Purely predictive (non-fittable) model
pmodel.RdA pmodel is a model with reduced capacities.
It can be used to make predictions, but it cannot be updated or re-fitted,
and therefore not cross-validated.
Usage
pmodel(
x,
data,
response,
label = "pmodel",
class = attr(x, "class")[[1]],
weights = NULL,
predict_function = predict
)Value
An object of class “pmodel” (also inheriting of class “model”),
a simplified version of a model, including the components
label, data, response_type, response, fit,
predict_function, class and weights.
Unlike a model, it does not have components formula and call
Details
The model object x does not have to fulfill the formal requirements of a model
(model formula, call component, etc.).
The only requirement is that for any data set of the same shape as data,
predictions can be obtained with the predict_function.
For tasks including only predicting, not re-fitting, the methods for class model
also work for a pmodel.
In particular, the following methods remain applicable:
predict.model,
residuals.model,
response.model,
performance.model,
plot.model,
label.model,
weights.model.
Among the generics with no appropriate method for a pmodel are
fit (with arguments inducing a modification of the model),
cv and all functions cv_*
(cv_performance, cv_predict, etc.).
Examples
mylm <- lm(Sepal.Length ~ ., iris)
mypm <- pmodel(mylm, iris, "Sepal.Length")
mypm
#> --- A “pmodel” object ---
#> label: pmodel
#> model class: lm
#> data: data.frame [150 x 5]
#> response_type: continuous
#> fit: Object of class ‘lm’
predict(mypm)
#> 1 2 3 4 5 6 7 8
#> 5.004788 4.756844 4.773097 4.889357 5.054377 5.388886 4.923684 5.038124
#> 9 10 11 12 13 14 15 16
#> 4.707255 4.920872 5.186890 5.121048 4.788359 4.539586 5.086884 5.470981
#> 17 18 19 20 21 22 23 24
#> 5.057188 4.973273 5.370812 5.204964 5.203972 5.123859 4.722679 5.059837
#> 25 26 27 28 29 30 31 32
#> 5.369821 4.922692 5.058017 5.087712 4.955199 5.021870 4.972281 4.975092
#> 33 34 35 36 37 38 39 40
#> 5.416761 5.351910 4.889357 4.690173 4.921864 5.085892 4.673919 5.038124
#> 41 42 43 44 45 46 47 48
#> 4.890348 4.295281 4.773097 5.044575 5.505146 4.725328 5.319403 4.856021
#> 49 50 51 52 53 54 55 56
#> 5.186890 4.905610 6.490778 6.293414 6.575522 5.495523 6.177983 6.158089
#> 57 58 59 60 61 62 63 64
#> 6.477336 5.059188 6.290603 5.579438 5.026681 5.945463 5.540480 6.342011
#> 65 66 67 68 69 70 71 72
#> 5.461359 6.192416 6.194236 5.871349 5.797525 5.574807 6.447640 5.743467
#> 73 74 75 76 77 78 79 80
#> 6.277989 6.355454 6.041829 6.142827 6.375347 6.545827 6.144647 5.324214
#> 81 82 83 84 85 86 87 88
#> 5.442294 5.390885 5.642470 6.511500 6.194236 6.361076 6.409674 5.827220
#> 89 90 91 92 93 94 95 96
#> 5.925569 5.594701 6.007503 6.308676 5.675805 5.009599 5.859727 6.040009
#> 97 98 99 100 101 102 103 104
#> 5.958905 6.041829 4.828488 5.826392 6.971778 6.117018 6.866149 6.662333
#> 105 106 107 108 109 110 111 112
#> 6.751709 7.446619 5.583325 7.242804 6.629826 7.203469 6.333447 6.282867
#> 113 114 115 116 117 118 119 120
#> 6.534451 5.903400 6.009029 6.404749 6.628997 7.894739 7.434006 5.912211
#> 121 122 123 124 125 126 127 128
#> 6.736446 5.969242 7.461881 5.982684 6.849066 7.142797 5.949349 6.131451
#> 129 130 131 132 133 134 135 136
#> 6.518198 6.940802 6.995851 7.708997 6.486682 6.292669 6.639628 6.968966
#> 137 138 139 140 141 142 143 144
#> 6.721184 6.678586 6.048527 6.501115 6.572418 6.189311 6.117018 6.902295
#> 145 146 147 148 149 150
#> 6.723004 6.222647 5.934916 6.317193 6.586851 6.297300
residuals(mypm)
#> 1 2 3 4 5 6
#> 0.095211981 0.143156450 -0.073096946 -0.289356835 -0.054376913 0.011114267
#> 7 8 9 10 11 12
#> -0.323683608 -0.038123516 -0.307254656 -0.020872352 0.213109802 -0.321047908
#> 13 14 15 16 17 18
#> 0.011640933 -0.239585893 0.713116294 0.229018580 0.342811832 0.126727498
#> 19 20 21 22 23 24
#> 0.329187643 -0.104963574 0.196027701 -0.023859163 -0.122679348 0.040163147
#> 25 26 27 28 29 30
#> -0.569821081 0.077307668 -0.058016873 0.112287590 0.244800875 -0.321870120
#> 31 32 33 34 35 36
#> -0.172281226 0.424907518 -0.216761291 0.148089724 0.010643165 0.309827445
#> 37 38 39 40 41 42
#> 0.578136372 -0.185892430 -0.273919159 0.061876484 0.109651890 0.204718616
#> 43 44 45 46 47 48
#> -0.373096946 -0.044574732 -0.405145622 0.074671968 -0.219403483 -0.256021337
#> 49 50 51 52 53 54
#> 0.113109802 0.094389769 0.509221918 0.106586218 0.324477547 0.004477184
#> 55 56 57 58 59 60
#> 0.322017402 -0.458089242 -0.177335941 -0.159187524 0.309397473 -0.379438483
#> 61 62 63 64 65 66
#> -0.026680731 -0.045462821 0.459519526 -0.242011401 0.138641386 0.507583985
#> 67 68 69 70 71 72
#> -0.594235995 -0.071349335 0.402475156 0.025192753 -0.547640404 0.356532715
#> 73 74 75 76 77 78
#> 0.022010910 -0.255453541 0.358170647 0.457172879 0.424653102 0.154173084
#> 79 80 81 82 83 84
#> -0.144647101 0.375785906 0.057706038 0.109114912 0.157530482 -0.511500143
#> 85 86 87 88 89 90
#> -0.794235995 -0.361076053 0.290326329 0.472779619 -0.325569464 -0.094700604
#> 91 92 93 94 95 96
#> -0.507502580 -0.208675903 0.124194985 -0.009598630 -0.259727174 -0.340009373
#> 97 98 99 100 101 102
#> -0.258904962 0.158170647 0.271512274 -0.126391677 -0.671777514 -0.317017734
#> 103 104 105 106 107 108
#> 0.233851489 -0.362332996 -0.251708602 0.153380750 -0.683324634 0.057196266
#> 109 110 111 112 113 114
#> 0.070173797 -0.003468587 0.166553314 0.117133483 0.265549054 -0.203400038
#> 115 116 117 118 119 120
#> -0.209029042 -0.004748917 -0.128997498 -0.194739274 0.265994187 0.087789057
#> 121 122 123 124 125 126
#> 0.163553518 -0.369242328 0.238118630 0.317315531 -0.149066410 0.057202758
#> 127 128 129 130 131 132
#> 0.250651028 -0.031451151 -0.118197550 0.259198294 0.404149460 0.191002865
#> 133 134 135 136 137 138
#> -0.086682032 0.007331302 -0.539628383 0.731033741 -0.421184361 -0.278586392
#> 139 140 141 142 143 144
#> -0.048526760 0.398884551 0.127582321 0.710688760 -0.317017734 -0.102295264
#> 145 146 147 148 149 150
#> -0.023004341 0.477353262 0.365084445 0.182806710 -0.386851096 -0.397299933
response(mypm)
#> [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
#> [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
#> [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
#> [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
#> [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
#> [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
#> [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
#> [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
#> [145] 6.7 6.7 6.3 6.5 6.2 5.9
performance(mypm)
#> --- Performance table ---
#> Metric: rmse
#> train_rmse test_rmse
#> pmodel 0.30063 NA