解释R

Question

我在解释嵌套混合效果模型中的基线系数时遇到问题。我已经安装了一个模型Test.Score~Antra +（1 | School / Class）作为课程嵌套在学校内。当我使用coef（模型）查看系数时，他们似乎反直觉：

$`Class:School`
      (Intercept) SubjectMaths
1:A    82.73262    -4.108333
1:B    83.98870    -4.108333
1:C    82.26456    -4.108333
2:A    82.25383    -4.108333
2:B    78.22047    -4.108333
2:C    80.18982    -4.108333

$School
(Intercept) SubjectMaths
A    88.39636    -4.108333
B    77.74404    -4.108333
C    78.68460    -4.108333

attr(,"class")
[1] "coef.mer"

学校内的班级如何才能比学校内的班级低得多？要复制的数据如下：

Test.Score = c(94,88,86,90,94,87,87,92,89,92,87,94,93,91,89,92,91,
    91,95,91,82,84,90,81,92,89,85,94,88,94,94,94,86,94,93,84,82,
    92,92,83,89,83,81,87,84,80,81,83,88,82,81,90,82,85,87,82,86,
    84,87,88,82,91,95,77,88,87,79,75,91,77,82,91,95,92,89,83,79,
    90,83,83,82,79,79,78,83,82,81,77,80,79,84,83,81,78,77,75,76,
    76,84,75,78,78,71,79,70,75,75,78,76,71,76,76,73,71,80,70,71,
    78,71,74,76,74,74,77,81,78,79,76,82,79,80,73,72,83,72,81,81,
    72,79,74,67,75,71,66,65,71,73,69,65,67,71,72,68,73,65,65,74,
    67,72,72,82,70,72,86,89,87,87,88,74,92,70,89,86,63,68,74,88,
    71,88,91,76,86,75,79,76,69,86,71,78,67,67,73,69,81,79,78,80,
    72,81,69,72,75,76,68,72,78,78,77,71,73,70,77,75,75,69,77,74,
    76,68,78,76,75,68,74,69,78,76,70,79,78,67,65,86,88,65,88,73,
    66,65,85)
School = rep(c("A","B","C"), each = 80)
Class = rep(c("1","2"), each = 20,6)
Subject = rep(c("English","Maths"), each = 40, 3)
data = data.frame(Test.Score, School, Class, Subject)
data$Class = factor(data$Class)
mod = lmer(Test.Score ~ Subject + (1|School/Class), REML = F, 
    data = data)
coef(mod)

Answer 1

问题是为每个随机效应列出的系数仅包括 特定随机效应的影响。特别是，2级School:Class系数仅反映学校内班级与总体人口平均值的偏差 - 不学校级别的影响。这可能看起来很奇怪或错误，但（1）你可以通过predict()得到你想要的东西（见下文）和（2）lme4实际上并没有“嵌套”的内部表示“因此，很难确定一般系数中应包含哪些随机效应（这是一种解释，而不是借口）。

对于它的价值，coef()确实可以正常运行nlme::lme的模型...

library(lme4)
## using sum-to-zero contrasts for convenience
mod = lmer(Test.Score ~ Subject + (1|School/Class), REML = FALSE, 
           data = data, contrasts=list(Subject=contr.sum))
pframe <- with(data,expand.grid(School=levels(School),
                            Subject=levels(Subject),
                            Class=levels(Class)))
pframe$Test.Score <- predict(mod,newdata=pframe)

如果你想要平均课程价值，你需要平均英语和数学分数...

nlme::lme中的相同模型：

mod2 = nlme::lme(Test.Score ~ Subject, random = ~ 1|School/Class, method="ML",
    data = data, contrasts=list(Subject=contr.sum))
coef(mod2)         ## Class within School
coef(mod2,level=1) ## School-level

对于一些乏味（以及整齐的工具 - 这也可以通过其他方式完成），重新排列绘图系数：

rr2 <- tibble::rownames_to_column(coef(mod)[["Class:School"]])
rr2 <- dplyr::rename(rr2,Test.Score=`(Intercept)`)
rr2 <- tidyr::separate(rowname,data=rr2,into=c("Class","School"))
rr2$Subject <- NA

rr3 <- tibble::rownames_to_column(coef(mod)[["School"]])
rr3 <- dplyr::rename(rr3,Test.Score=`(Intercept)`,School=rowname)
rr3$Subject <- NA
rr3$Class <- 1.5

将所有内容绘制在一起（数据，预测，系数）：

library(ggplot2); theme_set(theme_bw())
ggplot(data,aes(Class,Test.Score,colour=Subject))+
    geom_boxplot()+
    geom_point(data=pframe,size=3,shape=16,position=position_dodge(width=0.75))+
    facet_wrap(~School,labeller=label_both)+
    geom_point(data=rr2,size=3,shape=17)+
    geom_hline(yintercept=fixef(mod)["(Intercept)"],lty=2)+
    geom_point(data=rr3,size=5,shape=18)+
    theme(panel.spacing=grid::unit(0,"lines")) ## cosmetic

有色点是预测;灰点是系数（三角形=等级;钻石=学校等级）。