英文:
How do I use cor.test within foreach in R?
问题
我不确定如何更正代码以在foreach中运行cor.test。我尝试过的代码如下（prac3是我正在处理的数据框的名称）。我想要相关的变量在列中。
cor.res <- foreach(i = seq_len(ncol(prac3)) %dopar% {d <- prac3[, i]correlation <- cor.test(d$x, d$y, method = "spearman")out <- c(correlation$estimate, correlation$p.value)})
希望能提供帮助。
英文:
I'm not sure how to correct the code to run a cor.test within foreach. The code I have tried is below (prac3 is the name of the data frame I am working with). The variables I want to correlate are in columns.
cor.res<- foreach(i=seq_len(ncol(prac3)) %dopar% {d <- prac3[,i]correlation<- cor.test(d$x, d$y, method = "spearman")out <- c(correlation$estimate,correlation$p.value)}cor.res<- foreach(i=seq_len(ncol(prac3)) %dopar% {correlation<- cor.test(prac3[,i], prac3, method = "spearman")out <- c(correlation$estimate,correlation$p.value)}cor.res<- foreach(i=seq_len(ncol(prac3)) %dopar% {correlation<- cor.test(prac3[[i[1]]], prac3[[i[2]]], method = "spearman")out <- c(correlation$estimate,correlation$p.value)}
Any help would be appreciated.
答案1
得分: 1
以下是代码的翻译部分：
这是我将如何执行它。问题在于cor.test()需要两个向量。因此，与其循环遍历单个变量（I），你可能想要获取所有变量的组合。话虽如此，foreach循环只遍历单个索引。我首先要做的是创建一个包含所有组合的数据框：
library(dplyr)library(foreach)data(mtcars)mtcars <- mtcars[,1:7]eg <- expand.grid(row = 1:ncol(mtcars), col = 1:ncol(mtcars))eg <- eg %>% filter(row < col)
接下来，你可以通过以下方式将数字更改为变量名称：
eg$row <- names(mtcars)[eg$row]eg$col <- names(mtcars)[eg$col]
现在，你需要循环遍历eg的所有行，其中eg[i,1]将代表第一个变量名，而eg[i,2]将代表第二个变量名。我们可以使用broom包的tidy()函数对结果进行清理，然后使用rbind将它们组合在一起：
cor.res<- foreach(i=1:nrow(eg), .combine = rbind) %dopar% {broom::tidy(cor.test(mtcars[, eg[i,1]],mtcars[, eg[i,2]],method="spearman")) %>%mutate(row=eg[i,1], col=eg[i,2])}cor.res#> # A tibble: 21 × 7#> estimate statistic p.value method alternative row col#> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr>#> 1 -0.911 10425. 4.69e-13 Spearman's rank correlation … two.side mpg cyl#> 2 -0.909 10415. 6.37e-13 Spearman's rank correlation … two.side mpg disp#> 3 0.928 395. 2.28e-14 Spearman's rank correlation … two.side cyl disp#> 4 -0.895 10337. 5.09e-12 Spearman's rank correlation … two.side mpg hp#> 5 0.902 536. 1.87e-12 Spearman's rank correlation … two.side cyl hp#> 6 0.851 813. 6.79e-10 Spearman's rank correlation … two.side disp hp#> 7 0.651 1902. 5.38e- 5 Spearman's rank correlation … two.side mpg drat#> 8 -0.679 9160. 1.94e- 5 Spearman's rank correlation … two.side cyl drat#> 9 -0.684 9186. 1.61e- 5 Spearman's rank correlation … two.side disp drat#> 10 -0.520 8294. 2.28e- 3 Spearman's rank correlation … two.side hp drat#> # … with 11 more rows, and abbreviated variable name ªalternative
2023-02-23创建，使用reprex v2.0.2
英文:
This is how I would do it. The issue is that cor.test() wants two vectors. So, rather than looping over a single variable (I) you probably want to get all pairs of variables. That said, foreach loops over a single index. What I would do is to first make a data frame that has all the combinations first:
library(dplyr)library(foreach)data(mtcars)mtcars <- mtcars[,1:7]eg <- expand.grid(row = 1:ncol(mtcars), col = 1:ncol(mtcars))eg <- eg %>% filter(row < col)
Next, you can change the numbers to variable names in the following way:
eg$row <- names(mtcars)[eg$row]eg$col <- names(mtcars)[eg$col]
Now, you need to loop over all the rows of eg, where eg[i,1] will stand in for the first variable name and eg[i,2] will stand in for the second variable name. We can use tidy() from the broom package to clean the results up a bit and then combine them with rbind:
cor.res<- foreach(i=1:nrow(eg), .combine = rbind) %dopar% {broom::tidy(cor.test(mtcars[, eg[i,1]],mtcars[, eg[i,2]],method="spearman")) %>%mutate(row=eg[i,1], col=eg[i,2])}cor.res#> # A tibble: 21 × 7#> estimate statistic p.value method alter…¹ row col#> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr>#> 1 -0.911 10425. 4.69e-13 Spearman's rank correlation … two.si… mpg cyl#> 2 -0.909 10415. 6.37e-13 Spearman's rank correlation … two.si… mpg disp#> 3 0.928 395. 2.28e-14 Spearman's rank correlation … two.si… cyl disp#> 4 -0.895 10337. 5.09e-12 Spearman's rank correlation … two.si… mpg hp#> 5 0.902 536. 1.87e-12 Spearman's rank correlation … two.si… cyl hp#> 6 0.851 813. 6.79e-10 Spearman's rank correlation … two.si… disp hp#> 7 0.651 1902. 5.38e- 5 Spearman's rank correlation … two.si… mpg drat#> 8 -0.679 9160. 1.94e- 5 Spearman's rank correlation … two.si… cyl drat#> 9 -0.684 9186. 1.61e- 5 Spearman's rank correlation … two.si… disp drat#> 10 -0.520 8294. 2.28e- 3 Spearman's rank correlation … two.si… hp drat#> # … with 11 more rows, and abbreviated variable name ¹alternative
<sup>Created on 2023-02-23 with reprex v2.0.2</sup>
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