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
r2 <- diff(range(s_curve_noise_umap$UMAP2))/diff(range(s_curve_noise_umap$UMAP1))
model <- fit_highd_model(training_data = s_curve_noise_training,
emb_df = s_curve_noise_umap_scaled, bin1 = 4, r2 = r2, col_start_highd = "x")
df_bin_centroids <- model$df_bin_centroids
df_bin <- model$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")
#> # A tibble: 75 × 32
#> ID x1 x2 x3 x4 x5 x6 x7 pred_hb_id
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 -0.120 0.114 -1.99 -0.00246 -1.78e-2 -0.0181 -3.17e-3 9
#> 2 2 -0.0492 0.822 0.00121 0.0161 9.68e-3 -0.0834 2.30e-3 19
#> 3 3 -0.774 0.243 0.367 -0.0198 4.08e-3 -0.0349 -9.11e-3 19
#> 4 4 -0.606 1.96 -1.80 0.0132 -4.79e-4 -0.00478 -8.43e-3 5
#> 5 6 0.818 0.0388 -1.58 0.00253 1.67e-3 0.0781 -7.71e-3 10
#> 6 7 0.910 1.55 1.42 0.0124 1.60e-2 -0.00248 -8.32e-3 31
#> 7 8 -0.0691 0.978 0.00239 0.0115 3.50e-3 0.0898 3.59e-3 19
#> 8 9 0.859 1.55 -0.488 -0.00753 -1.23e-2 0.0336 -6.65e-3 14
#> 9 11 -0.0400 0.286 0.000801 0.0123 6.13e-3 -0.0121 -3.47e-4 19
#> 10 12 0.765 0.898 1.64 -0.0178 1.51e-2 -0.0710 -6.24e-3 28
#> # ℹ 65 more rows
#> # ℹ 23 more variables: model_high_d_x1 <dbl>, model_high_d_x2 <dbl>,
#> # model_high_d_x3 <dbl>, model_high_d_x4 <dbl>, model_high_d_x5 <dbl>,
#> # model_high_d_x6 <dbl>, model_high_d_x7 <dbl>, error_square_x1 <dbl>,
#> # error_square_x2 <dbl>, error_square_x3 <dbl>, error_square_x4 <dbl>,
#> # error_square_x5 <dbl>, error_square_x6 <dbl>, error_square_x7 <dbl>,
#> # row_wise_total_error <dbl>, abs_error_x1 <dbl>, abs_error_x2 <dbl>, …