prediction.md
In quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data

predict_emb() works

Code
  predict_emb(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
  model_2d = scurve_model_obj$model_2d)
Output
  # A tibble: 1,000 x 4
     pred_emb_1 pred_emb_2    ID pred_h
          <dbl>      <dbl> <int>  <int>
   1     0.275       0.823     1    200
   2     0.734       0.462     2    131
   3     0.734       0.462     3    131
   4     0.0251      0.968     4    227
   5     0.192       1.11      5    259
   6     0.442       0.968     6    232
   7     0.150       0.318     7     94
   8     0.859       0.679     8    177
   9     0.818       0.896     9    222
  10     0.901       0.318    10    103
  # i 990 more rows

glance() works

Code
  glance(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
  model_2d = scurve_model_obj$model_2d)
Output
  # A tibble: 1 x 2
    Error  RMSE
    <dbl> <dbl>
  1  613. 0.431

augment() works

Code
  augment(highd_data = scurve, model_highd = scurve_model_obj$model_highd,
  model_2d = scurve_model_obj$model_2d)
Output
  # A tibble: 1,000 x 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     200
   2     2 -0.0492 0.166  0.00121  0.0115  -0.0166  -0.0297   0.00509     131
   3     3 -0.774  0.651  0.367   -0.0172   0.00600  0.0211   0.00303     131
   4     4 -0.606  0.952 -1.80     0.0157  -0.00978 -0.0590  -0.00754     227
   5     5 -0.478  1.10  -1.88    -0.00423  0.00495 -0.0482  -0.00982     259
   6     6  0.818  1.78  -1.58     0.0124   0.0198   0.0560  -0.000730    232
   7     7  0.910  0.975  1.42    -0.0111   0.0132   0.0299   0.00401      94
   8     8 -0.0691 1.90   0.00239  0.0125  -0.00463  0.0260   0.00590     177
   9     9  0.859  1.34  -0.488   -0.00195 -0.0145  -0.00950  0.00593     222
  10    10 -0.727  1.56   0.314    0.0189   0.0147  -0.0659   0.00617     103
  # 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>, ...

gen_diffbin1_errors() works

Code
  gen_diffbin1_errors(highd_data = scurve, nldr_data = scurve_umap)
Message
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
  v Model generated successfully!!!
Output
        Error      RMSE b1 b2    b   m   a1   a2       d_bar
  1  628.8048 0.4098319  5  7   35  22 0.26 0.23 0.040914704
  2  562.7530 0.3672331  6  8   48  27 0.23 0.20 0.036862602
  3  520.4474 0.3364583  7  9   63  33 0.20 0.17 0.032327667
  4  430.4658 0.2722071  8 11   88  46 0.16 0.14 0.025996005
  5  406.8971 0.2544715  9 12  108  51 0.14 0.12 0.025513054
  6  380.7331 0.2388800 10 13  130  59 0.13 0.11 0.022664184
  7  329.2051 0.2014154 11 15  165  83 0.11 0.10 0.015603331
  8  321.5839 0.1977916 12 16  192  87 0.11 0.09 0.016286420
  9  306.5549 0.1846552 13 17  221  99 0.10 0.09 0.014296013
  10 276.8768 0.1678185 14 19  266 118 0.09 0.08 0.012761403
  11 273.3133 0.1632627 15 20  300 127 0.08 0.07 0.012188695
  12 256.7057 0.1539926 16 21  336 143 0.08 0.07 0.010727669
  13 243.8163 0.1448055 17 23  391 162 0.07 0.06 0.010073650
  14 230.5567 0.1363121 18 24  432 171 0.07 0.06 0.009841970
  15 223.3106 0.1314176 19 25  475 186 0.07 0.06 0.009021009
  16 214.6699 0.1266627 20 27  540 204 0.06 0.05 0.008720843
  17 210.8964 0.1245104 21 28  588 218 0.06 0.05 0.008159495
  18 208.3375 0.1225372 22 29  638 229 0.06 0.05 0.007960544
  19 191.4192 0.1131797 23 31  713 252 0.05 0.05 0.007507407
  20 187.6444 0.1098865 24 32  768 265 0.05 0.04 0.007249035
  21 188.3273 0.1110222 25 33  825 265 0.05 0.04 0.007499669
  22 181.0666 0.1083178 26 35  910 288 0.05 0.04 0.007106813
  23 177.7219 0.1072918 27 36  972 286 0.05 0.04 0.007554324
  24 178.9625 0.1076837 28 37 1036 290 0.04 0.04 0.007612155
  25 174.3416 0.1046141 29 38 1102 300 0.04 0.04 0.007522166