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augment() will add column(s) for predictions to the given data.

Usage

# S3 method for model_fit
augment(x, new_data, eval_time = NULL, ...)

Arguments

x

A model_fit object produced by fit.model_spec() or fit_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 new_data contains a survival::Surv() object, additional columns are added for inverse probability of censoring weights (IPCW) are also created (see tidymodels.org page in the references below). This enables the user to compute performance metrics in the yardstick package.

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  carb
#>    <dbl> <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     4
#>  2  23.3  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  27.6  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.5  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  17.6  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  21.6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  13.9  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  21.7  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  25.6  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  17.1  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
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