pls.regression: Partial Least Squares (PLS) Regression Interface

In snazzieR: Chic and Sleek Functions for Beautiful Statisticians

Partial Least Squares (PLS) Regression Interface

Description

Performs Partial Least Squares (PLS) regression using either the NIPALS or SVD algorithm for component extraction. This is the main user-facing function for computing PLS models. Internally, it delegates to either NIPALS.pls() or SVD.pls().

Usage

pls.regression(x, y, n.components = NULL, calc.method = c("SVD", "NIPALS"))

Arguments

x	A numeric matrix or data frame of predictor variables (X), with dimensions n × p.
y	A numeric matrix or data frame of response variables (Y), with dimensions n × q.
n.components	Integer specifying the number of latent components (H) to extract. If NULL, defaults to the rank of x.
calc.method	Character string indicating the algorithm to use. Must be either "SVD" (default) or "NIPALS".

Details

This function provides a unified interface for Partial Least Squares regression. Based on the value of calc.method, it computes latent variables using either:

• "SVD" — A direct method using the singular value decomposition of the cross-covariance matrix (X^\top Y).

• "NIPALS" — An iterative method that alternately estimates predictor and response scores until convergence.

The outputs from both methods include scores, weights, loadings, regression coefficients, and explained variance.

Value

A list (from either SVD.pls() or NIPALS.pls()) containing:

model.type

Character string ("PLS Regression").

T, U

Score matrices for X and Y.

W, C

Weight matrices for X and Y.

P_loadings, Q_loadings

Loading matrices.

B_vector

Component-wise regression weights.

coefficients

Final regression coefficient matrix (rescaled).

intercept

Intercept vector (typically zero due to centering).

X_explained, Y_explained

Variance explained by each component.

X_cum_explained, Y_cum_explained

Cumulative variance explained.

References

Abdi, H., & Williams, L. J. (2013). Partial least squares methods: Partial least squares correlation and partial least square regression. Methods in Molecular Biology (Clifton, N.J.), 930, 549–579. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-1-62703-059-5_23")}

de Jong, S. (1993). SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), 251–263. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/0169-7439(93)85002-X")}

See Also

SVD.pls, NIPALS.pls

Examples

## Not run: 
X <- matrix(rnorm(100 * 10), 100, 10)
Y <- matrix(rnorm(100 * 2), 100, 2)

# Using SVD (default)
model1 <- pls.regression(X, Y, n.components = 3)

# Using NIPALS
model2 <- pls.regression(X, Y, n.components = 3, calc.method = "NIPALS")

## End(Not run)

snazzieR documentation built on June 8, 2025, 11:35 a.m.