Generate random samples from the gamma distribution
r_g.RdThis function generates random samples from the gamma distribution.
Arguments
- lots
The number of lots
- n
The sample size
- mu
The mean of the gamma distribution
- k
The shape parameter of the gamma distribution
- ...
Additional arguments to be passed to
rgamma()from thestatspackage
Examples
r_g(lots = 5, n = 10, mu = 5, k = 2)
#> n_1 n_2 n_3 n_4 n_5 n_6 n_7
#> sample_1 5.957473 7.928890 5.464507 5.196444 8.838120 1.659009 1.2526876
#> sample_2 4.421920 2.503306 4.423087 6.198565 0.399009 7.169252 4.6145926
#> sample_3 2.371882 13.906414 5.899216 2.734700 5.414502 5.565400 0.1104306
#> sample_4 5.680469 3.320305 5.017543 6.546413 5.882522 3.288721 3.4473737
#> sample_5 7.448738 5.230865 7.288507 6.581854 7.531707 3.833780 1.6505902
#> n_8 n_9 n_10
#> sample_1 0.8348413 3.4057171 6.617045
#> sample_2 4.4130611 3.3513799 4.876379
#> sample_3 14.2938307 0.3320052 5.783699
#> sample_4 13.4970626 6.4183344 1.416673
#> sample_5 8.0355939 9.3259512 1.068377
r_g(lots = 10, n = 20, mu = 10, k = 3)
#> n_1 n_2 n_3 n_4 n_5 n_6 n_7
#> sample_1 9.470461 10.973128 3.361450 23.302114 21.696144 9.776866 9.682267
#> sample_2 16.672141 12.759295 11.643940 7.988178 1.274184 1.608469 20.057369
#> sample_3 16.703383 15.745755 10.971888 17.418835 6.435449 1.419308 4.409258
#> sample_4 4.568837 23.212204 8.099853 8.309550 20.929427 3.419891 19.034693
#> sample_5 8.834609 8.501506 16.695118 2.816933 5.172600 5.472581 8.771406
#> sample_6 20.997784 12.319499 16.906056 3.790916 12.306398 11.577292 9.612835
#> sample_7 4.976414 2.863152 8.998031 11.491319 19.815295 15.163191 5.688238
#> sample_8 3.530481 7.397948 7.851622 5.551949 14.818226 10.908829 14.372578
#> sample_9 7.372012 10.630724 10.927591 20.945807 20.654539 2.934069 3.039184
#> sample_10 8.401100 3.721737 10.527813 13.930297 13.199237 9.487278 15.909253
#> n_8 n_9 n_10 n_11 n_12 n_13
#> sample_1 13.651874 6.495899 10.4517663 4.859107 10.466220 13.753148
#> sample_2 12.111977 8.970102 11.2526856 6.804881 13.989358 3.352700
#> sample_3 14.644892 12.247765 13.4309191 14.536973 12.381193 8.158253
#> sample_4 14.595135 14.442554 17.0776484 13.022099 4.020971 7.624362
#> sample_5 15.435095 6.212757 1.6960405 10.033468 8.102398 9.870453
#> sample_6 6.567392 15.355429 4.1164122 2.905751 19.399744 14.088284
#> sample_7 3.959074 9.973281 6.8451264 12.899595 24.783073 6.406518
#> sample_8 9.129771 6.722556 3.7385312 9.863152 7.902794 5.294624
#> sample_9 8.234441 9.320469 9.1361765 4.164230 6.264210 18.293541
#> sample_10 18.253533 8.594222 0.7011864 3.473195 17.320778 8.250692
#> n_14 n_15 n_16 n_17 n_18 n_19 n_20
#> sample_1 6.475410 5.367061 18.439833 19.641567 7.737478 6.280294 8.257611
#> sample_2 4.057832 5.525591 4.374584 12.577409 14.564593 12.511782 13.835623
#> sample_3 5.227290 9.815558 5.583585 7.112826 20.398534 7.575938 8.208494
#> sample_4 8.088397 7.512834 3.814838 11.212246 12.793706 8.487088 9.933950
#> sample_5 5.991553 21.378734 17.927578 16.146793 10.571182 3.452604 9.699701
#> sample_6 15.096870 13.027301 6.346571 10.492527 10.572409 3.520232 10.517260
#> sample_7 13.120818 4.088458 11.551197 3.697391 14.755739 8.090635 10.028891
#> sample_8 3.205841 18.615816 5.030002 3.600857 3.318017 3.301137 14.255051
#> sample_9 19.703589 3.493325 5.756015 3.446601 12.204097 14.285327 7.118099
#> sample_10 5.380769 12.035494 6.477259 23.769549 13.467987 7.004121 3.323648