augment() will add column(s) for predictions to the given data.
Usage
# S3 method for class 'model_fit'
augment(x, new_data, eval_time = NULL, ...)Arguments
- x
A model fit produced by
fit.model_spec()orfit_xy.model_spec().- new_data
A data frame or matrix.
- eval_time
For censored regression models, a vector of time points at which the survival probability is estimated.
- ...
Not currently used.
Details
Regression
For regression models, a .pred column is added. If x was created using
fit.model_spec() and new_data contains a regression outcome column, a
.resid column is also added.
Classification
For classification models, the results can include a column called
.pred_class as well as class probability columns named .pred_{level}.
This depends on what type of prediction types are available for the model.
Censored Regression
For these models, predictions for the expected time and survival probability
are created (if the model engine supports them). If the model supports
survival prediction, the eval_time argument is required.
If survival predictions are created and the survival outcome can be
resolved from new_data (either as a literal survival::Surv() column or
from the variables referenced in the model's formula LHS), additional
columns are added for inverse probability of censoring weights (IPCW) (see
tidymodels.org page in the references below). This enables the user to
compute performance metrics in the yardstick package. The same
columns can be obtained directly from predict.model_fit() by setting
add_censoring_weights = TRUE.
Quantile Regression
For quantile regression models, a .pred_quantile column is added that
contains the quantile predictions for each row. This column has a special
class "quantile_pred" and can be unnested using tidyr::unnest()
Examples
car_trn <- mtcars[11:32,]
car_tst <- mtcars[ 1:10,]
reg_form <-
linear_reg() |>
set_engine("lm") |>
fit(mpg ~ ., data = car_trn)
reg_xy <-
linear_reg() |>
set_engine("lm") |>
fit_xy(car_trn[, -1], car_trn$mpg)
augment(reg_form, car_tst)
#> # A tibble: 10 × 13
#> .pred .resid mpg cyl disp hp drat wt qsec vs am
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 23.4 -2.43 21 6 160 110 3.9 2.62 16.5 0 1
#> 2 23.3 -2.30 21 6 160 110 3.9 2.88 17.0 0 1
#> 3 27.6 -4.83 22.8 4 108 93 3.85 2.32 18.6 1 1
#> 4 21.5 -0.147 21.4 6 258 110 3.08 3.22 19.4 1 0
#> 5 17.6 1.13 18.7 8 360 175 3.15 3.44 17.0 0 0
#> 6 21.6 -3.48 18.1 6 225 105 2.76 3.46 20.2 1 0
#> 7 13.9 0.393 14.3 8 360 245 3.21 3.57 15.8 0 0
#> 8 21.7 2.70 24.4 4 147. 62 3.69 3.19 20 1 0
#> 9 25.6 -2.81 22.8 4 141. 95 3.92 3.15 22.9 1 0
#> 10 17.1 2.09 19.2 6 168. 123 3.92 3.44 18.3 1 0
#> # ℹ 2 more variables: gear <dbl>, carb <dbl>
augment(reg_form, car_tst[, -1])
#> # A tibble: 10 × 11
#> .pred cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 23.4 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 23.3 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 27.6 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.5 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 17.6 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 21.6 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 13.9 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 21.7 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 25.6 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 17.1 6 168. 123 3.92 3.44 18.3 1 0 4 4
augment(reg_xy, car_tst)
#> # A tibble: 10 × 12
#> .pred mpg cyl disp hp drat wt qsec vs am gear
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 23.4 21 6 160 110 3.9 2.62 16.5 0 1 4
#> 2 23.3 21 6 160 110 3.9 2.88 17.0 0 1 4
#> 3 27.6 22.8 4 108 93 3.85 2.32 18.6 1 1 4
#> 4 21.5 21.4 6 258 110 3.08 3.22 19.4 1 0 3
#> 5 17.6 18.7 8 360 175 3.15 3.44 17.0 0 0 3
#> 6 21.6 18.1 6 225 105 2.76 3.46 20.2 1 0 3
#> 7 13.9 14.3 8 360 245 3.21 3.57 15.8 0 0 3
#> 8 21.7 24.4 4 147. 62 3.69 3.19 20 1 0 4
#> 9 25.6 22.8 4 141. 95 3.92 3.15 22.9 1 0 4
#> 10 17.1 19.2 6 168. 123 3.92 3.44 18.3 1 0 4
#> # ℹ 1 more variable: carb <dbl>
augment(reg_xy, car_tst[, -1])
#> # A tibble: 10 × 11
#> .pred cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 23.4 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 23.3 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 27.6 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.5 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 17.6 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 21.6 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 13.9 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 21.7 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 25.6 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 17.1 6 168. 123 3.92 3.44 18.3 1 0 4 4
# ------------------------------------------------------------------------------
data(two_class_dat, package = "modeldata")
cls_trn <- two_class_dat[-(1:10), ]
cls_tst <- two_class_dat[ 1:10 , ]
cls_form <-
logistic_reg() |>
set_engine("glm") |>
fit(Class ~ ., data = cls_trn)
cls_xy <-
logistic_reg() |>
set_engine("glm") |>
fit_xy(cls_trn[, -3],
cls_trn$Class)
augment(cls_form, cls_tst)
#> # A tibble: 10 × 6
#> .pred_class .pred_Class1 .pred_Class2 A B Class
#> <fct> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 Class1 0.518 0.482 2.07 1.63 Class1
#> 2 Class1 0.909 0.0913 2.02 1.04 Class1
#> 3 Class1 0.648 0.352 1.69 1.37 Class2
#> 4 Class1 0.610 0.390 3.43 1.98 Class2
#> 5 Class2 0.443 0.557 2.88 1.98 Class1
#> 6 Class2 0.206 0.794 3.31 2.41 Class2
#> 7 Class1 0.708 0.292 2.50 1.56 Class2
#> 8 Class1 0.567 0.433 1.98 1.55 Class2
#> 9 Class1 0.994 0.00582 2.88 0.580 Class1
#> 10 Class2 0.108 0.892 3.74 2.74 Class2
augment(cls_form, cls_tst[, -3])
#> # A tibble: 10 × 5
#> .pred_class .pred_Class1 .pred_Class2 A B
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Class1 0.518 0.482 2.07 1.63
#> 2 Class1 0.909 0.0913 2.02 1.04
#> 3 Class1 0.648 0.352 1.69 1.37
#> 4 Class1 0.610 0.390 3.43 1.98
#> 5 Class2 0.443 0.557 2.88 1.98
#> 6 Class2 0.206 0.794 3.31 2.41
#> 7 Class1 0.708 0.292 2.50 1.56
#> 8 Class1 0.567 0.433 1.98 1.55
#> 9 Class1 0.994 0.00582 2.88 0.580
#> 10 Class2 0.108 0.892 3.74 2.74
augment(cls_xy, cls_tst)
#> # A tibble: 10 × 6
#> .pred_class .pred_Class1 .pred_Class2 A B Class
#> <fct> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 Class1 0.518 0.482 2.07 1.63 Class1
#> 2 Class1 0.909 0.0913 2.02 1.04 Class1
#> 3 Class1 0.648 0.352 1.69 1.37 Class2
#> 4 Class1 0.610 0.390 3.43 1.98 Class2
#> 5 Class2 0.443 0.557 2.88 1.98 Class1
#> 6 Class2 0.206 0.794 3.31 2.41 Class2
#> 7 Class1 0.708 0.292 2.50 1.56 Class2
#> 8 Class1 0.567 0.433 1.98 1.55 Class2
#> 9 Class1 0.994 0.00582 2.88 0.580 Class1
#> 10 Class2 0.108 0.892 3.74 2.74 Class2
augment(cls_xy, cls_tst[, -3])
#> # A tibble: 10 × 5
#> .pred_class .pred_Class1 .pred_Class2 A B
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Class1 0.518 0.482 2.07 1.63
#> 2 Class1 0.909 0.0913 2.02 1.04
#> 3 Class1 0.648 0.352 1.69 1.37
#> 4 Class1 0.610 0.390 3.43 1.98
#> 5 Class2 0.443 0.557 2.88 1.98
#> 6 Class2 0.206 0.794 3.31 2.41
#> 7 Class1 0.708 0.292 2.50 1.56
#> 8 Class1 0.567 0.433 1.98 1.55
#> 9 Class1 0.994 0.00582 2.88 0.580
#> 10 Class2 0.108 0.892 3.74 2.74
# ------------------------------------------------------------------------------
if (rlang::is_installed("quantreg")) {
# Quantile regression example
qr_form <-
linear_reg() |>
set_engine("quantreg") |>
set_mode("quantile regression", quantile_levels = c(0.25, 0.5, 0.75)) |>
fit(mpg ~ ., data = car_trn)
augment(qr_form, car_tst)
augment(qr_form, car_tst[, -1])
}