R 如何编写循环来运行数据帧的t测试？_R_Loops_Dataframe_Statistics_Hypothesis Test

R 如何编写循环来运行数据帧的t测试？

r loops dataframe statistics

R 如何编写循环来运行数据帧的t测试？,r,loops,dataframe,statistics,hypothesis-test,R,Loops,Dataframe,Statistics,Hypothesis Test,我遇到了一个问题，对存储在数据帧中的一些数据运行t测试。我知道如何一个接一个地做，但一点效率都没有。我可以问一下如何写一个循环来完成它吗例如，我在testData中获得了数据： testData <- dput(testData) structure(list(Label = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L ),

我遇到了一个问题，对存储在数据帧中的一些数据运行t测试。我知道如何一个接一个地做，但一点效率都没有。我可以问一下如何写一个循环来完成它吗

例如，我在testData中获得了数据：

testData <- dput(testData)
structure(list(Label = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("Bad", "Good"), class = "factor"), F1 = c(0.647789237, 
0.546087915, 0.461342005, 0.794212207, 0.569199511, 0.735685704, 
0.650942066, 0.457497016, 0.808619288, 0.673100668, 0.68781739, 
0.470094549, 0.958591821, 1, 0.46908343, 0.578755283, 0.289380462, 
0.685117658, 0.296011479, 0.208821225, 0.461487258, 0.176144907, 
0.325684001), F2 = c(0.634327378, 0.602685034, 0.70643658, 0.577336318, 
0.61069332, 0.676176013, 0.685433524, 0.601847779, 0.641738937, 
0.822097452, 0.549508092, 0.711380436, 0.605492874, 0.419354439, 
0.654424433, 0.782191133, 0.826394651, 0.63269692, 0.835389099, 
0.760279322, 0.711607982, 1, 0.858631893), F3 = c(0.881115444, 
0.850553659, 0.855405201, 0.732706141, 0.816063806, 0.841134018, 
0.899594853, 0.788591779, 0.767461265, 0.954481259, 0.840970764, 
0.897785959, 0.789288481, 0.604922471, 0.865024811, 0.947356946, 
0.96622214, 0.879623595, 0.953189022, 0.960153373, 0.868949632, 
1, 0.945716439), F4 = c(0.96939781, 0.758302, 0.652984943, 0.803719964, 
0.980135127, 0.945287339, 0.84045753, 0.926053105, 0.974856922, 
0.829936068, 0.89662815, 0.823594767, 1, 0.886954348, 0.825638185, 
0.798524271, 0.524755093, 0.844685467, 0.522120663, 0.388604114, 
0.725126521, 0.46430556, 0.604943457), F5 = c(0.908895247, 0.614799496, 
0.529111461, 0.726753028, 0.942601677, 0.86641298, 0.75771251, 
0.88237302, 1, 0.817706498, 0.834060845, 0.813550164, 0.927107922, 
0.827680764, 0.797814872, 0.768118872, 0.271122929, 0.790632558, 
0.391325631, 0.257446927, 0.687042673, 0.239520504, 0.521753545
), F6 = c(0.589651031, 0.170481902, 0.137755423, 0.24453692, 
0.505348067, 0.642589538, 0.308854104, 0.286913756, 0.60756673, 
0.531315171, 0.389958915, 0.236113471, 1, 0.687877983, 0.305962183, 
0.40469629, 0.08012222, 0.376774451, 0.098261016, 0.046544022, 
0.201513755, 0.02085411, 0.113698232), F7 = c(0.460358642, 0.629499543, 
0.598616653, 0.623674078, 0.526920757, 0.494086383, 0.504021253, 
0.635105287, 0.558992452, 0.397770725, 0.543528957, 0.538542617, 
0.646897446, 0.543646493, 0.47463817, 0.385081029, 0.555731206, 
0.43769237, 0.501754893, 0.586155312, 0.496028109, 1, 0.522921361
), F8 = c(0.523850222, 0.448936418, 0.339311791, 0.487421437, 
0.462073661, 0.493421514, 0.464091025, 0.496938844, 0.5817454, 
0.474404602, 0.720114482, 0.493098785, 1, 0.528538582, 0.478233718, 
0.2695123, 0.362377901, 0.462252858, 0.287725327, 0.335584366, 
0.397324649, 0.469082387, 0.403397835), F9 = c(0.481230473, 0.349419856, 
0.309729777, 0.410783763, 0.465172146, 0.520935471, 0.380916463, 
0.422238573, 0.572283353, 0.434705384, 0.512705279, 0.358892539, 
1, 0.606926979, 0.370574926, 0.319739889, 0.249984729, 0.381053882, 
0.245597953, 0.22883148, 0.314061676, 0.233511631, 0.269890359
), F10 = c(0.592403628, 0.249811036, 0.256613757, 0.305839002, 
0.497637944, 0.601946334, 0.401643991, 0.302626606, 0.623582766, 
0.706254724, 0.435846561, 0.324357521, 1, 0.740362812, 0.402588813, 
0.537414966, 0.216458806, 0.464852608, 0.251228269, 0.181500378, 
0.31840514, 0.068594104, 0.253873772), F11 = c(0.490032261, 0.366486136, 
0.336749996, 0.421899324, 0.479339762, 0.527364467, 0.398297911, 
0.432190187, 0.584030586, 0.453666402, 0.526861753, 0.388880674, 
1, 0.615835576, 0.39058525, 0.350811433, 0.290220147, 0.397424867, 
0.288095106, 0.274852912, 0.340129804, 0.271099396, 0.305499273
)), .Names = c("Label", "F1", "F2", "F3", "F4", "F5", "F6", "F7", 
"F8", "F9", "F10", "F11"), class = "data.frame", row.names = c(NA, 
-23L))

testData使用plyr
软件包中的lappy
或ldply
之类的东西，这非常简单：
library(plyr)
cols_to_test <- c("F1", "F2", "F3")
results <- ldply(
  cols_to_test,
  function(colname) {
    t_val = t.test(testData[[colname]] ~ testData$Label)$statistic
    return(data.frame(colname=colname, t_value=t_val))
    })

我使用restrape2
将数据放在长格式中，然后使用您的代码，但在lappy
中
library(reshape2)
dat <- melt(testData)
lapply(unique(dat$variable),function(x){
       Good <- subset(dat, Label  == 'Good' & variable ==x)$value
       Bad <- subset(dat, Label == 'Bad' & variable ==x)$value
       t.test(Good,Bad)
 })

library（重塑2）
dat您可以使用公式界面来t.test
并使用lappy
沿着列名迭代以构建公式：
lapply(names(testData)[-1],function(x)
           t.test(as.formula(paste(x,"Label",sep="~")),
                  data=testData))

[[1]]

        Welch Two Sample t-test

data:  F1 by Label 
t = -3.6391, df = 13.969, p-value = 0.002691
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.4519374 -0.1167204 
sample estimates:
 mean in group Bad mean in group Good 
         0.3776753          0.6620042 


[[2]]

        Welch Two Sample t-test

data:  F2 by Label 
t = 3.7358, df = 12.121, p-value = 0.002796
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 0.06997617 0.26529723 
sample estimates:
 mean in group Bad mean in group Good 
         0.8008989          0.6332622 

...

这是一个简单的解决方案，不需要额外的软件包：
lapply(testData[-1], function(x) t.test(x ~ testData$Label))

这里testData[-1]
指的是testData
的所有列，但第一列（包含标签）。负索引用于排除数据。
这似乎是一种很常见的情况，我自己也经常这样做。为此，现在有一个名为matrixTests
的包。因此，您可以对每个列进行t检验，如下所示：
library(matrixTests)

goodMat <- testData[testData[,1]=="Good",-1]
badMat  <- testData[testData[,1]=="Bad",-1]

result <- col_t_welch(goodMat, badMat)

对于p值，有一列名为pvalue：
忘记提到这是一个测试数据，在实际数据集中，特征的名称不是“F1”到“F11”。这里有“大小”、“方向”等常用词。我如何将相同的脚本应用到我的问题上呢？这是一种很好的方法。你能多说一点关于testData[-1]的内容吗，这里的[-1]是什么？谢谢。对不起，斯文，我很天真，还是不太确定。testData[-1]指的是testData的所有列，但为什么我们使用负索引？@SamoJerom这里的负索引允许排除第一列。对于两个样本的t-test呢？@ManasiShah您可以尝试排除最后一列：lapply（testData[-c（1，ncol（testData）]，函数（x）假设。test（x~testData$Label | testData$Block）
。很好的软件包。谢谢。我已经成功地在我的数据上使用了此代码。有人能告诉我为什么df没有（nA+nB）吗−2如本文所述？@CharlotteJelleyman默认情况下，var.equal=FALSE因此它使用Welch近似值-您提供的链接中的第二个版本。
lapply(testData[-1], function(x) t.test(x ~ testData$Label))

library(matrixTests)

goodMat <- testData[testData[,1]=="Good",-1]
badMat  <- testData[testData[,1]=="Bad",-1]

result <- col_t_welch(goodMat, badMat)

> result
    obs.x obs.y obs.tot    mean.x    mean.y  mean.diff       var.x       var.y     stderr        df statistic       pvalue    conf.low   conf.high alternative mean.null conf.level
F1     15     8      23 0.6620042 0.3776753  0.2843289 0.030422051 0.032610380 0.07813088 13.969401  3.639136 0.0026907550  0.11672039  0.45193741   two.sided         0       0.95
F2     15     8      23 0.6332622 0.8008989 -0.1676367 0.007950091 0.011868380 0.04487264 12.121463 -3.735834 0.0027964901 -0.26529723 -0.06997617   two.sided         0       0.95
F3     15     8      23 0.8256733 0.9401514 -0.1144781 0.006957733 0.001949685 0.02659999 20.988353 -4.303688 0.0003146478 -0.16979764 -0.05915849   two.sided         0       0.95
F4     15     8      23 0.8742631 0.6091331  0.2651299 0.009285928 0.027017832 0.06321622  9.639523  4.194017 0.0020007742  0.12355816  0.40670172   two.sided         0       0.95
F5     15     8      23 0.8164387 0.4908705  0.3255682 0.015196701 0.054574685 0.08851525  9.132819  3.678104 0.0049648236  0.12577586  0.52536063   two.sided         0       0.95
F6     15     8      23 0.4429950 0.1678080  0.2751870 0.055993325 0.021810423 0.08036909 20.281178  3.424040 0.0026474215  0.10768889  0.44268512   two.sided         0       0.95
F7     15     8      23 0.5450866 0.5606705 -0.0155839 0.005238405 0.035530607 0.06921382  8.119018 -0.225156 0.8274218437 -0.17478492  0.14361711   two.sided         0       0.95
F8     15     8      23 0.5328120 0.3734072  0.1594048 0.023064998 0.005458074 0.04711609 20.936316  3.383236 0.0028151348  0.06140341  0.25740625   two.sided         0       0.95
F9     15     8      23 0.4797677 0.2803339  0.1994337 0.027905214 0.002845209 0.04707440 18.511452  4.236565 0.0004696924  0.10072958  0.29813785   two.sided         0       0.95
F10    15     8      23 0.4961010 0.2865410  0.2095600 0.045493711 0.023072590 0.07692196 18.972832  2.724320 0.0134746988  0.04854491  0.37057514   two.sided         0       0.95
F11    15     8      23 0.4941480 0.3147666  0.1793814 0.025996108 0.001953517 0.04446643 17.527205  4.034086 0.0008157456  0.08577994  0.27298287   two.sided         0       0.95

> result$pvalue
[1] 0.0026907550 0.0027964901 0.0003146478 0.0020007742 0.0049648236 0.0026474215 0.8274218437 0.0028151348 0.0004696924 0.0134746988 0.0008157456