Augment data with predictions

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

Usage

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

Arguments

x

A cluster_fit object produced by fit.cluster_spec() or fit_xy.cluster_spec() .

new_data

A data frame or matrix.

...

Not currently used.

Value

A tibble::tibble() with containing new_data with columns added depending on the mode of the model.

Details

For partition models, a .pred_cluster column is added.

Examples

kmeans_spec <- k_means(num_clusters = 5) %>%
  set_engine("stats")

kmeans_fit <- fit(kmeans_spec, ~., mtcars)

kmeans_fit %>%
  augment(new_data = mtcars)
#> # A tibble: 32 × 12
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .pred_cluster <fct>