Simulate a dataset coming from a multivariate non-normal distribution specifying the vector of means, variance-covariance matrix, vector of skewnesses and kurtoses. The function use the semTools::mvrnonnorm() function.
Arguments
- n
number of observations
- d
the effect size (i.e., the vector of means of the multivariate distribution). If
dis a vector of length 1, the value is recycled creating a vector of lengthnrow(Sigma), otherwisedneed to be a vector of lengthSigma. The function randomize the sign ofdfor each dimension.- Sigma
the variance-covariance matrix
- skew
vector of skewnesses
- kurt
vector of kurtoses
Examples
Sigma <- gen_sigma(4, 0.5)
sim_data(100, 0.5, Sigma)
#> X1 X2 X3 X4
#> 1 0.6945013487 -0.12507322 -0.084744602 0.10309742
#> 2 -0.3828898844 -1.33385831 -0.437097779 -1.05315210
#> 3 1.0814923393 -0.38645371 0.724804412 -0.25185627
#> 4 1.6918001107 1.44675138 0.015250647 0.72239016
#> 5 -0.6021093300 -0.52978537 -0.073011332 0.36964183
#> 6 2.3725579783 1.88375320 0.166739515 1.05974036
#> 7 0.6352734344 -1.55537678 -0.497075108 -0.64424079
#> 8 0.0765336613 -0.50956835 -1.489388378 -1.24046458
#> 9 0.2222922081 -1.27343174 -1.776426002 -2.20889130
#> 10 0.4036463962 -0.53582695 -0.460563771 -1.30910749
#> 11 0.0235205467 -1.35395404 -0.404203246 -1.88127563
#> 12 2.6389529007 3.04609195 -0.035526875 1.62965450
#> 13 0.1746648906 -0.38828644 -1.810368397 0.85932690
#> 14 -0.3496375787 -1.46633367 -2.059883946 -2.18272017
#> 15 1.1746514965 0.37783645 -0.120640268 0.84872407
#> 16 2.3386021094 0.09213165 0.210431034 0.70816643
#> 17 -0.1426667935 -1.90981007 -1.849471973 -1.86632403
#> 18 0.9871465422 -0.06901940 -1.220952635 -1.16570157
#> 19 -0.5134629678 -0.79413692 -2.633165376 -1.68317549
#> 20 2.2471764995 -0.36881181 -0.945738607 0.10907292
#> 21 1.8271205330 -1.73339924 -1.391375192 -1.30985417
#> 22 -0.7322096130 -1.91084173 -0.781096154 -1.35694146
#> 23 1.8234974630 -0.36337624 1.033782893 -1.36466918
#> 24 2.0405048281 -0.03730409 -0.311523685 0.32763548
#> 25 0.8049850974 -0.58166721 -0.706354370 0.40211750
#> 26 2.0316737582 0.76383771 0.940526026 0.14216470
#> 27 2.5109493750 -0.24900665 -0.098419798 0.06019178
#> 28 -0.5469557943 -1.19279843 -0.298797799 -0.74141414
#> 29 1.1868961896 -0.46157676 -1.570637653 -0.19332706
#> 30 1.3414329085 -0.21483929 0.067578346 -0.12916267
#> 31 -0.6321191613 -1.28201022 -1.222773943 -1.11636645
#> 32 -1.3565659089 0.34043948 -0.329049094 -0.93247651
#> 33 -0.0739959337 -1.14830977 -0.236060691 -0.97084941
#> 34 -1.5938550306 -0.55425783 -0.208465510 -1.47698439
#> 35 3.3326545740 -0.31135888 -0.851220665 1.12513216
#> 36 -1.4514998175 -1.41982235 -2.634283750 -2.08719254
#> 37 1.5194212236 -0.48521273 -0.871573595 -1.81464032
#> 38 0.7955797805 -0.82517414 -0.554699811 -0.69957908
#> 39 1.7141529954 0.08550179 -0.592489328 -0.91131374
#> 40 -0.6028791866 -1.63904851 -0.328433489 -0.30814331
#> 41 0.0426143968 -1.11137189 -0.064904272 -0.65819826
#> 42 1.5626350472 -0.31483806 -0.670385961 -1.67346234
#> 43 2.1973948521 0.08112143 0.350290078 1.32981005
#> 44 1.0874288250 -0.40690423 0.242121039 -0.72579450
#> 45 1.0304463169 -0.63573224 -3.675435832 -1.16113882
#> 46 -0.7467360186 -2.59947055 -2.901800479 -1.47273367
#> 47 0.1577528349 -0.74458000 -1.097583482 -0.74858548
#> 48 0.8433927801 -0.24763319 0.094151410 0.27745117
#> 49 0.4860107778 -0.04043503 -0.583877141 -0.44911138
#> 50 -1.0712808781 -1.95680712 -0.173620197 -2.00829701
#> 51 0.6117463933 -0.89080767 -0.875597139 -1.33425481
#> 52 -0.4202290992 -0.98212668 -0.275510522 -1.01075466
#> 53 0.7088327391 0.36067509 1.246270243 2.07233548
#> 54 0.6308407150 -0.98519896 -1.268273509 -0.64532716
#> 55 1.6439492959 -0.41445733 -0.047066684 -0.62792205
#> 56 2.6806661984 1.53050504 0.351653371 0.46957693
#> 57 -0.9922336903 0.29153121 -0.902449050 -0.87276958
#> 58 -0.6267149154 -0.29657039 -2.175812725 -1.86602639
#> 59 -0.5086647294 -0.22611732 -1.345196851 -3.42010589
#> 60 2.3075500545 -0.51226689 -0.723559111 -0.19881817
#> 61 -0.0730584876 0.25240599 -0.409340734 -0.04641462
#> 62 2.1384635866 0.96907329 0.296770974 0.53933260
#> 63 2.4565875697 -0.73256044 1.627982854 1.51465498
#> 64 1.4948420167 0.13734344 -0.070029189 1.82591255
#> 65 -2.7502125399 -0.66762629 -1.113797752 -2.27395564
#> 66 0.2819917953 -0.81644026 -1.454618977 -1.88678240
#> 67 -0.7334834322 -1.57806538 -1.625672324 -1.01901403
#> 68 0.7860044569 -1.50519955 -1.248404826 -0.28017693
#> 69 -1.6242814933 -0.70720785 -1.068569935 -2.37159330
#> 70 0.1246862674 -0.66229362 -1.611422018 0.12857581
#> 71 0.1348743553 -1.97438628 -0.833476519 -0.76034479
#> 72 0.5625864056 0.73758784 -1.744106672 0.68091112
#> 73 0.6059885607 -0.64074153 -0.970486489 -0.59168752
#> 74 0.4336926333 -0.85854219 1.128803673 -0.49943069
#> 75 1.3867562080 1.27492861 1.182395430 -0.24047656
#> 76 0.6206307740 -0.41840023 -0.472297964 1.08241693
#> 77 -0.3741465121 -1.84109392 -1.461288967 -1.76798722
#> 78 0.9948943629 -0.02595679 0.254957171 -1.38445334
#> 79 0.7397643275 -0.22499607 -2.212720228 -0.69469737
#> 80 0.6817924695 -1.28835009 -2.075360845 -1.47261322
#> 81 2.1802761259 -0.22080196 -1.108934274 -0.31258722
#> 82 -0.0754660812 0.23673963 0.494420279 -1.15667351
#> 83 -0.1039725472 0.13932645 -1.841062125 0.64734532
#> 84 1.1517957636 -1.58428846 0.048356516 -1.03213776
#> 85 0.6497327465 1.07530652 0.928773732 1.11924865
#> 86 0.3835280855 -0.22077156 -0.649123877 0.08415271
#> 87 -0.3122574432 -1.65566439 0.181432449 -0.42665560
#> 88 -0.3787563564 -0.50060978 -0.105970619 -1.53360622
#> 89 1.7216953117 1.07530005 -0.582713872 0.41168575
#> 90 0.3479108061 -0.15068006 -0.034302124 -1.72968807
#> 91 0.5066991449 -0.02604716 -1.136000904 -0.22452172
#> 92 0.2385452904 0.85743242 0.118084741 0.10804016
#> 93 -1.1508288846 -3.13442981 -3.212423992 -1.65126130
#> 94 0.3044866259 -1.15718126 -0.510446096 -0.71117261
#> 95 0.0005102654 -0.18961885 -1.034471287 -0.60184728
#> 96 1.1551238212 0.85624417 -0.175194448 0.16148067
#> 97 -0.3574050562 -1.20128482 -0.384752977 -0.69752017
#> 98 -0.2011727958 0.04307491 0.009287984 -2.35704743
#> 99 -1.1257306831 0.16656025 -0.840130286 -0.75096658
#> 100 0.2253539465 0.07123285 -0.093428224 -0.59897297