How to generate a normal random vector using the mean of a vector in R?

R ProgrammingServer Side ProgrammingProgramming

To create a normal random vector, we can use rnorm function with mean and standard deviation as well as without passing these arguments. If we have a different vector derived from another distribution or simply represent some numbers then we can use the mean of that vector in the rnorm function for mean argument.

Example

 Live Demo

set.seed(101)
x1<-rpois(100,5)
x1

Output

[1] 4 2 6 6 3 4 5 4 6 5 8 6 6 8 5 5 7 3 4 1 6 9 3 6 8
[26] 7 2 4 4 6 4 4 3 3 5 8 3 7 1 8 4 5 6 4 4 3 2 8 7 2
[51] 2 4 6 6 5 7 6 5 6 5 7 7 5 11 7 6 1 4 8 9 3 9 5 7 7
[76] 4 2 3 6 9 6 7 7 10 1 9 4 4 3 1 6 8 6 3 3 7 4 3 2 2

Example

Sample1<-rnorm(50,mean(x1))
Sample1

Output

[1] 3.969745 4.845529 5.697901 3.723097 5.869058 4.068813 5.285381 6.249809
[9] 6.293722 4.692137 4.860198 3.708827 4.478642 5.232458 5.542604 5.506835
[17] 4.432202 5.268902 5.062350 5.045177 6.629897 6.739937 6.273158 5.042396
[25] 3.301065 4.082555 5.422492 3.842054 5.258339 5.069016 6.972148 6.231675
[33] 4.608625 4.576119 3.391073 5.590750 5.125387 6.468046 5.844097 6.672549
[41] 6.445470 5.085735 4.758987 4.399835 5.402015 4.329474 4.675095 6.484993
[49] 5.617454 4.305604

Example

 Live Demo

x2<-runif(40,2,5)
x2

Output

[1] 3.236019 2.943532 4.886012 4.174843 4.094289 3.075312 2.827839 4.304467
[9] 2.326852 4.612022 2.986805 4.847419 3.605988 4.678886 4.022470 2.406397
[17] 4.327018 3.053227 3.050786 3.685067 3.890822 4.389564 2.951477 3.848331
[25] 4.401844 3.014718 2.465123 2.022005 2.403867 4.456938 4.659330 4.275964
[33] 4.288265 4.511266 2.694515 3.961114 2.702145 2.339766 4.894741 2.344851

Example

Sample2<-rnorm(50,mean(x2))
Sample2

Output

[1] 2.7645665 2.6537541 1.4076983 2.8925864 3.8609001 2.6550045 2.6722310
[8] 3.1268140 5.0901382 2.1818420 2.6430374 3.6710024 3.2822846 1.8175404
[15] 4.3777518 1.6070401 5.2098435 5.3133112 4.9168906 3.0455723 2.5167968
[22] 3.0880777 2.0142883 3.6437147 4.0962980 3.1383124 3.8954753 2.9561004
[29] 3.1869059 2.9787093 2.8073421 3.6791349 2.6728036 4.1584487 3.0235851
[36] 0.6031717 3.8891456 5.5590415 3.1556851 3.6746972 3.4642883 3.8202408
[43] 1.9220866 1.8392810 2.4990537 4.2023688 2.6454874 2.1476048 3.4247437
[50] 1.5751937

Example

 Live Demo

x3<-rexp(40,3.5)
x3

Output

[1] 0.340070611 0.060874495 0.437249688 0.451933591 0.501142156 0.121902693
[7] 0.164040205 0.910057958 0.058536057 0.144731409 0.470393085 0.487080705
[13] 0.164886282 0.254101261 0.465869002 0.006060842 0.002589951 0.290490088
[19] 0.107732895 0.190234003 0.055386172 0.046035375 0.316864801 0.164904071
[25] 0.332742858 0.083107876 0.012238640 0.635069982 0.035221374 0.674091355
[31] 0.113435488 1.376133524 0.326500018 0.264186670 0.130493013 0.293179982
[37] 0.954143275 0.078153099 0.346049862 0.081041369

Example

Sample3<-rnorm(50,mean(x3))
Sample3

Output

[1] 0.82932733 0.43281739 -0.73718085 0.75001763 -0.27458596 1.08118060
[7] -0.65670267 0.70135351 0.98586218 -2.49283423 0.70378079 0.10228658
[13] 0.61340795 0.95991848 0.27352495 -1.11108685 0.18902158 -1.18286719
[19] 0.39360739 0.11334012 1.36230597 1.45772059 -0.29650409 1.59499517
[25] -0.81136649 0.86194355 0.54641710 1.54240538 1.18082602 3.09350532
[31] -0.44871342 -0.44068343 0.51672887 0.83014588 1.18188996 0.35880207
[37] -0.08949876 -0.01457706 0.20724582 -1.36114868 2.21332679 0.34850885
[43] 1.11953554 0.14462373 0.15050421 -0.79027087 1.49768851 -0.56139687
[49] 0.36863964 0.10356583

Example

 Live Demo

x4<-sample(0:9,100,replace=TRUE)
x4

Output

[1] 0 1 8 1 3 5 0 5 6 8 2 6 0 7 6 2 4 6 3 1 3 5 5 0 7 2 6 9 0 1 5 7 2 0 3 4 2
[38] 4 7 2 0 2 9 3 4 4 7 3 7 9 3 9 8 8 6 4 4 4 8 3 7 2 9 6 3 3 9 4 6 3 0 5 3 0
[75] 4 4 5 2 5 6 5 8 8 6 3 5 6 1 0 0 5 7 5 8 8 4 1 6 2 9

Example

Sample4<-rnorm(50,mean(x4))
Sample4

Output

[1] 4.836175 3.963319 4.496714 5.130524 7.196743 5.152192 5.675204 3.859852
[9] 5.403791 3.860237 4.916302 5.928370 4.343460 4.875806 5.749838 3.623455
[17] 3.936333 2.705256 3.892078 4.226458 4.289463 2.518079 2.758168 5.049665
[25] 4.751905 3.784941 3.238820 4.634881 6.190992 3.850284 4.071455 4.349709
[33] 3.965399 5.825253 6.392003 4.545963 5.234481 3.671405 2.777317 3.708514
[41] 3.659098 3.774697 5.156098 6.222536 3.956885 4.573353 4.499367 5.852005
[49] 3.917576 3.444557

Example

 Live Demo

x5<-sample(1001:9999,100,replace=TRUE)
x5

Output

[1] 9506 5004 1216 6114 4258 7686 3782 5827 4960 1054 2371 4178 3991 1668 9233
[16] 8990 8994 3284 9370 2713 4549 5747 2770 6757 6832 7635 1561 1659 2521 8928
[31] 8147 4741 9194 7724 7071 4758 5936 6833 3098 1596 6216 5024 6129 1785 8970
[46] 9457 2030 9202 9774 7865 7689 5187 9128 4633 4163 6979 2454 8299 1797 6964
[61] 2311 4397 4796 7164 5405 7905 6160 3061 6808 5207 4678 4937 6542 9713 8927
[76] 9087 8915 3996 9207 8813 1694 7151 2818 6583 3242 6414 7717 7532 7491 4300
[91] 2070 3141 2519 7315 9490 5158 8735 3187 7588 1760

Example

Sample5<-rnorm(50,mean(x5))
Sample5

Output

[1] 5835.694 5837.989 5837.878 5836.064 5838.504 5837.036 5836.654 5837.286
[9] 5836.040 5837.026 5837.058 5835.924 5837.195 5836.200 5835.307 5836.525
[17] 5837.052 5836.925 5836.167 5836.521 5837.745 5837.453 5835.681 5836.634
[25] 5837.442 5835.078 5836.910 5835.975 5836.866 5835.711 5836.928 5838.439
[33] 5837.929 5836.361 5835.677 5836.355 5835.905 5836.094 5834.243 5836.918
[41] 5837.966 5837.005 5838.369 5836.746 5837.065 5836.915 5836.037 5835.473
[49] 5837.818 5835.947
raja
Published on 10-Oct-2020 12:31:18
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