This function augments a dataset with predictions and error metrics obtained from a nonlinear dimension reduction (NLDR) model.
Usage
augment(
df_bin_centroids,
df_bin,
training_data,
newdata = NULL,
type_NLDR,
col_start = "x"
)Arguments
- df_bin_centroids
Centroid coordinates of hexagonal bins in 2D space.
- df_bin
Centroid coordinates of hexagonal bins in high dimensions.
- training_data
Training data used to fit the model.
- newdata
Data to be augmented with predictions and error metrics. If NULL, the training data is used (default is NULL).
- type_NLDR
The type of non-linear dimensionality reduction (NLDR) used.
- col_start
The text that begin the column name of the high-dimensional data.
Value
A tibble containing the augmented data with predictions, error metrics, and absolute error metrics.
Examples
df_bin_centroids <- s_curve_obj$s_curve_umap_model_distance_df$df_bin_centroids
#> Warning: Unknown or uninitialised column: `df_bin_centroids`.
df_bin <- s_curve_obj$s_curve_umap_model_distance_df$df_bin
#> Warning: Unknown or uninitialised column: `df_bin`.
augment(df_bin_centroids = df_bin_centroids, df_bin = df_bin,
training_data = s_curve_noise_training, newdata = NULL, type_NLDR = "UMAP",
col_start = "x")
#> Error in names(df_bin) <- `*vtmp*`: attempt to set an attribute on NULL