rnorm()函数产生一系列的随机数，随机数个数，均值和标准差都可以设定。
1 > x<-rnorm(100) #产生100个服从正态分布的随机数2 > print(x)
[1] -0.26324109 0.10288996 -0.19853384 [4] 0.20795624 -0.67943297 1.10336811 [7] 0.27014386 -0.22539815 0.21058139 [10] -0.08845235 0.57193731 0.38441138 [13] -0.16234544 -1.05885749 -0.31676977 [16] 0.09160984 0.85869406 -1.92437870 [19] 0.13930256 -0.38939669 -1.30904417 [22] -1.64585501 -1.10237222 -0.78996995 [25] -0.08953180 -0.57261995 0.75944219 [28] -0.27586470 0.22038731 1.07290135 [31] 1.31221548 -1.17559017 0.44867447 [34] 0.92308930 0.28249317 0.03514011 [37] -0.49339015 -0.97298188 1.64675994 [40] 0.05560634 0.21019148 0.46795645 [43] 0.93547472 1.24787602 -0.70754604 [46] -0.53861572 1.11944711 0.68947881 [49] -0.23630802 -1.28280493 -0.70265838 [52] -0.42406630 1.56637981 0.36190251 [55] 1.60644945 0.77273024 -1.28584961 [58] -1.20758388 0.76275871 0.28845264 [61] -0.92902203 0.17398453 -0.13379084 [64] 2.19181951 -0.02141348 -0.98340831 [67] -0.98250819 0.78877798 1.31430210 [70] -1.58568841 -0.02860521 -0.03645140 [73] -0.20290850 0.21163409 0.44485326 [76] 0.75211751 0.97126478 1.55586721 [79] -1.16956405 -1.22934317 -0.16197414 [82] 0.26927615 1.79530684 -0.50801284 [85] 1.39475512 -2.44997555 0.12599863 [88] 0.34823393 0.38774490 0.99990464 [91] 0.36716384 1.99150108 -0.38290675 [94] 0.60751652 0.09480957 -0.20563194 [97] 1.71996544 0.06382987 0.19579251[100] -0.10073099
1 > x<-rnorm(100,3,4) #产生100个均值是3，标准差为4的随机数2 > print(x)
[1] 1.49656925 11.95936490 6.88970327 [4] 0.40415294 5.86416522 0.63424442 [7] 0.48301686 -0.11507020 2.78108833 [10] 6.34683598 0.41899008 4.30549109 [13] 0.05657324 9.09961354 0.50791366 [16] 9.37733170 4.48574351 -0.89857176 [19] 1.12643236 3.93898234 0.17518864 [22] -3.54634182 -4.70234252 9.82584151 [25] -1.05972911 5.81132397 8.65915568 [28] -4.70963922 4.05207848 3.86882175 [31] 3.25272474 1.64543632 -0.63657621 [34] -3.19041652 10.93314413 -0.60856151 [37] 0.47559227 8.49264500 8.93107758 [40] 0.37652898 8.30558795 5.53069155 [43] 0.68242390 4.92089359 -0.42385840 [46] 1.84038254 4.92277540 6.82399382 [49] -0.50417642 6.74601180 1.36258799 [52] 9.96709281 -3.07820065 3.10318421 [55] 3.54411733 8.52122244 0.88853265 [58] 8.57470109 -2.14551460 -0.50774596 [61] 2.84178486 3.15692093 6.10531593 [64] -0.43015779 0.06777219 7.47884137 [67] 1.72870486 7.54601723 5.40613275 [70] 5.36976037 7.36394231 1.27398026 [73] 6.32744407 9.50486546 -3.33475582 [76] 4.55947536 3.14531065 1.26117393 [79] 7.78038761 2.24518204 3.10945300 [82] -0.13109504 -6.57291074 9.51343571 [85] -2.14250267 2.60657651 12.42863819 [88] 1.50207810 4.69823542 5.07431396 [91] -0.47208321 2.71782519 -0.04013664 [94] 3.91216269 3.40533228 6.13103940 [97] 0.29818172 6.49477693 3.76956111[100] 4.10297196
