如何對 lmer 模型進行事後測試？

這是我的數據框：

Group   <- c("G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3")
Subject <- c("S1","S2","S3","S4","S5","S6","S7","S8","S9","S10","S11","S12","S13","S14","S15","S1","S2","S3","S4","S5","S6","S7","S8","S9","S10","S11","S12","S13","S14","S15","S1","S2","S3","S4","S5","S6","S7","S8","S9","S10","S11","S12","S13","S14","S15")
Value   <- c(9.832217741,13.62390117,13.19671612,14.68552076,9.26683366,11.67886655,14.65083473,12.20969772,11.58494621,13.58474896,12.49053635,10.28208078,12.21945867,12.58276212,15.42648969,9.466436017,11.46582655,10.78725485,10.66159358,10.86701127,12.97863424,12.85276916,8.672953949,10.44587257,13.62135205,13.64038394,12.45778874,8.655142642,10.65925259,13.18336949,11.96595556,13.5552118,11.8337142,14.01763101,11.37502161,14.14801305,13.21640866,9.141392359,11.65848845,14.20350364,14.1829714,11.26202565,11.98431285,13.77216009,11.57303893)

data <- data.frame(Group, Subject, Value)

然後我運行一個線性混合效應模型來比較 3 組在“值”上的差異，其中“主題”是隨機因素：

library(lme4)
library(lmerTest)
model <- lmer (Value~Group + (1|Subject), data = data)
summary(model)

結果是：

Fixed effects:
           Estimate Std. Error       df t value Pr(>|t|)    
(Intercept) 12.48771    0.42892 31.54000  29.114   <2e-16 ***
GroupG2     -1.12666    0.46702 28.00000  -2.412   0.0226 *  
GroupG3      0.03828    0.46702 28.00000   0.082   0.9353    

但是，如何比較 Group2 和 Group3？學術文章中的約定是什麼？

您可以使用emmeans::emmeans()或lmerTest::difflsmeans()， 或multcomp::glht()。

我更喜歡emmeans（以前lsmeans）。

library(emmeans)
emmeans(model, list(pairwise ~ Group), adjust = "tukey")

下一個選項是difflsmeans。注意difflsmeans不能糾正多重比較，並且默認使用 Satterthwaite 方法來計算自由度，而不是 emmeans 默認使用的Kenward-Roger 方法，因此最好明確指定您喜歡的方法。

library(lmerTest)
difflsmeans(model, test.effs = "Group", ddf="Kenward-Roger")

Hack-R 在這個問題的另一個答案中描述了該multcomp::glht()方法。

此外，您可以通過加載lmerTest然後使用anova.

library(lmerTest)
lmerTest::anova(model)

為了清楚起見，您打算對每個主題評估 3 次價值，對嗎？看起來 Group 是“學科內”，而不是“學科間”。

引用自：https://stats.stackexchange.com/questions/237512