如何在R中的矩阵中找到行，列和总数的总和？
通过分别使用函数rowSums，colSums和sum，可以简单地找到矩阵中行，列和总数的总和。行总和，列总和和总计是最常用的比较分析工具，例如方差分析，卡方检验等。
例1
M1<−matrix(1:25,nrow=5) M1输出结果
[,1] [,2] [,3] [,4] [,5] [1,] 1 6 11 16 21 [2,] 2 7 12 17 22 [3,] 3 8 13 18 23 [4,] 4 9 14 19 24 [5,] 5 10 15 20 25
示例
rowSums(M1)输出结果
[1] 55 60 65 70 75
示例
colSums(M1)输出结果
[1] 15 40 65 90 115
示例
sum(M1)输出结果
[1] 325
例2
M2<−matrix(rpois(64,5),nrow=8) M2输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 3 7 6 3 8 4 4 4 [2,] 5 4 6 5 4 6 5 4 [3,] 7 4 3 6 4 2 6 4 [4,] 4 7 4 3 11 3 7 4 [5,] 3 4 11 9 3 4 4 3 [6,] 3 10 3 3 5 8 6 2 [7,] 9 2 3 4 6 6 3 4 [8,] 7 8 1 5 6 7 5 3
示例
rowSums(M2)输出结果
[1] 39 39 36 43 41 40 37 42
示例
colSums(M2)输出结果
[1] 41 46 37 38 47 40 40 28
示例
sum(M2)输出结果
[1] 317
范例3
M3<−matrix(rpois(100,8),nrow=20) M3输出结果
[,1] [,2] [,3] [,4] [,5] [1,] 5 11 12 5 11 [2,] 7 7 9 8 10 [3,] 15 11 8 11 7 [4,] 9 3 7 12 7 [5,] 6 9 4 4 7 [6,] 10 8 7 8 4 [7,] 14 6 6 7 9 [8,] 6 5 5 10 7 [9,] 8 7 3 7 10 [10,] 12 7 5 11 8 [11,] 8 10 9 11 10 [12,] 4 7 9 9 12 [13,] 9 8 15 10 6 [14,] 6 4 9 7 6 [15,] 7 10 7 7 9 [16,] 10 11 8 7 7 [17,] 9 6 12 6 9 [18,] 5 8 8 7 6 [19,] 5 7 3 3 8 [20,] 12 7 9 7 10输出结果
rowSums(M3)
示例
[1] 44 41 52 38 30 37 42 33 35 43 48 41 48 32 40 43 42 34 26 45
示例
colSums(M3)输出结果
[1] 167 152 155 157 163
示例
sum(M3)输出结果
[1] 794
例子4
M4<−matrix(rnorm(80),nrow=20) M4输出结果
[,1] [,2] [,3] [,4] [1,] 0.3188698 0.81972400 1.196668896 −0.1600363 [2,] 1.1626229 0.58996464 −0.936554585 1.1429420 [3,] −0.5603425 −1.76147185 0.287399797 0.4585298 [4,] −0.9019506 −0.23604072 −1.034877755 1.0206584 [5,] −0.5248356 1.05424991 −0.645217913 −0.4739691 [6,] −1.5610909 −0.20283113 0.996732180 −0.7709547 [7,] 0.2476689 0.76585019 −2.972610580 −0.2166821 [8,] −0.6014235 −0.80808451 −1.318205769 0.5311314 [9,] −0.5758808 1.00178363 0.030445943 0.9135367 [10,] 0.1207577 1.10829807 −0.057548495 0.2686915 [11,] 1.3505622 0.21916808 −0.521492576 0.2863660 [12,] −1.0845436 0.81589145 −0.316661626 −0.6171679 [13,] 0.6450188 1.22799788 −0.625778034 0.6154738 [14,] −2.9332291 1.09912124 0.274039557 0.5219165 [15,] 1.3656969 −1.91670352 1.099289293 0.1918600 [16,] −1.3845404 1.56674213 0.951188176 −0.2644617 [17,] −1.4644000 −0.02311353 0.006714121 0.2755697 [18,] −0.8878274 −1.08802913 1.098809046 −0.8005284 [19,] −0.6842826 0.05200371 0.488929737 1.7782674 [20,] 1.9084408 1.73997571 −0.419218542 −0.9593852
示例
rowSums(M4)输出结果
[1] 2.1752264 1.9589749 −1.5758848 −1.1522107 −0.5897728 −1.5381445 [7] −2.1757735 −2.1965824 1.3698855 1.4401988 1.3346037 −1.2024817 [13] 1.8627125 −1.0381517 0.7401427 0.8689281 −1.2052297 −1.6775759 [19] 1.6349182 2.2698128
示例
colSums(M4)输出结果
[1] −6.044709 6.024496 −2.417949 3.741758
示例
sum(M4)输出结果
[1] 1.303596