Generate Data from a Multivariate Normal Distribution
mvn(n, mu, Sigma)
| n | Integer. Samples size. |
|---|---|
| mu | Numeric vector. Mean vector. |
| Sigma | Numeric matrix. Variance-covariance matrix. |
Other multivariate normal data functions:
mvn_dat_simulation(),
mvn_dat_task(),
mvn_dat(),
setparamsmvn(),
useparamsmvn(),
vm_mod_dat_simulation_simulation_summary(),
vm_mod_dat_simulation_task_summary(),
vm_mod_dat_simulation(),
vm_mod_dat_task(),
vm_mod_dat(),
vm_sev_dat_simulation_simulation_summary(),
vm_sev_dat_simulation_task_summary(),
vm_sev_dat_simulation(),
vm_sev_dat_task(),
vm_sev_dat()
Ivan Jacob Agaloos Pesigan
n <- 100 mu <- c(0, 0) Sigma <- matrix(data = c(1, 0.50, 0.50, 1), ncol = 2) mvn(n = n, mu = mu, Sigma = Sigma)#> [,1] [,2] #> [1,] -0.73790083 -1.35923489 #> [2,] -0.49094551 -0.85304690 #> [3,] -0.73887704 0.08705147 #> [4,] 0.62697432 0.97169311 #> [5,] 0.24444770 1.41356391 #> [6,] -1.18114647 -2.54078618 #> [7,] -1.45996237 0.55580164 #> [8,] 0.38779309 1.08106326 #> [9,] 0.25799210 -0.55780408 #> [10,] -1.73281769 -0.64430202 #> [11,] 1.10845216 1.12052048 #> [12,] -0.95255454 -0.22850060 #> [13,] 0.75286179 -0.28846630 #> [14,] 1.05338816 0.52683304 #> [15,] -1.03563789 -2.61046271 #> [16,] 1.23636624 1.19292974 #> [17,] 0.64397520 0.51391726 #> [18,] -0.66649309 0.36585364 #> [19,] 0.59811898 0.42890735 #> [20,] 0.45978223 0.30179424 #> [21,] 0.92530191 1.34507532 #> [22,] 2.25815007 0.25021593 #> [23,] 0.08770908 1.37006258 #> [24,] -0.01449169 0.19009951 #> [25,] 0.34476511 -0.44642799 #> [26,] -0.07866596 0.16076361 #> [27,] -0.30030957 -1.43793119 #> [28,] -1.48957105 -1.26888907 #> [29,] -0.26204230 -0.68336409 #> [30,] -0.79634058 -0.70122321 #> [31,] 0.76306377 -1.19070134 #> [32,] -1.37548301 -0.66508636 #> [33,] -1.54730620 -2.61508632 #> [34,] 0.09568364 -0.64580424 #> [35,] 0.18622208 0.54840830 #> [36,] -0.34042737 0.40996472 #> [37,] 0.81102572 -0.40218359 #> [38,] -0.31292609 -0.90652615 #> [39,] 0.78012111 1.74418240 #> [40,] -0.91290871 0.01176133 #> [41,] 0.03891957 -0.67374344 #> [42,] 0.20757016 0.79750794 #> [43,] 0.17014186 0.73517046 #> [44,] -0.41328749 -1.00494950 #> [45,] -2.59485887 -1.63503679 #> [46,] -1.23545247 -0.31512369 #> [47,] -0.42863947 -0.44715632 #> [48,] 0.15141325 -1.11705670 #> [49,] 0.15304238 1.05148009 #> [50,] 1.06395386 -1.16505455 #> [51,] 0.74113930 2.19290238 #> [52,] -0.87752463 0.52802371 #> [53,] 1.37133454 2.76916876 #> [54,] 0.80670557 0.44426696 #> [55,] 1.04707041 -2.38291928 #> [56,] 0.37924283 0.88916920 #> [57,] -0.01221928 0.56127634 #> [58,] -0.10813359 -1.67183566 #> [59,] 0.66692910 0.36518913 #> [60,] -1.46916080 -0.58764896 #> [61,] -0.52454127 -1.74454783 #> [62,] -0.45314233 0.41533812 #> [63,] 0.65837477 -0.74965380 #> [64,] 1.55327338 -1.22764752 #> [65,] 0.23310374 -0.12270976 #> [66,] 1.55016685 0.04924261 #> [67,] -0.50887916 -0.42814733 #> [68,] -1.15106452 -1.47831234 #> [69,] 0.20087048 -0.16213456 #> [70,] 0.06138684 0.12833037 #> [71,] 3.18499018 -0.12966441 #> [72,] -3.45838286 -2.98950662 #> [73,] -1.18540326 -0.66624448 #> [74,] -0.09174490 0.05998626 #> [75,] -0.37965546 0.44206272 #> [76,] -0.42728989 -1.34710284 #> [77,] -0.74991889 0.75522552 #> [78,] -0.66078120 1.47239182 #> [79,] 0.93909715 -0.55002928 #> [80,] -1.61748719 -1.07363200 #> [81,] 0.11401016 -0.52963350 #> [82,] -0.50659583 -1.36347608 #> [83,] -1.56024435 -0.17580706 #> [84,] -0.67384683 0.68869381 #> [85,] -1.11967863 -0.94264401 #> [86,] 0.96089156 -0.47440280 #> [87,] -0.48182377 0.18050115 #> [88,] -0.23613091 -0.54105005 #> [89,] 1.30244298 1.50073880 #> [90,] 0.27209059 -0.15166652 #> [91,] 0.36122575 -0.58684350 #> [92,] -1.23423676 -0.53702114 #> [93,] 0.47117677 -0.16111592 #> [94,] 2.84099363 1.22644568 #> [95,] 0.76975420 2.90098098 #> [96,] 0.72442655 1.97852110 #> [97,] -0.69072534 0.14041627 #> [98,] 0.88897749 0.41308545 #> [99,] -2.40910415 -0.67465675 #> [100,] -0.09647028 -1.54913839