Function reference • enpls

Model fitting

Functions for fitting ensemble (sparse) partial least sqaures regression models.

enpls-package: enpls: Ensemble Partial Least Squares Regression

enpls.fit(): Ensemble Partial Least Squares Regression

enspls.fit(): Ensemble Sparse Partial Least Squares Regression

predict(<enpls.fit>): Make Predictions from a Fitted Ensemble Partial Least Squares Model

predict(<enspls.fit>): Make Predictions from a Fitted Sparse Ensemble Partial Least Squares Model

print(<enpls.fit>): Print Fitted Ensemble Partial Least Squares Object

print(<enspls.fit>): Print Fitted Ensemble Sparse Partial Least Squares Object

Cross-validation

Cross-validation routines for ensemble (sparse) PLS/SPLS models.

cv.enpls(): Cross Validation for Ensemble Partial Least Squares Regression

cv.enspls(): Cross Validation for Ensemble Sparse Partial Least Squares Regression

plot(<cv.enpls>): Plot cv.enpls object

plot(<cv.enspls>): Plot cv.enspls object

print(<cv.enpls>): Print cv.enpls Object

print(<cv.enspls>): Print cv.enspls Object

Measuring feature importance

Functions for measuring feature importance using ensemble PLS/SPLS models.

enpls.fs(): Ensemble Partial Least Squares for Measuring Feature Importance

enspls.fs(): Ensemble Sparse Partial Least Squares for Measuring Feature Importance

plot(<enpls.fs>): Plot enpls.fs object

plot(<enspls.fs>): Plot enspls.fs object

print(<enpls.fs>): Print enpls.fs Object

print(<enspls.fs>): Print enspls.fs Object

Outlier detection

Functions for detecting “outliers” using ensemble PLS/SPLS models.

enpls.od(): Ensemble Partial Least Squares for Outlier Detection

enspls.od(): Ensemble Sparse Partial Least Squares for Outlier Detection

plot(<enpls.od>): Plot enpls.od object

plot(<enspls.od>): Plot enspls.od object

print(<enpls.od>): Print enpls.od Object

print(<enspls.od>): Print enspls.od Object

Model applicability domain evaluation

Functions for evaluating model applicability domain using ensemble PLS/SPLS models.

enpls.ad(): Ensemble Partial Least Squares for Model Applicability Domain Evaluation

enspls.ad(): Ensemble Sparse Partial Least Squares for Model Applicability Domain Evaluation

plot(<enpls.ad>): Plot enpls.ad object

plot(<enspls.ad>): Plot enspls.ad object

print(<enpls.ad>): Print enpls.ad Object

print(<enspls.ad>): Print enspls.ad Object

Performance evaluation metrics

Functions for computing evaluation metrics for regression models.

enpls.rmse(): Root Mean Squared Error (RMSE)

enpls.rmsle(): Root Mean Squared Logarithmic Error (RMSLE)

enpls.mae(): Mean Absolute Error (MAE)

Datasets

Example datasets.

alkanes: Methylalkanes Retention Index Dataset

logd1k: logD7.4 Data for 1,000 Compounds