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 0.7171917 -0.16440417 2.7076926 #> 2 2 1 1.5872296 -0.16440417 4.0497225 #> 3 3 1 -0.7289360 -0.16440417 2.8124829 #> 4 4 1 0.6003483 -0.16440417 1.6454961 #> 5 5 1 0.5163209 -0.16440417 2.8399589 #> 6 6 1 0.2563833 -0.16440417 3.0883100 #> 7 7 1 -0.7781445 -0.16440417 3.0922434 #> 8 8 1 1.1424562 -0.16440417 3.7438529 #> 9 9 1 0.1711736 -0.16440417 1.9164980 #> 10 10 1 1.0828005 -0.16440417 2.1094020 #> 11 1 2 0.7171917 0.37636537 6.5942167 #> 12 2 2 1.5872296 0.37636537 3.8486785 #> 13 3 2 -0.7289360 0.37636537 3.4411992 #> 14 4 2 0.6003483 0.37636537 5.3235771 #> 15 5 2 0.5163209 0.37636537 4.2771207 #> 16 6 2 0.2563833 0.37636537 3.3479907 #> 17 7 2 -0.7781445 0.37636537 2.7002602 #> 18 8 2 1.1424562 0.37636537 4.1520841 #> 19 9 2 0.1711736 0.37636537 5.1249236 #> 20 10 2 1.0828005 0.37636537 4.4809776 #> 21 1 3 0.7171917 -0.82422093 3.2861012 #> 22 2 3 1.5872296 -0.82422093 2.8786693 #> 23 3 3 -0.7289360 -0.82422093 1.9902052 #> 24 4 3 0.6003483 -0.82422093 2.0458923 #> 25 5 3 0.5163209 -0.82422093 1.5216236 #> 26 6 3 0.2563833 -0.82422093 3.3659935 #> 27 7 3 -0.7781445 -0.82422093 2.9983831 #> 28 8 3 1.1424562 -0.82422093 3.4864340 #> 29 9 3 0.1711736 -0.82422093 2.7199936 #> 30 10 3 1.0828005 -0.82422093 4.6086848 #> 31 1 4 0.7171917 -0.91128136 1.6789966 #> 32 2 4 1.5872296 -0.91128136 5.6968101 #> 33 3 4 -0.7289360 -0.91128136 -1.0487718 #> 34 4 4 0.6003483 -0.91128136 2.0597989 #> 35 5 4 0.5163209 -0.91128136 2.3732315 #> 36 6 4 0.2563833 -0.91128136 4.2745678 #> 37 7 4 -0.7781445 -0.91128136 1.8469632 #> 38 8 4 1.1424562 -0.91128136 4.1014591 #> 39 9 4 0.1711736 -0.91128136 1.0318854 #> 40 10 4 1.0828005 -0.91128136 4.2240532 #> 41 1 5 0.7171917 1.66678147 5.0273318 #> 42 2 5 1.5872296 1.66678147 3.5821303 #> 43 3 5 -0.7289360 1.66678147 5.4538830 #> 44 4 5 0.6003483 1.66678147 7.9807309 #> 45 5 5 0.5163209 1.66678147 3.7473337 #> 46 6 5 0.2563833 1.66678147 7.1439931 #> 47 7 5 -0.7781445 1.66678147 4.8426279 #> 48 8 5 1.1424562 1.66678147 6.6205912 #> 49 9 5 0.1711736 1.66678147 6.7093920 #> 50 10 5 1.0828005 1.66678147 4.3002833 #> 51 1 6 0.7171917 -0.32253158 3.5070761 #> 52 2 6 1.5872296 -0.32253158 4.5057448 #> 53 3 6 -0.7289360 -0.32253158 0.9024044 #> 54 4 6 0.6003483 -0.32253158 3.9435972 #> 55 5 6 0.5163209 -0.32253158 4.3432880 #> 56 6 6 0.2563833 -0.32253158 1.5616119 #> 57 7 6 -0.7781445 -0.32253158 1.5701492 #> 58 8 6 1.1424562 -0.32253158 1.8015241 #> 59 9 6 0.1711736 -0.32253158 2.9668905 #> 60 10 6 1.0828005 -0.32253158 4.5541003 #> 61 1 7 0.7171917 0.06766548 3.6839723 #> 62 2 7 1.5872296 0.06766548 3.5298722 #> 63 3 7 -0.7289360 0.06766548 1.3879482 #> 64 4 7 0.6003483 0.06766548 3.0134067 #> 65 5 7 0.5163209 0.06766548 3.8805034 #> 66 6 7 0.2563833 0.06766548 2.8650564 #> 67 7 7 -0.7781445 0.06766548 3.9055132 #> 68 8 7 1.1424562 0.06766548 5.1346467 #> 69 9 7 0.1711736 0.06766548 2.2637057 #> 70 10 7 1.0828005 0.06766548 3.8728360 #> 71 1 8 0.7171917 -0.41971766 4.1334290 #> 72 2 8 1.5872296 -0.41971766 4.1028115 #> 73 3 8 -0.7289360 -0.41971766 2.0302724 #> 74 4 8 0.6003483 -0.41971766 2.8500859 #> 75 5 8 0.5163209 -0.41971766 1.2627937 #> 76 6 8 0.2563833 -0.41971766 2.0276961 #> 77 7 8 -0.7781445 -0.41971766 0.9953581 #> 78 8 8 1.1424562 -0.41971766 4.0599506 #> 79 9 8 0.1711736 -0.41971766 3.1317760 #> 80 10 8 1.0828005 -0.41971766 3.4296845 #> 81 1 9 0.7171917 -0.09253167 2.7561118 #> 82 2 9 1.5872296 -0.09253167 4.7486546 #> 83 3 9 -0.7289360 -0.09253167 0.9585535 #> 84 4 9 0.6003483 -0.09253167 3.6791317 #> 85 5 9 0.5163209 -0.09253167 2.7224103 #> 86 6 9 0.2563833 -0.09253167 2.6014336 #> 87 7 9 -0.7781445 -0.09253167 0.6113194 #> 88 8 9 1.1424562 -0.09253167 5.3929322 #> 89 9 9 0.1711736 -0.09253167 3.5604510 #> 90 10 9 1.0828005 -0.09253167 3.8776255 #> 91 1 10 0.7171917 1.38999715 5.8347359 #> 92 2 10 1.5872296 1.38999715 6.5293892 #> 93 3 10 -0.7289360 1.38999715 4.4470914 #> 94 4 10 0.6003483 1.38999715 5.8856564 #> 95 5 10 0.5163209 1.38999715 4.7294257 #> 96 6 10 0.2563833 1.38999715 5.8323700 #> 97 7 10 -0.7781445 1.38999715 3.5409873 #> 98 8 10 1.1424562 1.38999715 6.7181566 #> 99 9 10 0.1711736 1.38999715 4.5778140 #> 100 10 10 1.0828005 1.38999715 4.5562784