Draw multivariate random variables
draw_multivariate(formula, sep = "_")Arguments
- formula
Formula describing the multivariate draw. The lefthand side is the names or prefix and the right-hand side is the multivariate draw function call, such as mvrnorm from the MASS library or rmnom from the extraDistr library.
- sep
Separator string between prefix and variable number. Only used when a single character string is provided and multiple variables created.
Value
tibble
Examples
library(MASS)
# draw from multivariate normal distribution
dat <-
draw_multivariate(c(Y_1, Y_2) ~ mvrnorm(
n = 500,
mu = c(0, 0),
Sigma = matrix(c(1, 0.5, 0.5, 1), 2, 2)
))
cor(dat)
#> Y_1 Y_2
#> Y_1 1.0000000 0.4569785
#> Y_2 0.4569785 1.0000000
# equivalently, you can provide a prefix for the variable names
# (easier if you have many variables)
draw_multivariate(Y ~ mvrnorm(
n = 5,
mu = c(0, 0),
Sigma = matrix(c(1, 0.5, 0.5, 1), 2, 2)
))
#> Y_1 Y_2
#> 1 1.9483802 0.4971360
#> 2 -0.9623803 -0.2275308
#> 3 0.8766591 -0.5132523
#> 4 -0.5009920 0.6545170
#> 5 -1.0706870 0.3176628
# within fabricate
fabricate(
N = 100,
draw_multivariate(c(Y_1, Y_2) ~ mvrnorm(
n = N,
mu = c(0, 0),
Sigma = matrix(c(1, 0.5, 0.5, 1), 2, 2)
))
)
#> ID Y_1 Y_2
#> 1 001 -1.75838747 -0.305394817
#> 2 002 0.49512035 1.364335415
#> 3 003 -0.26760296 -0.598114346
#> 4 004 -1.34498504 -1.504375238
#> 5 005 0.13387904 0.773988012
#> 6 006 1.52543408 0.887352422
#> 7 007 1.69029022 1.866850669
#> 8 008 0.91083407 1.853377218
#> 9 009 -0.55658289 -1.082421951
#> 10 010 -0.10185072 0.111880891
#> 11 011 -0.03237983 -0.220238446
#> 12 012 -0.26994707 0.581064117
#> 13 013 0.98085810 0.646053885
#> 14 014 -0.93961733 -0.215240915
#> 15 015 0.83916804 1.302535620
#> 16 016 -1.48389204 -1.354082369
#> 17 017 0.43427180 -0.606213174
#> 18 018 0.61419985 0.001483948
#> 19 019 0.83439453 0.551064203
#> 20 020 -0.55097226 -0.979307935
#> 21 021 0.39053937 1.211186112
#> 22 022 -0.42529168 -0.222451027
#> 23 023 -0.14747518 -0.472360605
#> 24 024 0.40526767 0.158497638
#> 25 025 0.69828273 -0.268442961
#> 26 026 -1.29927217 -1.774079751
#> 27 027 2.58837956 1.255935721
#> 28 028 -0.78130885 -0.435619129
#> 29 029 -0.06987219 -1.064375070
#> 30 030 -1.54893087 0.774766171
#> 31 031 0.43337965 0.284874908
#> 32 032 -1.58868508 -0.375124661
#> 33 033 0.21523188 0.799686446
#> 34 034 -0.22498070 0.562488688
#> 35 035 1.01254563 2.730540080
#> 36 036 1.15168407 0.890724114
#> 37 037 1.19930025 0.966017966
#> 38 038 -0.34546300 -0.631989079
#> 39 039 0.21551936 -1.307912362
#> 40 040 0.47165985 1.048184397
#> 41 041 -1.03632864 -1.608002683
#> 42 042 0.37264174 1.433496345
#> 43 043 1.11651348 1.997850353
#> 44 044 -1.51880842 0.360049892
#> 45 045 0.32621180 -0.382636299
#> 46 046 -1.74147276 -1.745295633
#> 47 047 -0.12774006 -0.100337709
#> 48 048 -0.98998069 0.042970384
#> 49 049 -0.84593133 -0.411202822
#> 50 050 -0.43645846 -0.867263019
#> 51 051 0.16259443 -1.033140208
#> 52 052 -1.33917119 0.080492079
#> 53 053 -0.53586487 0.265402168
#> 54 054 -0.02204544 0.678204919
#> 55 055 1.67661240 0.447316770
#> 56 056 1.49903686 1.679946396
#> 57 057 -0.99501363 0.124265816
#> 58 058 0.33707337 0.736599701
#> 59 059 1.29982863 -0.160419216
#> 60 060 -0.44430964 0.103239064
#> 61 061 0.56635929 0.337858766
#> 62 062 -0.21509810 -0.051255177
#> 63 063 0.67783531 -0.286888430
#> 64 064 -0.17297777 0.481541834
#> 65 065 -1.30592831 0.362810338
#> 66 066 0.71233015 -0.726660355
#> 67 067 -0.74632616 0.603966350
#> 68 068 0.28283946 -0.351898374
#> 69 069 -0.29818469 0.102708007
#> 70 070 0.03009696 1.484306501
#> 71 071 -1.45906388 -1.559686917
#> 72 072 0.96260329 0.069022259
#> 73 073 -0.04769567 -1.953496739
#> 74 074 -0.44414372 -1.266562115
#> 75 075 2.10303292 2.701721250
#> 76 076 -1.27046947 -0.924007910
#> 77 077 -0.15026232 0.746869506
#> 78 078 -1.64809006 -0.925525523
#> 79 079 0.73775670 1.554536363
#> 80 080 0.40162060 -0.629337231
#> 81 081 -0.95618238 -0.718509329
#> 82 082 -0.12031536 -0.014843661
#> 83 083 -0.16779625 -0.421299868
#> 84 084 0.01639729 1.100302554
#> 85 085 -0.05787954 -0.561509907
#> 86 086 -0.36592027 -0.393480803
#> 87 087 0.02819025 -0.811578843
#> 88 088 -0.15246468 -0.397155020
#> 89 089 0.26419753 -0.804814993
#> 90 090 -2.03661020 -0.023147110
#> 91 091 -1.11839879 1.109248771
#> 92 092 -0.18735188 -0.074416212
#> 93 093 0.03640982 -0.320149922
#> 94 094 0.14930430 -0.754382219
#> 95 095 1.05762048 -0.100302843
#> 96 096 -0.09755452 0.014884445
#> 97 097 0.14358317 -1.424726414
#> 98 098 0.51396723 1.639998242
#> 99 099 -0.41867569 -1.201465103
#> 100 100 -1.69785721 -0.361777662
# You can also write the following, which works but gives less control over the names
fabricate(N = 100,
Y = mvrnorm(
n = N,
mu = c(0, 0),
Sigma = matrix(c(1, 0.5, 0.5, 1), 2, 2)
))
#> ID Y.1 Y.2
#> 1 001 -0.625563348 0.016467891
#> 2 002 -1.517829236 1.072828735
#> 3 003 1.219073753 0.551312640
#> 4 004 2.662330818 2.546616392
#> 5 005 0.864515677 -1.077347224
#> 6 006 -1.178485308 -1.402220628
#> 7 007 0.963679209 1.741132173
#> 8 008 0.523042625 0.257666565
#> 9 009 -1.586545786 -0.481157521
#> 10 010 -1.390667838 -0.869944877
#> 11 011 1.960222036 0.898188661
#> 12 012 0.762637885 0.258655483
#> 13 013 0.947011183 -0.755879813
#> 14 014 -1.158486355 -0.650286101
#> 15 015 -0.920031626 0.074744032
#> 16 016 0.291378464 0.957693367
#> 17 017 -0.781832013 -1.743293077
#> 18 018 0.061941855 -0.379614476
#> 19 019 1.191727506 0.028336940
#> 20 020 0.289159378 0.095851859
#> 21 021 0.138938706 -1.504462385
#> 22 022 1.084408755 1.620859839
#> 23 023 -0.336712507 -1.529159764
#> 24 024 1.525519839 1.104399458
#> 25 025 0.990527431 1.006541351
#> 26 026 0.442822320 0.061488474
#> 27 027 -0.763618994 -1.279826555
#> 28 028 -1.123112081 -0.066167013
#> 29 029 -1.509544237 -0.913966340
#> 30 030 -2.013655837 -0.187443873
#> 31 031 0.865343688 1.491999471
#> 32 032 -0.057851362 1.035657217
#> 33 033 -0.133584526 -1.895573855
#> 34 034 1.116988408 0.578477421
#> 35 035 0.078956352 0.513718114
#> 36 036 -0.027637389 -0.419349463
#> 37 037 0.052278926 0.501115043
#> 38 038 -0.846293295 0.402731787
#> 39 039 1.132347188 1.161166205
#> 40 040 -0.304337339 -1.011882660
#> 41 041 -1.740403572 -0.236330997
#> 42 042 -1.240382324 0.417922833
#> 43 043 -0.256879431 0.100137027
#> 44 044 -1.273354867 -1.134160505
#> 45 045 -2.046231558 -1.391196514
#> 46 046 1.254485586 0.223863315
#> 47 047 -0.364298247 -0.969278046
#> 48 048 -0.878747593 0.578893323
#> 49 049 2.320204747 0.659004140
#> 50 050 0.038354752 0.456332262
#> 51 051 0.417840727 -0.778248324
#> 52 052 0.026100165 0.893315023
#> 53 053 -1.098918011 -0.751233117
#> 54 054 -1.284410852 -0.977390281
#> 55 055 -1.025362566 -0.140833141
#> 56 056 1.584792803 0.863087005
#> 57 057 -0.305450712 0.184202677
#> 58 058 -1.713688139 -0.628286562
#> 59 059 -0.626779391 -0.487534353
#> 60 060 -0.928814305 -0.123136192
#> 61 061 -1.568939073 -2.244675779
#> 62 062 1.617951572 1.236480234
#> 63 063 -0.936064403 -0.002387546
#> 64 064 0.786278084 -0.331833554
#> 65 065 -0.830109435 0.334496634
#> 66 066 1.993157040 1.323527314
#> 67 067 -2.616959752 -0.678988069
#> 68 068 -0.185794500 1.427059391
#> 69 069 -0.260082914 -0.034627030
#> 70 070 1.843304742 0.692651490
#> 71 071 -1.132269846 -0.058975120
#> 72 072 0.441110698 0.345075776
#> 73 073 0.066080212 -1.101175151
#> 74 074 -0.653118698 1.465740606
#> 75 075 -2.021279759 -0.888441459
#> 76 076 0.128199636 -0.511403669
#> 77 077 0.323051036 -1.161673641
#> 78 078 -1.927962832 0.471923511
#> 79 079 0.637106739 -0.379229367
#> 80 080 0.320465730 0.531006244
#> 81 081 -1.043283114 0.568317506
#> 82 082 0.762188826 0.813255431
#> 83 083 -0.106344935 0.638090578
#> 84 084 0.458383721 1.722074965
#> 85 085 -0.082357003 0.168761589
#> 86 086 0.399432682 0.084116699
#> 87 087 0.831696862 2.253653556
#> 88 088 -0.047889172 -0.071434640
#> 89 089 -2.072020793 0.385817197
#> 90 090 1.427961557 0.898658169
#> 91 091 0.467355068 0.473640697
#> 92 092 -0.003988341 0.845583085
#> 93 093 1.167848822 -0.478686034
#> 94 094 0.299927591 -0.101372141
#> 95 095 1.253440936 0.579376220
#> 96 096 0.307669842 -2.839589558
#> 97 097 0.292034090 -0.791178084
#> 98 098 -0.901882504 0.436813853
#> 99 099 -1.600647091 0.496275784
#> 100 100 -0.188741364 -1.008424826