如何在R中创建具有随机值的矩阵？
通常，使用给定值创建矩阵，但是如果我们要创建具有随机值的矩阵，则将对矩阵函数使用常规方法。R中的随机选择可以根据我们的目标以多种方式完成，例如，如果要从正态分布中随机选择值，则将使用rnorm函数并将其存储在矩阵中，然后将其传递到矩阵函数中。
示例
M1<-matrix(rnorm(36),nrow=6) M1
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.6379473 0.80467462 0.3398509 -0.4758089 1.292599611 1.24523919 [2,] 0.6832479 0.72504723 0.1156296 1.0744991 -1.104026056 -0.06716353 [3,] -0.3335057 0.97958477 1.0170069 -0.6235373 1.753112623 -1.00673575 [4,] 1.2129095 0.02634779 1.8586486 1.7806879 0.004136129 -1.07514918 [5,] -0.3875573 0.03151228 0.9427233 -0.3176984 1.094996191 -0.38860379 [6,] -0.4515077 -1.20904118 -0.7977128 0.8835648 0.762056845 1.17373715
示例
M2<-matrix(runif(36),nrow=6) M2
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.4396258 0.01739362 0.4757882 0.6158219 0.01676052 0.9088362 [2,] 0.6166727 0.42229846 0.7145908 0.6728221 0.45173776 0.3929604 [3,] 0.2847230 0.18784287 0.2837930 0.7159734 0.47905932 0.8931971 [4,] 0.9507952 0.67257546 0.8142641 0.9384804 0.15925309 0.2147781 [5,] 0.8064644 0.38956299 0.9106267 0.9887673 0.47287897 0.6783567 [6,] 0.2491736 0.15939018 0.4609571 0.8415587 0.40739792 0.6961309
示例
M3<-matrix(rexp(36),nrow=6) M3
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 1.22202307 3.99991458 0.26236165 0.5986415 0.96159749 0.6132856 [2,] 0.49396273 1.81352838 0.78716769 0.5748733 0.75516992 1.8929672 [3,] 0.68045397 0.12632613 1.41299054 0.6019353 0.20209984 0.2298947 [4,] 1.06590096 0.97355601 1.04021884 0.3013939 0.06098269 2.0683307 [5,] 0.05269628 0.34586402 0.09467971 2.4993345 1.92620852 0.3513072 [6,] 0.67667707 0.07113277 2.72820562 0.5355704 3.04340352 0.1567816
示例
M4<-matrix(rpois(36,5),nrow=6) M4
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 5 6 6 3 7 4 [2,] 6 9 7 8 8 4 [3,] 3 7 6 5 7 8 [4,] 5 4 5 5 6 3 [5,] 4 8 8 3 2 3 [6,] 8 6 4 6 8 8
示例
M5<-matrix(rpois(100,10),ncol=10) M5
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 9 11 17 16 6 3 9 13 12 11 [2,] 11 11 10 5 10 9 7 8 10 8 [3,] 9 6 4 6 11 10 13 15 19 9 [4,] 6 9 13 15 16 7 7 13 9 9 [5,] 9 12 9 12 11 11 13 6 4 9 [6,] 13 4 13 15 1 10 9 10 12 7 [7,] 12 9 13 13 11 12 10 12 11 9 [8,] 10 7 10 15 10 8 17 9 7 9 [9,] 12 12 10 14 5 9 11 4 11 5 [10,] 16 8 7 9 14 10 11 9 8 14
示例
M6<-matrix(rbinom(100,20,0.6),ncol=10) M6
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 16 10 9 11 10 12 16 12 10 10 [2,] 14 12 15 10 10 16 12 7 15 12 [3,] 15 11 12 16 10 11 12 10 8 14 [4,] 12 13 11 12 10 11 15 12 12 13 [5,] 12 11 11 14 11 12 13 11 13 14 [6,] 12 12 10 12 17 10 15 12 13 12 [7,] 12 14 16 10 12 10 11 12 8 12 [8,] 14 14 13 8 15 11 8 13 13 15 [9,] 12 14 16 14 8 15 13 9 11 16 [10,] 9 15 7 10 12 16 13 14 12 11
示例
M7<-matrix(runif(64,10,15),ncol=8) M7
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 10.76525 12.00224 12.29660 13.18522 13.49011 10.68609 14.71455 13.71585 [2,] 12.69970 10.76252 11.28360 13.80618 14.38140 12.32404 10.13467 13.46201 [3,] 13.69108 10.51077 12.99480 10.11390 12.08921 10.01959 12.23036 10.96521 [4,] 10.99119 12.42010 10.48445 14.96720 12.88057 11.30026 12.78480 11.24625 [5,] 13.39850 10.17959 13.94115 10.05765 11.17439 10.14223 11.75271 14.92220 [6,] 10.58127 10.12117 10.84108 10.88573 13.60804 12.54398 12.23277 10.32932 [7,] 11.20005 12.51919 12.19456 12.66209 10.62735 14.91281 11.95937 13.42508 [8,] 11.85078 13.30276 10.25665 12.85449 11.91376 10.89669 11.69131 12.37313
示例
M8<-matrix(sample(1:20,100,replace=TRUE),ncol=10) M8
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 8 16 8 20 5 11 7 11 4 2 [2,] 6 14 20 8 18 3 7 19 3 18 [3,] 13 6 3 20 16 20 2 7 7 11 [4,] 12 20 4 20 18 1 18 10 4 1 [5,] 6 20 20 15 13 10 9 13 7 3 [6,] 4 14 15 11 13 14 20 5 16 19 [7,] 3 14 10 19 15 8 9 2 15 13 [8,] 4 19 8 19 4 18 14 11 5 12 [9,] 10 3 13 3 14 14 5 1 13 10 [10,] 18 8 5 7 15 18 16 6 14 3
示例
M9<-matrix(sample(50:60,36,replace=TRUE),ncol=6) M9
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [1,] 56 54 51 55 57 59 [2,] 54 50 56 54 56 57 [3,] 50 60 50 56 51 54 [4,] 53 55 50 55 60 57 [5,] 53 58 51 55 59 59 [6,] 58 52 50 56 57 56
示例
M10<-matrix(sample(1000:2000,64,replace=TRUE),ncol=8) M10
输出结果
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 1497 1437 1338 1272 1235 1339 1422 1923 [2,] 1416 1008 1843 1804 1678 1314 1355 1258 [3,] 1440 1525 1592 1673 1207 1295 1723 1028 [4,] 1863 1523 1033 1060 1388 1321 1724 1464 [5,] 1852 1874 1630 1490 1858 1454 1844 1366 [6,] 1188 1304 1712 1445 1037 1390 1617 1712 [7,] 1904 1842 1545 1859 1578 1023 1298 1131 [8,] 1154 1577 1716 1005 1350 1695 1542 1243