我有一个包含9个列表的列表,请参阅以下代码,其中我只想分别为Pearson,Spearson和Kendall相关性循环三个列表p
,r
和t
,而不是所有9个列表。
当前的伪代码如下所示,其中测试函数为corrplot(M.cor, ...)
,请参见下面的完整伪代码
for (i in p.mat.all) {
...
}
包含mtcars
测试数据的代码
library("psych")
library("corrplot")
M <- mtcars
M.cor <- cor(M)
p.mat.all <- psych::corr.test(M.cor, method = c("pearson", "kendall", "spearman"),
adjust = "none", ci = F)
str(p.mat.all)
str(p.mat.all$r)
str(p.mat.all$t)
str(p.mat.all$p)
关于9个列表的列表的输出
List of 9
$ r : num [1:11, 1:11] 1 -0.991 -0.993 -0.956 0.939 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
$ n : num 11
$ t : num [1:11, 1:11] Inf -21.92 -25.4 -9.78 8.22 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
$ p : num [1:11, 1:11] 0.00 4.04e-09 1.09e-09 4.32e-06 1.78e-05 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
$ se : num [1:11, 1:11] 0 0.0452 0.0391 0.0978 0.1143 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
.. ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
$ adjust: chr "none"
$ sym : logi TRUE
$ ci : NULL
$ Call : language psych::corr.test(x = M.cor, method = c("pearson", "kendall", "spearman"), adjust = "none", ci = F)
- attr(*, "class")= chr [1:2] "psych" "corr.test"
num [1:11, 1:11] 1 -0.991 -0.993 -0.956 0.939 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
num [1:11, 1:11] Inf -21.92 -25.4 -9.78 8.22 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
num [1:11, 1:11] 0.00 4.04e-09 1.09e-09 4.32e-06 1.78e-05 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...
我的伪代码是关于使用测试函数corrplot
循环所有三个相关性,但它不起作用,因为它遍历所有9个列表
for (i in p.mat.all) {
p.mat <- i
print("p.mat ===========")
print(i)
alpha <- 0.05
corrplot( M.cor,
method="color",
type="upper",
addCoefasPercent = TRUE,
tl.col = "black",
tl.pos = "td",
p.mat = p.mat, sig.level = alpha, insig = "blank",
order = "original"
)
}
预期输出:仅循环t
,p
和r
列表,以便将它们传递给测试函数corrplot
R:3.3.1
操作系统:Debian 8.5
答案 0 :(得分:4)
或使用* apply函数:
lapply(p.mat.all[c("r","p","t")], function(x) {
# x takes now first p.mat.all$r, then p.mat.all$p, etc
print("p.mat ===========")
print(x)
alpha <- 0.05
corrplot( M.cor,
method="color",
type="upper",
addCoefasPercent = TRUE,
tl.col = "black",
tl.pos = "td",
p.mat = x, sig.level = alpha, insig = "blank",
order = "original"
)
})
答案 1 :(得分:1)