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 1.28418797 -0.896772204 -1.05711472 -0.94536832
#> 2 0.59865732 -0.856672782 -0.62006584 -1.13584848
#> 3 0.66322948 -0.873537287 -0.65837825 -1.00446263
#> 4 -0.17981303 0.364356467 -0.88200631 -1.73024328
#> 5 2.42943571 -0.717186307 0.49996133 -0.42026777
#> 6 0.32472389 -0.566624732 0.35010464 0.32468722
#> 7 -0.37928344 -1.184479504 -1.37181019 -0.52435851
#> 8 0.55782430 -0.679075294 -0.02320069 -0.29678589
#> 9 -0.60083693 -0.460273708 -0.99328117 -0.78444843
#> 10 0.54203292 -0.147266134 -0.81048833 0.06276401
#> 11 0.12425119 -1.351414527 -2.66065176 -0.95854098
#> 12 0.84275776 -1.265815280 -1.00651137 1.35683703
#> 13 -0.02156442 -1.443984815 -1.71704717 -1.58620646
#> 14 -0.94883804 -1.557843345 -1.39506490 -2.03086532
#> 15 0.02976575 -0.990231586 1.40651705 0.61024416
#> 16 0.49547879 -0.833884184 -1.03426726 -0.86308587
#> 17 1.88656425 0.304495086 -0.42491646 0.71903284
#> 18 -0.07170397 -1.530065008 -0.36997705 -0.93166031
#> 19 -0.13471209 -2.297740562 -0.86766344 -0.81487368
#> 20 -0.58669397 -1.844194075 -1.02937285 -1.72321245
#> 21 -1.67366563 -0.779186336 -3.32468125 -1.14401505
#> 22 -0.97405730 -2.144417092 -2.12628559 -1.04066855
#> 23 1.16460266 -0.643100573 1.03362317 0.35747781
#> 24 0.20422105 -0.949750709 -0.59790198 -0.97247911
#> 25 -0.74385283 -0.560218608 0.68426648 -0.80796721
#> 26 2.77584201 0.791256282 -1.12097790 0.65454513
#> 27 0.58245069 -0.262060240 -1.15636500 -1.36320182
#> 28 0.24361884 -1.218511104 -0.12132492 -0.24399813
#> 29 0.48328025 -0.396757748 0.68965528 -0.43705964
#> 30 0.05996359 0.028153443 -1.35586400 -0.47045456
#> 31 1.82174074 -0.806240922 1.46642645 -0.20908694
#> 32 0.70572728 1.020860483 -0.14057880 -1.04657372
#> 33 -0.56267182 0.523268115 0.46312879 -0.54553594
#> 34 1.78121113 -1.094811052 -0.13392738 0.76467795
#> 35 2.15231942 0.634433428 0.26749275 -0.46768424
#> 36 1.22975592 0.349642366 2.56132121 -0.63271983
#> 37 2.31081379 -0.915590740 -1.94903813 -0.14148807
#> 38 -0.37937904 0.778388013 -0.19317508 -1.25092918
#> 39 -0.59958448 0.362434694 -0.03975314 -1.04647730
#> 40 2.02768556 0.645393873 -0.20666962 -0.32272418
#> 41 -0.31835461 0.207793224 0.59519010 0.02213162
#> 42 1.10161445 0.273861705 -0.66343222 0.53622685
#> 43 -0.90503264 -0.871664866 -0.67486007 -0.63704391
#> 44 1.31251381 1.083433179 0.20307864 0.69144016
#> 45 -0.56672529 -2.868036283 -2.98383250 -1.34536891
#> 46 -2.11079394 -3.143866181 -2.76951667 -2.01914435
#> 47 1.34788385 2.009882734 0.73775177 0.44494631
#> 48 -1.23890972 -0.421620513 -0.89429128 -0.81427853
#> 49 0.93820490 -0.810485202 -1.02775366 0.92883391
#> 50 -0.08491062 -0.346483585 -0.08616497 -1.48433900
#> 51 1.66618642 1.196910875 -0.48312575 1.41927921
#> 52 -1.37859418 -2.860463745 -2.52624324 -1.19599637
#> 53 1.91521131 0.629205658 -0.92623453 0.06860749
#> 54 0.97907757 0.642386664 -0.40284999 -0.24128148
#> 55 0.90496824 -0.120403170 1.02608152 0.88634915
#> 56 0.68956295 0.725397586 -0.67494824 1.61501320
#> 57 -0.70873165 -0.896043305 0.86077607 -2.38943647
#> 58 -0.40891875 -0.571339395 -1.83818039 -0.15522748
#> 59 1.32290666 -0.313158694 -1.05916489 -0.41558368
#> 60 0.18377312 -2.568011415 -0.55491480 -1.52389549
#> 61 -1.23363293 -1.856311396 -1.75310892 -1.97111713
#> 62 0.52806846 0.202597278 -0.14824267 -0.38776931
#> 63 1.19015522 -1.164204312 0.15580117 -0.79517586
#> 64 0.07729300 -0.693387718 -0.39606714 -1.67933907
#> 65 1.65880112 -0.359787707 0.48338043 -0.75010300
#> 66 1.24886975 1.567334879 0.34552044 -0.18717023
#> 67 0.70444784 0.469493622 0.41889017 -0.01608539
#> 68 -0.10176294 0.519476274 0.10759790 0.53803607
#> 69 1.15431466 -2.184708931 -0.89805141 -0.61204695
#> 70 0.13971650 -1.814020684 0.65790929 -0.66791993
#> 71 1.56247214 -0.436240750 1.23714901 0.61415420
#> 72 2.02646921 0.381337694 -0.64498930 0.09716317
#> 73 1.66093805 0.068424720 1.29988502 -0.82439648
#> 74 0.16406980 -1.019734838 -0.95390601 0.03328547
#> 75 2.52986207 1.208178113 0.79004808 1.04552151
#> 76 0.37259036 -0.023516012 0.69659107 -0.75372873
#> 77 -0.55643360 -2.277921737 -1.65769407 -2.03300780
#> 78 0.89419716 0.645638873 0.07086938 0.10015633
#> 79 -0.94243125 1.031220993 2.10211899 0.96038785
#> 80 0.27545212 -0.701977642 -1.44685073 -0.13028936
#> 81 -1.31264768 -0.916456065 -1.30124711 -1.66637276
#> 82 1.18791707 2.265388065 1.98254043 0.49087118
#> 83 0.42852002 0.470506535 0.66584722 1.00840117
#> 84 1.47006122 0.047119386 -0.21663204 0.14121423
#> 85 1.26456450 0.048985103 0.76022646 0.82281287
#> 86 1.17355070 0.061084544 -0.36278769 1.03012137
#> 87 -0.38235179 -0.531498685 -0.17756311 -2.23207029
#> 88 0.66271124 -0.267077230 -1.18401654 0.29200087
#> 89 -0.33603234 -2.021233675 -1.46699450 -1.36100594
#> 90 1.03641392 0.556131323 -0.22567656 -0.44938340
#> 91 -0.21715314 -1.215333005 -1.53039149 -2.09242957
#> 92 2.48493102 -0.431097331 1.67747648 1.72127067
#> 93 -0.72644261 -2.653636510 -1.21255876 -1.27886612
#> 94 1.11066684 0.657073969 -0.63448848 -1.01021476
#> 95 0.78973706 0.007709657 -1.05480218 1.11351087
#> 96 0.18965931 -0.715569353 -0.60647560 -0.98647920
#> 97 -0.70332099 -0.578942497 -0.78447014 0.10228607
#> 98 0.23917113 0.344323732 -1.13300319 0.40453889
#> 99 1.77368415 -0.344474840 -1.15778851 -0.18391161
#> 100 -1.04301491 -1.383488765 -0.55977904 -1.60522376