3

这是我的数据框

df <- structure(list(g1 = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", "C"), class = "factor"), g2 = structure(c(1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L), .Label = c("a", "b"), class = "factor"), v1 = 1:10, v2 = c(5, 5, 6, 2, 4, 4, 2, 1, 9, 8), v3 = c(29, 10, 56, 93, 20, 14, 12, 87, 67, 37)), .Names = c("g1", "g2",  "v1", "v2", "v3"), row.names = c(NA, -10L), class = "data.frame")

   g1 g2 v1 v2 v3
1   A  a  1  5 29
2   A  a  2  5 10
3   A  a  3  6 56
4   A  b  4  2 93
5   A  b  5  4 20
6   C  a  6  4 14
7   C  a  7  2 12
8   C  b  8  1 87
9   C  b  9  9 67
10  C  b 10  8 37

我想为组 g1 和 g2 的每个组合（在本例中为 Aa、Ab、Ca、Cb）创建一个 v1、v2 和 v3 的相关矩阵。所以我想使用包 Hmisc 并与 plyr 结合

library(Hmisc)
library(plyr)

这有效（当然忽略组）：

rcorr(as.matrix(df[,3:5]), type="pearson")

但这不会：

cor.matrix <- dlply(df, .(g1,g2), rcorr(as.matrix(df[,3:5]), type="pearson"))
Error:attempt to apply non-function

我究竟做错了什么？

4

1 回答 1

2

如果您每组有超过 4 个观察值，则此方法有效（因此，为什么我rbind又df增加了 2个观察值df）：

df <- structure(list(g1 = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), 
    .Label = c("A", "C"), class = "factor"), 
    g2 = structure(c(1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L), 
    .Label = c("a", "b"), class = "factor"), 
    v1 = 1:10, v2 = c(5, 5, 6, 2, 4, 4, 2, 1, 9, 8), 
    v3 = c(29, 10, 56, 93, 20, 14, 12, 87, 67, 37)), 
    .Names = c("g1", "g2",  "v1", "v2", "v3"), row.names = c(NA, -10L), 
    class = "data.frame")


df <- rbind(df, df, df)

library(Hmisc)
lapply(split(df, df[, 1:2]), function(x) {
    rcorr(as.matrix(x[,3:5]), type="pearson")
})

编辑 这有效：

dlply(df, .(g1,g2), function(x) rcorr(as.matrix(x[,3:5]), type="pearson"))
于 2014-05-31T13:50:31.477 回答