predict.spectral_model.md
In proximetricsR: Spectral Preprocessing and Chemometric Calibration of NIR Sensors

Predictions with formula from a data.frame are correct

Code
  predictions_df_form$predictions
Output
        ncomp_1     ncomp_2     ncomp_3     ncomp_4      ncomp_5      ncomp_6
  46 0.30674032 0.320636544 0.333660354 0.331273561 0.3634687767 0.3364168607
  54 0.23590773 0.294600782 0.289079432 0.307923549 0.3189520418 0.2863374436
  67 0.21963147 0.238837611 0.241393613 0.253460720 0.2637369937 0.2761933268
  71 0.12162060 0.111804446 0.128429306 0.056808077 0.0515597347 0.0247356051
  73 0.15393442 0.080740218 0.078241031 0.012452985 0.0057112824 0.0069032748

Model information of predictions with formula from a data.frame is correct

Code
  predictions_df_form$model_information$model_grid
Output
    ncomp           rsq       rmse largest_residual
  1     1 0.00538444397 0.23880883        1.0896055
  2     2 0.00111608609 0.24756153        1.1178821
  3     3 0.00695744878 0.24854622        1.0930028
  4     4 0.00508401634 0.25199983        1.0932020
  5     5 0.00226522313 0.25154415        1.0728407
  6     6 0.00027641142 0.26078089        1.0852900

Predictions for dataframes from a formula are correctly printed

Code
  print(predictions_df_form)
Output
  Predicted response: THC 
  Spectral preprocessing recipe (device: "unspecified"): 
   - Step 1: prep_resample
      min_wav: 1100; max_wav: 1600; resolution: 5
   - Step 2: prep_derivative
      m: 1; w: 5; p: 11; algorithm: 'nwp'
   - Step 3: prep_snv
   - Step 4: prep_smooth
      w: 3; algorithm: 'moving-average'
  Number of predictions: 5 
  Considered pls factors: 1, 2, 3, 4, 5, 6 
  Proposed number of pls factors by the model:  10 
  Units of the predicted response:  % 
  _______________________________________________________

   Predictions obtained from the model with 'newdata'

     ncomp_1 ncomp_2 ncomp_3 ncomp_4 ncomp_5 ncomp_6
  46   0.307  0.3206  0.3337  0.3313 0.36347  0.3364
  54   0.236  0.2946  0.2891  0.3079 0.31895  0.2863
  67   0.220  0.2388  0.2414  0.2535 0.26374  0.2762
  71   0.122  0.1118  0.1284  0.0568 0.05156  0.0247
  73   0.154  0.0807  0.0782  0.0125 0.00571  0.0069
  _______________________________________________________

Predictions with matrices from a matrix are correct

Code
  predictions_mat_mat$predictions
Output
        ncomp_4      ncomp_5      ncomp_6     ncomp_7     ncomp_8    ncomp_9
  1 0.331273561 0.3634687767 0.3364168607 0.444516433 0.428319964 0.43800532
  2 0.307923549 0.3189520418 0.2863374436 0.214666114 0.179239192 0.13620860
  3 0.253460720 0.2637369937 0.2761933268 0.303615214 0.296301182 0.28890156
  4 0.056808077 0.0515597347 0.0247356051 0.088918495 0.076994889 0.11173230
  5 0.012452985 0.0057112824 0.0069032748 0.058709537 0.059256168 0.10013744
      ncomp_10
  1 0.40031979
  2 0.21029308
  3 0.31424760
  4 0.10084960
  5 0.11502699

Model information of predictions with matrices from a matrix is correct

Code
  predictions_mat_mat$model_information$model_grid
Output
     ncomp           rsq       rmse largest_residual
  4      4 0.00508401634 0.25199983        1.0932020
  5      5 0.00226522313 0.25154415        1.0728407
  6      6 0.00027641142 0.26078089        1.0852900
  7      7 0.01320488098 0.29068948        1.1786691
  8      8 0.03013242118 0.30963986        1.2389772
  9      9 0.02727642180 0.33867561        1.2063062
  10    10 0.04107142279 0.33881800        1.2398988

Predictions of a dataframe from a matrix are correctly printed

Code
  print(predictions_df_mat)
Output
  Predicted response: THC 
  Spectral preprocessing recipe (device: "unspecified"): 
   - Step 1: prep_resample
      min_wav: 1100; max_wav: 1600; resolution: 5
   - Step 2: prep_derivative
      m: 1; w: 5; p: 11; algorithm: 'nwp'
   - Step 3: prep_snv
   - Step 4: prep_smooth
      w: 3; algorithm: 'moving-average'
  Number of predictions: 5 
  Final number of pls factors: 4 
  Proposed number of pls factors by the model:  10 
  _______________________________________________________

   Predictions obtained from the model with 'newdata'

     ncomp_4
  46  0.3313
  54  0.3079
  67  0.2535
  71  0.0568
  73  0.0125
  _______________________________________________________