R循环 - 迭代字符串列表，扩展为函数输入答案

【问题标题】：R loops - iterate a list of strings, expand as function inputR循环 - 迭代字符串列表，扩展为函数输入
【发布时间】：2018-02-16 13:23:57
【问题描述】：

我是一个 python 人，在使用 for 循环时遇到了麻烦。我有一个列表，表示包含列（Sample_Name_Column、ComparisonColumn、MeasureA、MeasureB、MeasureC、MeasureD）的数据框中特定列的名称，我想将其用于线性混合效应模型（使用 nlme 库）。所以我写了一个简单的循环来尝试这样做：

list <- c("MeasureA","MeasureB","MeasureC","MeasureD")
for (i in list){
  model = lme(i ~ ComparisonColumn, random=~1|Sample_Name_Column, 
  data=sampleDataSheet, method="REML")
}

但这当然失败了。

Error in model.frame.default(formula = ~i + ComparisonColumn + Sample_Name_Column,  :   variable lengths differ (found for 'ComparisonColumn')

函数 lme 不扩展变量；正在寻找一列 i 作为输入。还有其他函数，如 print() 或 length()。奇怪的。无论如何，我发现了一些使用 .asformula 并重新格式化 here 的帖子，但我在让它工作时遇到了很多麻烦。

for (i in groupList) {
model = lme(as.formula(paste0(i, " ~ ComparisonColumn, random=~1|Sample_Name_Column")), data=sampleDataSheet, method="REML")
}

我更进一步（因为已正确插入可迭代对象）：

Error in parse(text = x, keep.source = FALSE) : 
  <text>:1:26: unexpected ','
1: MeasureA ~ ComparisonColumn,
                               ^

但这里也有问题。

我应该补充一点，直接运行模型是可行的：

model = lme(MeasureA ~ ComparisonColumn, random=~1|Sample_Name_Column, 
data=sampleDataSheet, method="REML")

Linear mixed-effects model fit by REML
  Data: 
sampleDataSheet
Log-restricted-likelihood: -462.6646
Fixed: MeasureA ~ ComparisonColumn
(Intercept)      ComparisonColumnTreatmentA 
 0.81377249 -0.08312908 

Random effects:
 Formula: ~1 | Sample_Name_Column
        (Intercept)  Residual
StdDev:   0.1800545 0.5348801
Number of Observations: 564
Number of Groups: 16

我已经完成了一些工作，但是请哪位好心人帮我完成它？

谢谢，克

【问题讨论】：

标签： r loops

【解决方案1】：

as.formula 本身似乎不适合您的需要，因为您有两个公式。

看看这个例子，比较一下fm1和fm2的结果，可能对你有帮助：

fm1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)

params <- list(fixed = reformulate(c("age", "Sex"), response =  "distance"),
           random = reformulate("1"),
           data = Orthodont)

fm2 <- do.call(lme,params)

【讨论】：

谢谢。这似乎让我了解了其中的一部分，但是当我这样做时，某些函数的行为很奇怪。我将在下面发布一个答案来说明。
我认为这不是什么大问题，因为每个模型都是一个结构化的对象，你可以从 wwitch 那里得到你需要的任何信息，例如：fm2$coefficients 会给你系数.. 你可以研究str(fm2) ... 的对象结构，因此您可以轻松地使用您需要的信息构建一个 data.frame，您还可以在此 data.frame 中为使用的数据添加名称..
另一种方法是强制输出对象 .. 在我的示例中，您可以通过 all.equal(fm1, fm2) 比较 fm1 和 fm2 并看到区别在于组件 call。所以通过修改fm2$call你可以获得相同的输出..

【解决方案2】：

我认为 MrSmithGoesToWashington 已经让我快到了。我现在使用的代码是：

library(lsmeans)
library(multcomp)
library(nlme)

groupList = c("MeasureA","MeasureB","MeasureC","MeasureD")

for (i in groupList){
 print(i)

 # following MrSmithGoesToWashington's example
 params = list(fixed = reformulate("ComparisonColumn", response = i), random = reformulate("1|Sample_Name_Column"), data = sampleDataSheet, method="REML")
 model = do.call(lme,params)

 anova.lme(model, type="sequential", adjustSigma = FALSE)
 posthoc = glht(model, linfct=mcp(ComparisonColumn="Tukey"))

 Multiple_Comparisons_of_Means = summary(posthoc, sampleDataSheet=adjusted("single-step"))

 print(Multiple_Comparisons_of_Means)
}

但是对于来自 sampleDataSheet 的数据的解释方式有些奇怪。

没有循环，模型变量打印为：

> model
Linear mixed-effects model fit by REML
  Data: test 
  Log-restricted-likelihood: -2961.527
  Fixed: MeasureD ~ ComparisonColumn 
       (Intercept) ComparisonColumnNP 
      1.924292e+02       7.532103e-03 

Random effects:
 Formula: ~1 | Sample_Name_Column
        (Intercept) Residual
StdDev:    21.12601  45.3235

Number of Observations: 564
Number of Groups: 16

但在循环中，参数由 do.call 函数处理的方式，数据框作为“结构”整体打印；它打印为（一旦将循环迭代到“MeasureD”）：

> model
Linear mixed-effects model fit by REML
  Data: structure(list(Sample_Name_Column = structure(c(10L, 10L, 11L,  10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,  10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,  10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,  11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,  11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,  11L, 11L, 11L, 11L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,  9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,  9L, 9L, 9L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,  12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,  12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,  13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,  13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,  13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L,  14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,  14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,  14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L,  15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,  15L, 15L, 15L, 15L, .....), .Label = c("A.2",  "A.3", "A.4", "A.5",  "A.7", "A.4", "A.6", "A.8",  "B.10", "B.8", "B.9",  "B.3", "B.4", "B.5", "B.6",  "B.7"), class = "factor"), ComparisonColumn = structure(c(2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,  .....), .Label = c("LP", "NP"), class = "factor"), MeasureA = c(0L,  1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L,  1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L,  1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,  0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,  0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L,  1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L,  0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L,  1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,  1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 2L, 0L, 1L, 0L, .....  0L, 1L, 0L), MeasureB = c(0L, 1L, 1L, 2L, 0L, 0L, 3L, 1L, 1L,  3L, 0L, 3L, 0L, 1L, 1L, 0L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 0L, 1L,  0L, 1L, 0L, 0L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 4L, 3L, 1L, 1L,  2L, 0L, 1L, 2L, 2L, 1L, 1L, 3L, 0L, 1L, 1L, 1L, 1L, 0L, 2L, 2L,  0L, 2L, 2L, 2L, 0L, 2L, 1L, 1L, 0L, 0L, 1L, 2L, 2L, 2L, 2L, 0L,  0L, 1L, 2L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 2L, 0L, 0L,  1L, 2L, 1L, 0L, 1L, 2L, 1L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 0L,  0L, 2L, 4L, 1L, 1L, 1L, 2L, 0L, 1L, 2L, 2L, 2L, 2L, 0L, 1L, 1L,  1L, 2L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 1L, 2L, 2L, 2L, 0L, 1L, 0L,  0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 2L,  2L, 0L, 1L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L,  ..... 0L, 0L, 0L, 0L,  1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L), MeasureC = c(3L,  3L, 0L, 2L, 2L, 0L, 0L, 1L, 5L, 4L, 3L, 1L, 3L, 1L, 1L, 3L, 2L,  2L, 4L, 1L, 2L, 5L, 3L, 5L, 2L, 2L, 3L, 2L, 1L, 1L, 4L, 1L, 1L,  1L, 1L, 5L, 1L, 0L, 4L, 4L, 1L, 0L, 2L, 3L, 5L, 2L, 3L, 2L, 2L,  2L, 1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 0L, 3L, 3L,  4L, 1L, 2L, 0L, 3L, 1L, 5L, 3L, 2L, 5L, 2L, 0L, 1L, 2L, 3L, 1L,  1L, 1L, 3L, 1L, 1L, 0L, 0L, 3L, 0L, 2L, 2L, 0L, 3L, 0L, 0L, 1L,  2L, 0L, 2L, 0L, 1L, 2L, 1L, 1L, 0L, 5L, 4L, 2L, 3L, 0L, 1L, 1L,  1L, 0L, 1L, 1L, 2L, 2L, 2L, 0L, 1L, 1L, 3L, 0L, 1L, 2L, 0L, 1L,  0L, 0L, 2L, 1L, 0L, 1L, 2L, 1L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 1L,  0L, 1L, 1L, 1L, 1L, 1L, 0L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 0L, 1L,  1L, 0L, 3L, 0L, 2L, 0L, 1L, 1L, 0L, 2L, 0L, 1L, 2L, 2L, 0L, 1L,  ..... 2L, 1L, 0L, 1L, 0L, 0L, 1L, 2L,  3L, 1L, 0L, 1L, 0L, 1L, 0L, 3L, 1L, 0L, 0L, 3L, 2L, 0L, 1L, 1L,  1L, 0L, 0L), MeasureD = c(157L, 150L, 120L, 159L, 193L, 96L,  225L, 197L, 278L, 252L, 191L, 165L, 240L, 202L, 221L, 225L, 167L,  235L, 249L, 231L, 219L, 273L, 185L, 221L, 150L, 180L, 282L, 216L,  128L, 255L, 161L, 152L, 90L, 154L, 153L, 135L, 130L, 145L, 131L,  175L, 99L, 148L, 173L, 115L, 196L, 227L, 208L, 139L, 278L, 234L,  148L, 109L, 233L, 167L, 151L, 141L, 122L, 106L, 120L, 140L, 266L,  226L, 277L, 198L, 237L, 162L, 203L, 201L, 192L, 237L, 230L, 221L,  182L, 184L, 298L, 191L, 240L, 210L, 250L, 186L, 187L, 229L, 230L,  206L, 293L, 182L, 218L, 209L, 171L, 152L, 279L, 324L, 122L, 132L,  223L, 250L, 155L, 189L, 206L, 213L, 233L, 215L, 95L, 164L, 213L,  188L, 273L, 284L, 206L, 185L, 209L, 176L, 136L, 190L, 214L, 240L,  231L, 190L, 211L, 165L, 246L, 236L, 244L, 265L, 160L, 220L, 203L,  186L, 110L, 181L, 180L, 264L, 159L, 151L, 179L, 144L, 187L, 144L,  280L, 280L, 295L, 214L, 217L, 246L, 184L, 204L, 200L, 223L, 192L,  226L, 209L, 146L, 209L, 181L, 223L, 196L, 226L, 147L, 191L, 180L,  154L, 162L, 170L, 174L, 144L, 230L, 155L, 197L, 228L, 196L, 166L,  182L, 169L, 192L, 206L, 117L, 133L, 127L, 193L, 156L, 140L, 267L,  234L, 280L, 181L, 230L, 169L, 192L, 166L, 182L, 140L, 244L, 201L,  230L, 168L, 159L, 152L, 211L, 195L, 125L, ..... 202L, 295L, 188L, 103L, 104L,  168L, 229L, 210L, 163L, 228L, 231L, 143L, 164L)), .Names = c("Sample_Name_Column",  "ComparisonColumn", "MeasureA", "MeasureB", "MeasureC", "MeasureD" ), class = "data.frame", row.names = c(NA, -564L)) 
  Log-restricted-likelihood: -2961.527
  Fixed: MeasureD ~ ComparisonColumn 
       (Intercept) ComparisonColumnNP 
      1.924292e+02       7.532103e-03 

Random effects:
 Formula: ~1 | Sample_Name_Column
        (Intercept) Residual
StdDev:    21.12601  45.3235

Number of Observations: 564
Number of Groups: 16

这会对均值的多重比较的输出产生影响，它会输出整个数据帧而不是数据帧名称。这不是一个大问题，但它很混乱。

> summary(Multiple_Comparisons_of_Means)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lme.formula(fixed = MeasureD ~ ComparisonColumn, data = list(
    Sample_Name_Column = c(10L, 10L, 11L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 11L, 11L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 12L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
    3L, 3L, 3L, 3L, 3L), ComparisonColumn = c(2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L), MeasureA = c(0L, 1L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 
    1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 
    1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 
    0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 
    1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 
    1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 
    0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    0L, 1L, 0L), MeasureB = c(0L, 1L, 1L, 2L, 0L, 0L, 3L, 1L, 
    1L, 3L, 0L, 3L, 0L, 1L, 1L, 0L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 
    0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 4L, 
    3L, 1L, 1L, 2L, 0L, 1L, 2L, 2L, 1L, 1L, 3L, 0L, 1L, 1L, 1L, 
    1L, 0L, 2L, 2L, 0L, 2L, 2L, 2L, 0L, 2L, 1L, 1L, 0L, 0L, 1L, 
    2L, 2L, 2L, 2L, 0L, 0L, 1L, 2L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 2L, 0L, 0L, 1L, 2L, 1L, 0L, 1L, 2L, 1L, 0L, 0L, 
    2L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 4L, 1L, 1L, 1L, 2L, 0L, 
    1L, 2L, 2L, 2L, 2L, 0L, 1L, 1L, 1L, 2L, 0L, 1L, 3L, 0L, 0L, 
    0L, 0L, 1L, 2L, 2L, 2L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 
    0L), MeasureC = c(3L, 3L, 0L, 2L, 2L, 0L, 0L, 1L, 5L, 4L, 
    3L, 1L, 3L, 1L, 1L, 3L, 2L, 2L, 4L, 1L, 2L, 5L, 3L, 5L, 2L, 
    2L, 3L, 2L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 5L, 1L, 0L, 4L, 4L, 
    1L, 0L, 2L, 3L, 5L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 2L, 2L, 4L, 0L, 3L, 3L, 4L, 1L, 2L, 0L, 3L, 
    1L, 5L, 3L, 2L, 5L, 2L, 0L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 1L, 
    1L, 0L, 0L, 3L, 0L, 2L, 2L, 0L, 3L, 0L, 0L, 1L, 2L, 0L, 2L, 
    0L, 1L, 2L, 1L, 1L, 0L, 5L, 4L, 2L, 3L, 0L, 1L, 1L, 1L, 0L, 
    1L, 1L, 2L, 2L, 2L, 0L, 1L, 1L, 3L, 0L, 1L, 2L, 0L, 1L, 0L, 
    0L, 2L, 1L, 0L, 1L, 2L, 1L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 
    0L, 1L, 1L, 1L, 1L, 1L, 0L, 2L, 1L, 3L, 2L, 1L, 1L, 1L, 0L, 
    1L, 1L, 0L, 3L, 0L, 2L, 0L, 1L, 1L, 0L, 2L, 0L, 1L, 2L, 2L, 
    0L, 1L, 1L, 1L, 1L, 1L, 0L, 4L, 4L, 2L, 1L, 1L, 1L, 2L, 2L, 
    MeasureD = c(157L, 150L, 120L, 159L, 193L, 96L, 225L, 197L, 
    278L, 252L, 191L, 165L, 240L, 202L, 221L, 225L, 167L, 235L, 
    249L, 231L, 219L, 273L, 185L, 221L, 150L, 180L, 282L, 216L, 
    128L, 255L, 161L, 152L, 90L, 154L, 153L, 135L, 130L, 145L, 
    131L, 175L, 99L, 148L, 173L, 115L, 196L, 227L, 208L, 139L, 
    278L, 234L, 148L, 109L, 233L, 167L, 151L, 141L, 122L, 106L)), random = ~1 | Sample_Name_Column, 
    method = "REML")

Linear Hypotheses:
              Estimate Std. Error z value Pr(>|z|)
NP - LP == 0  0.007532  11.238300   0.001    0.999
(Adjusted p values reported -- single-step method)

任何想法如何解决这个问题？否则它正在工作。谢谢。

【讨论】：

我现在明白，使用类似的东西：Multiple_Comparisons_of_Means$test$pvalues[1] 会给我我需要的信息。