Augment Data with Predictions and Error Metrics for NLDR Models
Source:R/prediction.R
augment.highd_vis_model.RdThis S3 method augments a dataset with predictions and error metrics obtained
from a nonlinear dimension reduction (NLDR) model stored in a highd_vis_model object.
Usage
# S3 method for class 'highd_vis_model'
augment(model_object, highd_data, ...)Value
A tibble containing the augmented data with predictions, error metrics, and absolute error metrics.
Examples
# Assuming 'fit' is a highd_vis_model object and 'scurve' contains the original data:
fit <- fit_highd_model(highd_data = scurve, nldr_data = scurve_umap, b1 = 30,
q = 0.1, hd_thresh = 5)
#> Warning: triangle collapsed!
#> Warning: three points coincide or are collinear!
#> ✔ Model generated successfully!!!
augment(fit, highd_data = scurve)
#> # A tibble: 1,000 × 32
#> ID x1 x2 x3 x4 x5 x6 x7 pred_h
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 1 -0.120 0.819 -1.99 0.0114 0.00351 0.0334 0.00638 816
#> 2 2 -0.0492 0.166 0.00121 0.0115 -0.0166 -0.0297 0.00509 711
#> 3 3 -0.774 0.651 0.367 -0.0172 0.00600 0.0211 0.00303 287
#> 4 4 -0.606 0.952 -1.80 0.0157 -0.00978 -0.0590 -0.00754 816
#> 5 5 -0.478 1.10 -1.88 -0.00423 0.00495 -0.0482 -0.00982 816
#> 6 6 0.818 1.78 -1.58 0.0124 0.0198 0.0560 -0.000730 711
#> 7 7 0.910 0.975 1.42 -0.0111 0.0132 0.0299 0.00401 397
#> 8 8 -0.0691 1.90 0.00239 0.0125 -0.00463 0.0260 0.00590 711
#> 9 9 0.859 1.34 -0.488 -0.00195 -0.0145 -0.00950 0.00593 711
#> 10 10 -0.727 1.56 0.314 0.0189 0.0147 -0.0659 0.00617 711
#> # ℹ 990 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>, …