parsnip (version 1.1.0)

augment.model_fit: Augment data with predictions

Description

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

Usage

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

Arguments

x

A model_fit object produced by fit.model_spec() or fit_xy.model_spec() .

new_data

A data frame or matrix.

...

Not currently used.

Details

For regression models, a .pred column is added. If x was created using fit.model_spec() and new_data contains the outcome column, a .resid column is also added.

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.

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)
augment(reg_form, car_tst[, -1])

augment(reg_xy, car_tst)
augment(reg_xy, car_tst[, -1])

# ------------------------------------------------------------------------------

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)
augment(cls_form, cls_tst[, -3])

augment(cls_xy, cls_tst)
augment(cls_xy, cls_tst[, -3])