两个矩阵的所有行的所有组合的相关度/p值 [英] Correlation/p values of all combinations of all rows of two matrices

问题描述

我想在第二个数据帧中计算每个物种(bac)与每个因子(fac)的相关性以及该相关性的p值.两者在相同的站点数量下进行了测量，但是bac和fac的数量不匹配.

bac1 <- c(1,2,3,4,5)
bac2 <- c(2,3,4,5,1)
bac3 <- c(4,5,1,2,3)
bac4 <- c(5,1,2,3,4)
bac <- as.data.frame(cbind(bac1, bac2, bac3, bac4 ))
colnames(bac) <- c("station1", "station2", "station3", "station4")
rownames(bac) <- c("bac1", "bac2", "bac3", "bac4", "bac5")

fac1 <- c(1,2,3,4,5,6)
fac2 <- c(2,3,4,5,1,6)
fac3<- c(3,4,5,1,2,6)
fac4<- c(4,5,1,2,3, 6)
fac <- as.data.frame(cbind(fac1, fac2, fac3, fac4))
colnames(fac) <- c("station1", "station2", "station3", "station4")
rownames(fac) <- c("fac1", "fac2", "fac3", "fac4", "fac5", "fac6")

我想象结果看起来像这样，在某处保留名称以知道呈现哪种组合:

bac1-fac1 cor1 p1
bac1-fac2 cor2 p2
bac1-fac3 cor3 p3

bac2-fac1 corx px...

我看过Hmist的rcorr函数和psych的corr.test函数，但是找不到行进行必要排列的示例...有什么想法吗?

解决方案

如果您重组数据，以便计算成对的列之间的相关性，那将非常容易.

tbac <- data.frame(t(bac))
tfac <- data.frame(t(fac))

f <- function (x, y) cor(x, y)

tab <- outer(tfac, tbac, Vectorize(f))

as.data.frame.table(tab)

我有一个使用相同想法的答案:匹配数据和计数相同值的数字.

I would like to calculate the correlation and the p value of that correlatio of each species (bac) to each of the factors (fac) in a second data frame. Both were measured at the same number of stations, but the number of bac and fac don't match.

bac1 <- c(1,2,3,4,5)
bac2 <- c(2,3,4,5,1)
bac3 <- c(4,5,1,2,3)
bac4 <- c(5,1,2,3,4)
bac <- as.data.frame(cbind(bac1, bac2, bac3, bac4 ))
colnames(bac) <- c("station1", "station2", "station3", "station4")
rownames(bac) <- c("bac1", "bac2", "bac3", "bac4", "bac5")

fac1 <- c(1,2,3,4,5,6)
fac2 <- c(2,3,4,5,1,6)
fac3<- c(3,4,5,1,2,6)
fac4<- c(4,5,1,2,3, 6)
fac <- as.data.frame(cbind(fac1, fac2, fac3, fac4))
colnames(fac) <- c("station1", "station2", "station3", "station4")
rownames(fac) <- c("fac1", "fac2", "fac3", "fac4", "fac5", "fac6")

I imagine the result looking somewhat like this, somewhere keeping the names to know which combination is presented:

bac1-fac1 cor1 p1
bac1-fac2 cor2 p2
bac1-fac3 cor3 p3

bac2-fac1 corx px...

I have looked at function rcorr from Hmist and corr.test from psych, but can't find an example with the neccessary permutation of rows...Any ideas?

解决方案

If you restructure your data, such that you compute correlation between paired columns, it would be super easy.

tbac <- data.frame(t(bac))
tfac <- data.frame(t(fac))

f <- function (x, y) cor(x, y)

tab <- outer(tfac, tbac, Vectorize(f))

as.data.frame.table(tab)

I had an answer using the same idea: Match data and count number of same value.

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