Fit a Partial Least Squares (PLS) model

Fits a supervised Partial Least Squares (PLS) model using a NIPALS-based algorithm with optional cross-validation and automatic component selection.

Usage

pls(X, Y, scaling, validation_strategy, maxPCo = 5)

Arguments

X

Numeric matrix of predictors (rows = samples, columns = variables).

Y

Numeric matrix or factor vector of responses.

scaling

A scaling strategy object (e.g., uv_scaling(center = TRUE)), specifying model-internal centering and/or scaling applied during fitting. This does not modify the original spectral matrix.

validation_strategy

A cross-validation strategy object defining how resampling is performed (e.g., k-fold, Monte Carlo).

maxPCo

Integer. Maximum number of predictive components to evaluate.

Value

An object of class m8_model containing the fitted PLS model, cross-validation results, and performance statistics.

Details

Model components are estimated sequentially using a NIPALS-based algorithm. For each component, cross-validated performance metrics (e.g., Q², R², classification AUC) are computed according to the supplied validation_strategy. Component extraction stops when the stopRule indicates overfitting or when maxPCo is reached.

Scaling specified via scaling is applied internally during model fitting and does not alter the input matrix X. Spectral preprocessing steps (e.g., alignment, baseline correction) should be performed prior to model fitting.

The returned model object stores:

• Fitted component models

• Cross-validation results

• Performance metrics (R², Q², AUC)

• Model control parameters

• Input data provenance metadata

• Session information for reproducibility

See also

opls, uv_scaling

Other modelling: opls(), pca()

Examples

data(covid)

cv <- balanced_mc(k=5, split=2/3)
scaling <- uv_scaling(center=TRUE)
model <-pls(X=covid$X, Y=covid$an$type, scaling, cv)
#> Performing discriminant analysis.
#> A PLS-DA model with 1 component was fitted.

show(model)
#> 
#> m8_model <pls>
#> ----------------------------------------
#> Dimensions  : 10 samples x 27819 variables
#> Mode        : classification 
#> Preprocess  : center | UV 
#> Components  : 1 (2 tested)
#> Validation  : BalancedMonteCarlo (k = 5) 
#> Stop rule   : cv_improvement_negligible 
#> ----------------------------------------
#> Use summary() for performance metrics.
#> 
summary(model)
#> $perf
#>   comp AUCt AUCv       R2X selected        fit               stop_reason
#> 1    1    1    1 0.2502977     TRUE     fitted                      <NA>
#> 2    2    1    1 0.2179150    FALSE not fitted cv_improvement_negligible
#>   stop_metric stop_delta
#> 1          NA         NA
#> 2           1          0
#> 
#> $engine
#> [1] "pls"
#> 
#> $y_type
#> [1] "DA"
#> 

Tp <- scores(model)
Pp <- loadings(model)