在R中以列为单位在两个数据帧之间应用函数(ks.test) [英] Applying function (ks.test) between two data frames colum-wise in R

问题描述

我的简单问题是:如何在两个数据帧之间逐列进行ks.test?

My simple question is: How do you do a ks.test between two data frames column by column?

例如.我们有两个数据框:

Eg. We have two data frames:

D1 <- data.frame(D$Ag, D$Al, D$As, D$Ba, D$Be, D$Ca, D$Cd, D$Co, D$Cu, D$Cr)
D2 <- data.frame(S$Ag, S$Al, S$As, S$Ba, S$Be, S$Ca, S$Cd, S$Co, S$Cu, S$Cr)

注意:这只是一个例子-实际情况将包括更多的列，并且它们包含特定位置的特定元素的浓度.

Note: this is just an example - real case would include much more columns and they contain concentrations of a certain element in a specific location.

现在我想在两个数据帧之间运行ks.test:

Now i would like to run a ks.test between the two data frames :

ks.test(D$Ag, S$Ag)
ks.test(D$Al, S$Al)
ks.test(D$As, S$As)

等不做奴隶制怎么办?

当我在一个数据帧上执行shapiro.test时，我只是使用:

When i did a shapiro.test on one data frame i simply use:

lshap1 <- lapply(D1, shapiro.test)
lres1 <- sapply(lshap1, `[`, c("statistic","p.value"))

我已经阅读了一些有关循环，聚合，映射的文章-尝试过类似的东西:

I have read something abot a loop, aggregate, mapply - tried different stuff like:

apply(D1, 2, function(D2) ks.test(D2,D1[,1])$p.value)

但是我得到了很多p值= 0 ..当我手动执行操作时情况并非如此.

but then i get a lot of p-values = 0.. . which is not the case when i do it manually.

09.10.2017 我将数据导入为两个数据帧，然后将一些数据提取到较小"的数据帧中进行分析-例如在这种情况下，要检查有毒元素并排除其他元素.

09.10.2017 I import the data as two data frames and then i extract some data to "smaller" data frames for analysis - e.g. in this case looking at toxic elements and excluding others.

样本数据:dput(head(D1))和dput(head(D2)).

## Output dput(head(D1)):
structure(list(DF.As = c(-0.154868225169351, -0.291459578010276,
0.0355227595866723, 0.0892191549433623, 0.189115121672669,
-0.365222418641706
), DF.Cd = c(1.28810277421719, 1.45844987179892, 0.642331353138319,
0.673164023466527, 0.131548822144598, 0.146964746525726), DF.Cu
c(8.01131080231879, 
6.52606822875086, 2.93449454196807, 4.08720148249298, 1.55494291704341,
1.73663851851503), DF.Cr = c(0.164849379809527, 0.196759436988158,
0.307645386162046, 0.302917612808149, 0.187202322026229, 0.25358922601195
), DF.Ni = c(0.362592459542858, 0.527078409257359, 0.477116357433909,
0.469287608844157, 0.225865184678244, 0.355321456594576), DF.Pb
c(0.414448963979605,
0.616598678960665, -0.0531899082482045, 0.47477978516042,
0.422106471495816,
0.0326241032568164), DF.Zn = c(74.7657982668, 74.2978919524635,
36.6575117549406, 47.8440365300156, 21.4962811912273, 23.3823413091772
)), .Names = c("DF.As", "DF.Cd", "DF.Cu", "DF.Cr", "DF.Ni", "DF.Pb",
"DF.Zn"), row.names = c(NA, 6L), class = "data.frame")

## Output dput(head(D2)):
structure(list(DO.As = c(0.0150158517208966, -0.0477743050574027,
-0.121541780066373, -0.0376195600535572, 0.115393920133327,
0.265450918075612), DO.Cd = c(0.367936811743133, 0.445545318262818,
0.350071986298948, 
0.331513644782201, 0.603874629105229, 0.598527030667747), DO.Cu
c(1.65127139067621, 
1.90306634226191, 1.08280240161368, 1.12130376047927, 1.23137174481965,
1.16618813144813), DO.Cr = c(0.162996340978278, 0.493799568371693,
0.18441814919492, 0.179883906525139, 0.128058190333676, 0.030406737049484
), DO.Ni = c(0.290717040452464, 0.331891307317008, 0.387987078391917,
0.36147470695146, 0.774910299821917, 0.323259411199816), DO.Pb
c(-0.0584055598838365, 
0.377799120780818, -0.0741768575020139, 0.511278669452117,
0.320822577941608, 0.250377389869303), DO.Zn = c(16.5625482436821,
14.5084409384572, 16.571001044493, 18.4509635406253, 15.6876446591721,
12.7649440587945)), .Names = c("DO.As", "DO.Cd", "DO.Cu", "DO.Cr", "DO.Ni",
"DO.Pb", "DO.Zn"), row.names = c(NA, 6L), class = "data.frame")

我正在发布此消息，因为仍然出现错误:

I am posting this as i still get an error:

## This is code for execution:
col.names = colnames(D1)
lapply(col.names, function(t, d1, d2){ks.test(d1[, t], d2[, t])}, D1, D2)

## Output:
 Error in `[.data.frame`(d2, , t) : undefined columns chosen

(显示回溯按钮):

6.stop("undefined columns selected") 
5.`[.data.frame`(d2, , t) 
4.d2[, t] 
3.ks.test(d1[, t], d2[, t]) 
2.FUN(X[[i]], ...) 
1.lapply(col.names, function(t, d1, d2) {ks.test(d1[, t], d2[, t])}, D1, D2) 

推荐答案

创建了两个带有某些随机数和相同列名的data.frames D1和D2.

Created two data.frames D1 and D2 with some random numbers and same column names.

set.seed(12)
D1 = data.frame(A=rnorm(n = 30,mean = 5,sd = 2.5),B=rnorm(n = 30,mean = 4.5,sd = 2.2),C=rnorm(n = 30,mean = 2.5,sd = 12))
D2 = data.frame(A=rnorm(n = 30,mean = 5,sd = 2.49),B=rnorm(n = 30,mean = 4.4,sd = 2.2),C=rnorm(n = 30,mean = 2,sd = 12))

现在，我们可以使用列名进行循环，并将其传递给D1和D2，以在各个data.frames的相应列上执行ks.test.

Now we can use the column names to loop through and pass it to D1 and D2 to perform the ks.test on the corresponding columns of the respective data.frames.

col.names = colnames(D1)
lapply(col.names,function(t,d1,d2){ks.test(d1[,t],d2[,t])},D1,D2)

#[[1]]

#Two-sample Kolmogorov-Smirnov test

#data:  d1[, t] and d2[, t]
#D = 0.167, p-value = 0.81
#alternative hypothesis: two-sided


#[[2]]

#Two-sample Kolmogorov-Smirnov test

#data:  d1[, t] and d2[, t]
#D = 0.233, p-value = 0.39
#alternative hypothesis: two-sided


#[[3]]

#Two-sample Kolmogorov-Smirnov test

#data:  d1[, t] and d2[, t]
#D = 0.2, p-value = 0.59
#alternative hypothesis: two-sided

在问题描述中使用的表示法中，理想情况下，以下代码应该起作用:

In the notation you have used in the question description, ideally the following code should work:

col.names =colnames(S)
lapply(col.names,function(t,d1,d2){ks.test(d1[,t],d2[,t])},D,S)

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