R：按变量迭代

发布于 2024-10-30 21:25:26 字数 470 浏览 1 评论 0原文

我有以下数据集1：

Height | Group
1,556  |  A
2,111  |  B
1,556  |  A
2,341  |  B
1,256  |  A
2,411  |  B

我想通过变量 Group

myvar <- c("Height")

res<- vector("list", length(myvars))

a <- factor(dataset1$Group)
myfactor <- levels(a)

i=1
for (myfactor in dataset1) {
    res[[i]] <- shapiro.test(dataset1$Size)
    i=i+1
}

res 计算高度的 shapiro wilk 正态性检验 - 返回 n 组测试，但全部具有相同的 p 值和 W。谁能帮我找出问题所在吗？

原文

I have the following dataset1:

Height | Group
1,556  |  A
2,111  |  B
1,556  |  A
2,341  |  B
1,256  |  A
2,411  |  B

I would like to compute shapiro wilk normality test for Height by variable Group

myvar <- c("Height")

res<- vector("list", length(myvars))

a <- factor(dataset1$Group)
myfactor <- levels(a)

i=1
for (myfactor in dataset1) {
    res[[i]] <- shapiro.test(dataset1$Size)
    i=i+1
}

res - returns n groups of tests, but all with same p-value and W.
Can anyone help me figure out what's wrong?

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尬尬 2024-11-06 21:25:26

编写新代码比查找代码中的所有错误更容易。

lapply(split(dataset1$Height,dataset1$Group),shapiro.test)

编写新代码比查找代码中的所有错误更容易。
  A`

        Shapiro-Wilk normality test

data:  X[[1L]] 
W = 0.75, p-value = 3.031e-08


编写新代码比查找代码中的所有错误更容易。
  B`

       Shapiro-Wilk normality test

data:  X[[2L]] 
W = 0.9134, p-value = 0.4295

It is easier to write new code than find all errors in your code.

lapply(split(dataset1$Height,dataset1$Group),shapiro.test)

It is easier to write new code than find all errors in your code.
  A`

        Shapiro-Wilk normality test

data:  X[[1L]] 
W = 0.75, p-value = 3.031e-08


It is easier to write new code than find all errors in your code.
  B`

       Shapiro-Wilk normality test

data:  X[[2L]] 
W = 0.9134, p-value = 0.4295

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衣神在巴黎 2024-11-06 21:25:26

您的代码可以通过各种方式进行处理。以下是一些：

您在循环外部创建 myfactor，然后将其设为迭代器。
dataset1 是您的数据（data.frame？）。我什至不确定 for (myfactor in dataset1) 创建的循环内将包含什么 myfactor。
您不会对发送到 shapiro.test 的数据进行子集化。
myvars 未定义，dataset1$Size 可能应该是 dataset1$Height。

试试这个吧。

res <- list()
for (mf in levels(dataset1$Group)) {
    res[[mf]] <- shapiro.test(dataset1$Height[dataset1$Group == mf])
}

Your code is hosed is all sorts of ways. Here are a few:

You create myfactor outside of the loop, but then you make it the iterator.
dataset1 is your data (data.frame?). I'm not even sure what myfactor will be inside a loop created by for (myfactor in dataset1).
You don't subset the data sent to shapiro.test.
myvars isn't defined and dataset1$Size should probably be dataset1$Height.

Try this instead.

res <- list()
for (mf in levels(dataset1$Group)) {
    res[[mf]] <- shapiro.test(dataset1$Height[dataset1$Group == mf])
}

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给妤﹃绝世温柔 2024-11-06 21:25:26

谢谢您的回复。
未来通知：
如果您希望按因子计算（对于数据集中选定的变量）正态性检验：

variaveis <- colnames(dataset1)[c(1:2)]
/////alternative: variaveis <- c("height", "weight") 
res<- vector("list", length(variaveis))

for (i in 1:length(variaveis)) {
    #calcula o shapiro por factor para variaveis selecionadas
    res[[i]] <- lapply(split(dataset1[,variaveis[i]] ,dataset1$sex), shapiro.test)
}
res

PS：上一个示例中的 sex = GROUP
再次感谢
希望这段代码有助于减少代码
M。

Thanks for the reply.
For future notice:
If you wish to compute (for selected variables in a dataset) a normality test by factor:

variaveis <- colnames(dataset1)[c(1:2)]
/////alternative: variaveis <- c("height", "weight") 
res<- vector("list", length(variaveis))

for (i in 1:length(variaveis)) {
    #calcula o shapiro por factor para variaveis selecionadas
    res[[i]] <- lapply(split(dataset1[,variaveis[i]] ,dataset1$sex), shapiro.test)
}
res

PS: sex = GROUP in the previous example
Again Thanks
Wish this code helps reducing code
M.

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