simdf_mixed.Rdsimdf_mixed Produces a dataframe with the same distributions of by-subject and by-item random intercepts as an existing dataframe
simdf_mixed(dat, sub_n = 100, item_n = 25, dv = 1, sub_id = 2, item_id = 3)
| dat | the existing dataframe |
|---|---|
| sub_n | the number of subjects to simulate |
| item_n | the number of items to simulate |
| dv | the column name or index containing the DV |
| sub_id | the column name or index for the subject IDs |
| item_id | the column name or index for the item IDs |
tibble
simdf_mixed(faceratings, 10, 10, "rating", "rater_id", "face_id")#> sub_id item_id sub_i item_i dv #> 1 1 1 0.15336871 0.1152507 3.4263347 #> 2 2 1 -0.07546968 0.1152507 2.6552210 #> 3 3 1 -0.09464565 0.1152507 2.5473771 #> 4 4 1 0.13680669 0.1152507 4.5736075 #> 5 5 1 0.46710613 0.1152507 4.6470300 #> 6 6 1 0.71713231 0.1152507 5.0470723 #> 7 7 1 -0.16377959 0.1152507 3.0286129 #> 8 8 1 -0.03648029 0.1152507 3.0203508 #> 9 9 1 -0.44490238 0.1152507 2.4007496 #> 10 10 1 -0.50294582 0.1152507 1.4482325 #> 11 1 2 0.15336871 0.9694343 4.2646276 #> 12 2 2 -0.07546968 0.9694343 3.5117228 #> 13 3 2 -0.09464565 0.9694343 1.9205807 #> 14 4 2 0.13680669 0.9694343 3.3332175 #> 15 5 2 0.46710613 0.9694343 4.1037056 #> 16 6 2 0.71713231 0.9694343 4.8340290 #> 17 7 2 -0.16377959 0.9694343 4.3931885 #> 18 8 2 -0.03648029 0.9694343 3.5224828 #> 19 9 2 -0.44490238 0.9694343 2.7403387 #> 20 10 2 -0.50294582 0.9694343 4.9209943 #> 21 1 3 0.15336871 -0.1809222 2.3955324 #> 22 2 3 -0.07546968 -0.1809222 5.0649247 #> 23 3 3 -0.09464565 -0.1809222 3.0032075 #> 24 4 3 0.13680669 -0.1809222 2.4135067 #> 25 5 3 0.46710613 -0.1809222 4.5656069 #> 26 6 3 0.71713231 -0.1809222 2.8650467 #> 27 7 3 -0.16377959 -0.1809222 3.6126806 #> 28 8 3 -0.03648029 -0.1809222 0.9722492 #> 29 9 3 -0.44490238 -0.1809222 0.6125408 #> 30 10 3 -0.50294582 -0.1809222 3.8909402 #> 31 1 4 0.15336871 1.0979794 5.9448229 #> 32 2 4 -0.07546968 1.0979794 3.9574235 #> 33 3 4 -0.09464565 1.0979794 4.8079004 #> 34 4 4 0.13680669 1.0979794 5.0826263 #> 35 5 4 0.46710613 1.0979794 3.3817108 #> 36 6 4 0.71713231 1.0979794 3.2445244 #> 37 7 4 -0.16377959 1.0979794 4.2746244 #> 38 8 4 -0.03648029 1.0979794 4.7967650 #> 39 9 4 -0.44490238 1.0979794 4.3737837 #> 40 10 4 -0.50294582 1.0979794 4.2923638 #> 41 1 5 0.15336871 0.2255086 5.9170173 #> 42 2 5 -0.07546968 0.2255086 2.7538325 #> 43 3 5 -0.09464565 0.2255086 3.1842397 #> 44 4 5 0.13680669 0.2255086 4.2260520 #> 45 5 5 0.46710613 0.2255086 1.5543152 #> 46 6 5 0.71713231 0.2255086 3.9556215 #> 47 7 5 -0.16377959 0.2255086 3.8447343 #> 48 8 5 -0.03648029 0.2255086 4.5385200 #> 49 9 5 -0.44490238 0.2255086 2.6456965 #> 50 10 5 -0.50294582 0.2255086 2.2025837 #> 51 1 6 0.15336871 -0.5688417 3.5177725 #> 52 2 6 -0.07546968 -0.5688417 2.9838297 #> 53 3 6 -0.09464565 -0.5688417 3.5923231 #> 54 4 6 0.13680669 -0.5688417 3.1022826 #> 55 5 6 0.46710613 -0.5688417 2.7535176 #> 56 6 6 0.71713231 -0.5688417 5.4940446 #> 57 7 6 -0.16377959 -0.5688417 2.5234260 #> 58 8 6 -0.03648029 -0.5688417 0.3511046 #> 59 9 6 -0.44490238 -0.5688417 1.8498874 #> 60 10 6 -0.50294582 -0.5688417 3.5379786 #> 61 1 7 0.15336871 -0.1003512 4.2409599 #> 62 2 7 -0.07546968 -0.1003512 3.5556705 #> 63 3 7 -0.09464565 -0.1003512 2.6899611 #> 64 4 7 0.13680669 -0.1003512 2.0439883 #> 65 5 7 0.46710613 -0.1003512 5.0354057 #> 66 6 7 0.71713231 -0.1003512 3.5521496 #> 67 7 7 -0.16377959 -0.1003512 2.3525066 #> 68 8 7 -0.03648029 -0.1003512 3.5415480 #> 69 9 7 -0.44490238 -0.1003512 4.1344729 #> 70 10 7 -0.50294582 -0.1003512 2.3269392 #> 71 1 8 0.15336871 1.6989795 6.4250556 #> 72 2 8 -0.07546968 1.6989795 5.7450023 #> 73 3 8 -0.09464565 1.6989795 3.8872425 #> 74 4 8 0.13680669 1.6989795 4.5973502 #> 75 5 8 0.46710613 1.6989795 5.7349937 #> 76 6 8 0.71713231 1.6989795 2.6852695 #> 77 7 8 -0.16377959 1.6989795 3.7919020 #> 78 8 8 -0.03648029 1.6989795 4.7810577 #> 79 9 8 -0.44490238 1.6989795 5.1999969 #> 80 10 8 -0.50294582 1.6989795 5.4375538 #> 81 1 9 0.15336871 1.2452224 5.3180720 #> 82 2 9 -0.07546968 1.2452224 2.7003936 #> 83 3 9 -0.09464565 1.2452224 4.3759073 #> 84 4 9 0.13680669 1.2452224 6.5338902 #> 85 5 9 0.46710613 1.2452224 3.8544295 #> 86 6 9 0.71713231 1.2452224 5.2503336 #> 87 7 9 -0.16377959 1.2452224 4.6527757 #> 88 8 9 -0.03648029 1.2452224 3.6883031 #> 89 9 9 -0.44490238 1.2452224 2.8378979 #> 90 10 9 -0.50294582 1.2452224 4.8626384 #> 91 1 10 0.15336871 -0.2064970 4.7460439 #> 92 2 10 -0.07546968 -0.2064970 1.8389187 #> 93 3 10 -0.09464565 -0.2064970 1.3021962 #> 94 4 10 0.13680669 -0.2064970 2.4226626 #> 95 5 10 0.46710613 -0.2064970 3.0711292 #> 96 6 10 0.71713231 -0.2064970 2.2198113 #> 97 7 10 -0.16377959 -0.2064970 0.9575364 #> 98 8 10 -0.03648029 -0.2064970 2.6135275 #> 99 9 10 -0.44490238 -0.2064970 1.2924662 #> 100 10 10 -0.50294582 -0.2064970 0.8947152