我希望能够计算每个物种(bac)与第二个数据框中的每个因子(fac)之间的相关性和p值。两者在相同数量的站点上进行了测量,但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")
我想象结果看起来有点像这样,同时保留名称以知道呈现的组合是哪个:
bac1-fac1 cor1 p1
bac1-fac2 cor2 p2
bac1-fac3 cor3 p3
bac2-fac1 corx px...
我查看了Hmist的rcorr函数和psych的corr.test函数,但是找不到必要行重排的示例... 有什么想法吗?