Add predicted values of different types to dataframe

add_pred(data, model, var = "pred", type = NULL, transformation = NULL)

Arguments

data: dataframe or tibble
model: model object
var: name of new variable in dataframe / tibble
type: type of predicted value
transformation: A possible transformation of predicted variable, e.g. reciprocal(), log() etc

Value

dataframe / tibble

Author

Søren Højsgaard

Examples

data(cars)
lm1 <- lm(dist ~ speed + I(speed^2), data=cars)
lm1 |> response() |> head()
#>  1  2  3  4  5  6 
#>  2 10  4 22 16 10 
cars <- cars |> add_pred(lm1)
cars |> head()
#>   speed dist      pred
#> 1     4    2  7.722637
#> 2     4   10  7.722637
#> 3     7    4 13.761157
#> 4     7   22 13.761157
#> 5     8   16 16.173834
#> 6     9   10 18.786430
cars <- cars |> add_resid(lm1)
cars
#>    speed dist      pred       resid
#> 1      4    2  7.722637  -5.7226371
#> 2      4   10  7.722637   2.2773629
#> 3      7    4 13.761157  -9.7611569
#> 4      7   22 13.761157   8.2388431
#> 5      8   16 16.173834  -0.1738340
#> 6      9   10 18.786430  -8.7864298
#> 7     10   18 21.598944  -3.5989441
#> 8     10   26 21.598944   4.4010559
#> 9     10   34 21.598944  12.4010559
#> 10    11   17 24.611377  -7.6113771
#> 11    11   28 24.611377   3.3886229
#> 12    12   14 27.823729 -13.8237287
#> 13    12   20 27.823729  -7.8237287
#> 14    12   24 27.823729  -3.8237287
#> 15    12   28 27.823729   0.1762713
#> 16    13   26 31.235999  -5.2359988
#> 17    13   34 31.235999   2.7640012
#> 18    13   34 31.235999   2.7640012
#> 19    13   46 31.235999  14.7640012
#> 20    14   26 34.848188  -8.8481876
#> 21    14   36 34.848188   1.1518124
#> 22    14   60 34.848188  25.1518124
#> 23    14   80 34.848188  45.1518124
#> 24    15   20 38.660295 -18.6602950
#> 25    15   26 38.660295 -12.6602950
#> 26    15   54 38.660295  15.3397050
#> 27    16   32 42.672321 -10.6723209
#> 28    16   40 42.672321  -2.6723209
#> 29    17   32 46.884266 -14.8842655
#> 30    17   40 46.884266  -6.8842655
#> 31    17   50 46.884266   3.1157345
#> 32    18   42 51.296129  -9.2961287
#> 33    18   56 51.296129   4.7038713
#> 34    18   76 51.296129  24.7038713
#> 35    18   84 51.296129  32.7038713
#> 36    19   36 55.907911 -19.9079105
#> 37    19   46 55.907911  -9.9079105
#> 38    19   68 55.907911  12.0920895
#> 39    20   32 60.719611 -28.7196109
#> 40    20   48 60.719611 -12.7196109
#> 41    20   52 60.719611  -8.7196109
#> 42    20   56 60.719611  -4.7196109
#> 43    20   64 60.719611   3.2803891
#> 44    22   66 70.942768  -4.9427675
#> 45    23   54 76.354224 -22.3542237
#> 46    24   70 81.965599 -11.9655985
#> 47    24   92 81.965599  10.0344015
#> 48    24   93 81.965599  11.0344015
#> 49    24  120 81.965599  38.0344015
#> 50    25   85 87.776892  -2.7768919