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我有一个名为 data 的数据框。我创建了一个循环遍历变量列表并使用 lapply 为每个变量创建线性模型的函​​数。这种方法基于这篇文章。

library(datasets)
testDF <- data.frame(Salaries)
#creates list of variables

varListTest <- names(testDF)[3:4] 

#creates a model for each of the variables in question
model<- lapply(varListTest, function(x) {
    lm(substitute(i~Rank, list(i = as.name(x))), data = testDF)}) 

#output model
lapply(model, summary) 

这很好用。但是,我也想以相同的方式运行事后测试,通常我会通过运行:

TukeyHSD(model)

这显然在这个例子中不起作用,但我认为这会:

lapply(model, TukeyHSD)

但这会返回:

no applicable method for 'TukeyHSD' applied to an object of class "lm"

我缺少什么来完成这项工作?

4

1 回答 1

1

尝试:

lapply(model, function(m) TukeyHSD(aov(m)))

这是一个可重现的示例:

testDF=iris


varListTest <- names(testDF)[1:3] 

#creates a model for each of the variables in question
model<- lapply(varListTest, function(x) {
  lm(substitute(i~Species, list(i = as.name(x))), data = testDF)})  


lapply(model, function(model) TukeyHSD(aov(model))) 

提供(截断)

[[1]]
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = model)

$Species
                      diff       lwr       upr p adj
versicolor-setosa    0.930 0.6862273 1.1737727     0
virginica-setosa     1.582 1.3382273 1.8257727     0
virginica-versicolor 0.652 0.4082273 0.8957727     0
于 2017-02-16T10:46:38.167 回答