r - 具有因子设计的 ANCOVA 中的事后检验错误

Question

有谁知道如何在采用析因设计的 ANCOVA 模型中进行事后检验？

我有两个向量，由 23 个基线值(协变量)和 23 个治疗后值(因变量)组成，我有两个具有两个水平的因子。我创建了一个 ANCOVA 模型并计算了调整后的均值、标准误差和置信区间。示例:

library(effects)

baseline = c(0.7672,1.846,0.6487,0.4517,0.5599,0.2255,0.5946,1.435,0.5374,0.4901,1.258,0.5445,1.078,1.142,0.5,1.044,0.7824,1.059,0.6802,0.8003,0.5547,1.003,0.9213)
after_treatment = c(0.4222,1.442,0.8436,0.5544,0.8818,0.08789,0.6291,1.23,0.4093,0.7828,-0.04061,0.8686,0.8525,0.8036,0.3758,0.8531,0.2897,0.8127,1.213,0.05276,0.7364,1.001,0.8974)

age = factor(c(rep(c("Young","Old"),11),"Young")) 
treatment = factor(c(rep("Drug",12),rep("Placebo",11)))

ANC = aov(after_treatment ~ baseline + treatment*age)

effect_treatage = effect("treatment*age",ANC)
data.frame(effect_treatage)

  treatment   age       fit        se     lower     upper
1      Drug   Old 0.8232137 0.1455190 0.5174897 1.1289377
2   Placebo   Old 0.6168641 0.1643178 0.2716452 0.9620831
3      Drug Young 0.5689036 0.1469175 0.2602413 0.8775659
4   Placebo Young 0.7603360 0.1462715 0.4530309 1.0676410

现在我想测试调整后的均值是否有差异

Young-Placebo:Young-Drug
老安慰剂:老药
Young-Placebo:Old Drug
老安慰剂:新药

所以我尝试了:

library(multcomp)
pH = glht(ANC, linfct = mcp(treatment*age="Tukey"))
# Error: unexpected '=' in "ph = glht(ANC_nback, linfct = mcp(treat*age="

和:

pH = TukeyHSD(ANC)
# Error in rep.int(n, length(means)) : unimplemented type 'NULL' in 'rep3'
# In addition: Warning message:
# In replications(paste("~", xx), data = mf) : non-factors ignored: baseline

有人知道怎么解决吗？

非常感谢!

PS更多信息见

R: How to graphically plot adjusted means, SE, CI ANCOVA

Answer 1

如果你想使用 multcomp，那么你可以利用 lsmeans 和 multcomp 包之间美妙而无缝的接口(interface)(参见 ?lsm)，而 lsmeans 提供对 glht() 的支持。

baseline = c(0.7672,1.846,0.6487,0.4517,0.5599,0.2255,0.5946,1.435,0.5374,0.4901,1.258,0.5445,1.078,1.142,0.5,1.044,0.7824,1.059,0.6802,0.8003,0.5547,1.003,0.9213)
after_treatment = c(0.4222,1.442,0.8436,0.5544,0.8818,0.08789,0.6291,1.23,0.4093,0.7828,-0.04061,0.8686,0.8525,0.8036,0.3758,0.8531,0.2897,0.8127,1.213,0.05276,0.7364,1.001,0.8974)
age = factor(c(rep(c("Young","Old"),11),"Young")) 
treatment = factor(c(rep("Drug",12),rep("Placebo",11)))
Treat <- data.frame(baseline, after_treatment, age, treatment)

ANC  <-  aov(after_treatment ~ baseline + treatment*age, data=Treat)

library(multcomp)
library(lsmeans)

summary(glht(ANC, linfct = lsm(pairwise ~ treatment * age)))
## Note: df set to 18
## 
##   Simultaneous Tests for General Linear Hypotheses
## 
## Fit: aov(formula = after_treatment ~ baseline + treatment * age, data = Treat)
## 
## Linear Hypotheses:
##                                  Estimate Std. Error t value Pr(>|t|)
## Drug,Old - Placebo,Old == 0       0.20635    0.21913   0.942    0.783
## Drug,Old - Drug,Young == 0        0.25431    0.20698   1.229    0.617
## Drug,Old - Placebo,Young == 0     0.06288    0.20647   0.305    0.990
## Placebo,Old - Drug,Young == 0     0.04796    0.22407   0.214    0.996
## Placebo,Old - Placebo,Young == 0 -0.14347    0.22269  -0.644    0.916
## Drug,Young - Placebo,Young == 0  -0.19143    0.20585  -0.930    0.789
## (Adjusted p values reported -- single-step method)

这消除了重新参数化的需要。您可以单独使用 lsmeans 获得相同的结果:

lsmeans(ANC, list(pairwise ~ treatment * age))
## $`lsmeans of treatment, age`
##  treatment age      lsmean        SE df  lower.CL  upper.CL
##  Drug      Old   0.8232137 0.1455190 18 0.5174897 1.1289377
##  Placebo   Old   0.6168641 0.1643178 18 0.2716452 0.9620831
##  Drug      Young 0.5689036 0.1469175 18 0.2602413 0.8775659
##  Placebo   Young 0.7603360 0.1462715 18 0.4530309 1.0676410
## 
## Confidence level used: 0.95 
## 
## $`pairwise differences of contrast`
##  contrast                       estimate        SE df t.ratio p.value
##  Drug,Old - Placebo,Old       0.20634956 0.2191261 18   0.942  0.7831
##  Drug,Old - Drug,Young        0.25431011 0.2069829 18   1.229  0.6175
##  Drug,Old - Placebo,Young     0.06287773 0.2064728 18   0.305  0.9899
##  Placebo,Old - Drug,Young     0.04796056 0.2240713 18   0.214  0.9964
##  Placebo,Old - Placebo,Young -0.14347183 0.2226876 18  -0.644  0.9162
##  Drug,Young - Placebo,Young  -0.19143238 0.2058455 18  -0.930  0.7893
## 
## P value adjustment: tukey method for comparing a family of 4 estimates