Compute predicted value of formula for linear (mixed) model
Source:R/get_prediction.R
get_prediction-method.RdCompute predicted value of formula for linear (mixed) model for with lm or lmer
Usage
get_prediction(fit, formula)
# S4 method for class 'lmerMod'
get_prediction(fit, formula)
# S4 method for class 'lm'
get_prediction(fit, formula)Details
Similar motivation as lme4:::predict.merMod(), but that function cannot use just a subset of the fixed effects: it either uses none or all. Note that the intercept is included in the formula by default. To exclude it from the prediction use ~ 0 + ... syntax
Examples
library(lme4)
# Linear model
fit <- lm(Reaction ~ Days, sleepstudy)
# prediction of intercept
get_prediction(fit, ~1)
#> 1 2 3 4 5 6 7 8
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 9 10 11 12 13 14 15 16
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 17 18 19 20 21 22 23 24
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 25 26 27 28 29 30 31 32
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 33 34 35 36 37 38 39 40
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 41 42 43 44 45 46 47 48
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 49 50 51 52 53 54 55 56
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 57 58 59 60 61 62 63 64
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 65 66 67 68 69 70 71 72
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 73 74 75 76 77 78 79 80
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 81 82 83 84 85 86 87 88
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 89 90 91 92 93 94 95 96
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 97 98 99 100 101 102 103 104
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 105 106 107 108 109 110 111 112
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 113 114 115 116 117 118 119 120
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 121 122 123 124 125 126 127 128
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 129 130 131 132 133 134 135 136
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 137 138 139 140 141 142 143 144
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 145 146 147 148 149 150 151 152
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 153 154 155 156 157 158 159 160
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 161 162 163 164 165 166 167 168
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 169 170 171 172 173 174 175 176
#> 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051 251.4051
#> 177 178 179 180
#> 251.4051 251.4051 251.4051 251.4051
# prediction of Days without intercept
get_prediction(fit, ~ 0 + Days)
#> 1 2 3 4 5 6 7 8
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 9 10 11 12 13 14 15 16
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 17 18 19 20 21 22 23 24
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 25 26 27 28 29 30 31 32
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 33 34 35 36 37 38 39 40
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 41 42 43 44 45 46 47 48
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 49 50 51 52 53 54 55 56
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 57 58 59 60 61 62 63 64
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 65 66 67 68 69 70 71 72
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 73 74 75 76 77 78 79 80
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 81 82 83 84 85 86 87 88
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 89 90 91 92 93 94 95 96
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 97 98 99 100 101 102 103 104
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 105 106 107 108 109 110 111 112
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 113 114 115 116 117 118 119 120
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 121 122 123 124 125 126 127 128
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 129 130 131 132 133 134 135 136
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 137 138 139 140 141 142 143 144
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 145 146 147 148 149 150 151 152
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 153 154 155 156 157 158 159 160
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 161 162 163 164 165 166 167 168
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 169 170 171 172 173 174 175 176
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 177 178 179 180
#> 62.80372 73.27100 83.73829 94.20557
# Linear mixed model
# fit model
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
# predict Days, but exclude intercept
get_prediction(fm1, ~ 0 + Days)
#> 1 2 3 4 5 6 7 8
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 9 10 11 12 13 14 15 16
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 17 18 19 20 21 22 23 24
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 25 26 27 28 29 30 31 32
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 33 34 35 36 37 38 39 40
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 41 42 43 44 45 46 47 48
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 49 50 51 52 53 54 55 56
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 57 58 59 60 61 62 63 64
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 65 66 67 68 69 70 71 72
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 73 74 75 76 77 78 79 80
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 81 82 83 84 85 86 87 88
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 89 90 91 92 93 94 95 96
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 97 98 99 100 101 102 103 104
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 105 106 107 108 109 110 111 112
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 113 114 115 116 117 118 119 120
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 121 122 123 124 125 126 127 128
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 129 130 131 132 133 134 135 136
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 137 138 139 140 141 142 143 144
#> 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186
#> 145 146 147 148 149 150 151 152
#> 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557 0.00000 10.46729
#> 153 154 155 156 157 158 159 160
#> 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100 83.73829 94.20557
#> 161 162 163 164 165 166 167 168
#> 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643 62.80372 73.27100
#> 169 170 171 172 173 174 175 176
#> 83.73829 94.20557 0.00000 10.46729 20.93457 31.40186 41.86914 52.33643
#> 177 178 179 180
#> 62.80372 73.27100 83.73829 94.20557
# predict Days and (Days | Subject) random effect, but exclude intercept
get_prediction(fm1, ~ 0 + Days + (Days | Subject))
#> 1 2 3 4 5 6
#> 2.2585510 21.9248127 41.5910744 61.2573361 80.9235979 100.5898596
#> 7 8 9 10 11 12
#> 120.2561213 139.9223830 159.5886448 179.2549065 -40.3987381 -38.5511327
#> 13 14 15 16 17 18
#> -36.7035274 -34.8559220 -33.0083167 -31.1607114 -29.3131060 -27.4655007
#> 19 20 21 22 23 24
#> -25.6178953 -23.7702900 -38.9604090 -33.9419795 -28.9235500 -23.9051205
#> 25 26 27 28 29 30
#> -18.8866910 -13.8682615 -8.8498320 -3.8314025 1.1870270 6.2054565
#> 31 32 33 34 35 36
#> 23.6906196 29.3435552 34.9964908 40.6494265 46.3023621 51.9552978
#> 37 38 39 40 41 42
#> 57.6082334 63.2611690 68.9141047 74.5670403 22.2603126 29.6576869
#> 43 44 45 46 47 48
#> 37.0550613 44.4524356 51.8498100 59.2471843 66.6445587 74.0419330
#> 49 50 51 52 53 54
#> 81.4393074 88.8366817 9.0395679 19.2346769 29.4297858 39.6248948
#> 55 56 57 58 59 60
#> 49.8200038 60.0151127 70.2102217 80.4053307 90.6004396 100.7955486
#> 61 62 63 64 65 66
#> 16.8405086 27.0841585 37.3278084 47.5714582 57.8151081 68.0587580
#> 67 68 69 70 71 72
#> 78.3024079 88.5460577 98.7897076 109.0333575 -7.2326151 4.3092525
#> 73 74 75 76 77 78
#> 15.8511201 27.3929877 38.9348553 50.4767229 62.0185906 73.5604582
#> 79 80 81 82 83 84
#> 85.1023258 96.6441934 -0.3336684 -0.6185476 -0.9034268 -1.1883061
#> 85 86 87 88 89 90
#> -1.4731853 -1.7580645 -2.0429437 -2.3278229 -2.6127022 -2.8975814
#> 91 92 93 94 95 96
#> 34.8904868 53.9860379 73.0815891 92.1771402 111.2726913 130.3682424
#> 97 98 99 100 101 102
#> 149.4637935 168.5593447 187.6548958 206.7504469 -25.2102286 -13.5695105
#> 103 104 105 106 107 108
#> -1.9287923 9.7119258 21.3526439 32.9933620 44.6340802 56.2747983
#> 109 110 111 112 113 114
#> 67.9155164 79.5562346 -13.0700342 4.0114696 21.0929734 38.1744772
#> 115 116 117 118 119 120
#> 55.2559809 72.3374847 89.4189885 106.5004922 123.5819960 140.6634998
#> 121 122 123 124 125 126
#> 4.5778642 12.0298881 19.4819119 26.9339358 34.3859597 41.8379836
#> 127 128 129 130 131 132
#> 49.2900074 56.7420313 64.1940552 71.6460791 20.8636782 34.8669653
#> 133 134 135 136 137 138
#> 48.8702523 62.8735394 76.8768265 90.8801136 104.8834007 118.8866878
#> 139 140 141 142 143 144
#> 132.8899748 146.8932619 3.2754656 14.6149665 25.9544673 37.2939681
#> 145 146 147 148 149 150
#> 48.6334689 59.9729698 71.3124706 82.6519714 93.9914723 105.3309731
#> 151 152 153 154 155 156
#> -25.6129993 -10.3232284 4.9665425 20.2563134 35.5460843 50.8358553
#> 157 158 159 160 161 162
#> 66.1256262 81.4153971 96.7051680 111.9949389 0.8070461 10.2861758
#> 163 164 165 166 167 168
#> 19.7653056 29.2444353 38.7235650 48.2026948 57.6818245 67.1609542
#> 169 170 171 172 173 174
#> 76.6400840 86.1192137 12.3145921 24.0659001 35.8172082 47.5685162
#> 175 176 177 178 179 180
#> 59.3198243 71.0711323 82.8224403 94.5737484 106.3250564 118.0763644