有没有人知道如何使用因子设计在ANCOVA模型中进行事后测试？
我有两个由23个基线值（协变量）组成的载体和治疗后的23个值（因变量），我有两个因子同时具有两个水平。我创建了一个ANCOVA模型并计算了调整后的平均值，标准误差和置信区间。例如：
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
的调整方法之间是否存在差异杨 - 安慰剂：杨 - 药物
老安慰剂：老药物
杨 - 安慰剂：老药物
Old-Placebo：Young-Drug
所以我试过了：
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
有谁知道如何解决这个问题？
非常感谢！
PS了解更多信息，请参阅
答案 0 :(得分：3)
如果您希望使用multcomp，那么您可以利用lsmeans和multcomp包之间精彩无缝的界面（请参阅?lsm），而{{{ 1}}支持lsmeans。
glht()这消除了重新参数化的需要。单独使用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)
可以获得相同的结果：
lsmeans答案 1 :(得分：2)
此处可以进行重新参数化：
treatAge <- interaction(treatment, age)
ANC1 <- aov(after_treatment ~ baseline + treatAge)
#fits are equivalent:
all.equal(logLik(ANC), logLik(ANC1))
#[1] TRUE
library(multcomp)
summary(glht(ANC1, linfct = mcp(treatAge="Tukey")))
# Simultaneous Tests for General Linear Hypotheses
#
#Multiple Comparisons of Means: Tukey Contrasts
#
#
#Fit: aov(formula = after_treatment ~ baseline + treatAge)
#
#Linear Hypotheses:
# Estimate Std. Error t value Pr(>|t|)
#Placebo.Old - Drug.Old == 0 -0.20635 0.21913 -0.942 0.783
#Drug.Young - Drug.Old == 0 -0.25431 0.20698 -1.229 0.617
#Placebo.Young - Drug.Old == 0 -0.06288 0.20647 -0.305 0.990
#Drug.Young - Placebo.Old == 0 -0.04796 0.22407 -0.214 0.996
#Placebo.Young - Placebo.Old == 0 0.14347 0.22269 0.644 0.916
#Placebo.Young - Drug.Young == 0 0.19143 0.20585 0.930 0.789
#(Adjusted p values reported -- single-step method)
答案 2 :(得分：2)
您需要在which中使用TukeyHSD参数; “在拟合模型中列出应计算间隔的术语”。这是必需的，因为模型中有一个非因子变量（'baseline'）。该变量在包含时会导致问题，这在未指定which时是默认值。
ANC = aov(after_treatment ~ baseline + treatment*age)
TukeyHSD(ANC, which = c("treatment:age"))
如果您希望使用更灵活的glht，请参阅第8页第3节 - here