0.94493 2648.67938 -0.063 0.9497
S_conditionS3:C_conditionC2:E_conditionE2 -1.61455 0.95237 2649.19981 -1.695 0.0901 .
S_conditionS2:C_conditionC3:E_conditionE2 -1.24787 0.94555 2649.51572 -1.320 0.1870
S_conditionS3:C_conditionC3:E_conditionE2 -1.39477 0.95265 2649.55567 -1.464 0.1433
S_conditionS2:C_conditionC2:E_conditionE3 -0.99598 0.94629 2650.45541 -1.053 0.2927
S_conditionS3:C_conditionC2:E_conditionE3 -1.28928 0.95209 2648.81876 -1.354 0.1758
S_conditionS2:C_conditionC3:E_conditionE3 -2.01203 0.94586 2649.91207 -2.127 0.0335 *
S_conditionS3:C_conditionC3:E_conditionE3 -1.70194 0.95235 2649.16702 -1.787 0.0740 .
我无法确定的是该模型中的截距。
是“ S_conditionS1”还是“ S_conditionS1：C_conditionC1：E_conditionE1”还是其他？
无论哪种方式，为什么每个预测变量的第一级都不会出现在输出中的其他任何地方？ （例如，如果截距确实是“ S_conditionS1：C_conditionC1：E_conditionE1”，那么为什么输出中没有诸如“ S_conditionS1：C_conditionC2：E_conditionE2”之类的系数的行，等等？？