Getting Started with msma

In msma: Multiblock Sparse Multivariable Analysis

knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5)
library(msma)

Overview

The msma package implements sparse and supervised matrix decomposition for single-block and multiblock multivariable data. The main function is msma(). The analysis is selected by the supplied inputs:

• X only: principal component analysis (PCA);
• X and Y: partial least squares (PLS);
• Z: optional external supervision variable;
• positive regularization parameters: sparse estimation.

X and Y may be matrices or lists of matrices. All blocks must contain the same observations in the same row order.

Simulated data

dat <- simdata(n = 40, rho = 0.8, Xps = 5, Yps = 4, seed = 1)
X <- dat$X[[1]]
Y <- dat$Y[[1]]
set.seed(1)
Z <- rbinom(nrow(X), 1, 0.5)

dim(X)
dim(Y)

PCA

A matrix supplied as X produces a single-block PCA.

fit_pca <- msma(X, comp = 2)
fit_pca
summary(fit_pca)

The main X-side results are:

• wbX: block-level weights or loadings;
• sbX: block-level scores;
• cpevX: cumulative percentage of explained variance;
• avX: adjusted variance attributable to each component.

fit_pca$wbX
head(fit_pca$sbX[[1]])
fit_pca$cpevX

plot(fit_pca, axes = 1, plottype = "bar", las = 2)
plot(fit_pca, v = "score", axes = 1:2, plottype = "scatter")

Sparse PCA

A positive lambdaX introduces sparsity into the X-side block weights.

fit_spca <- msma(X, comp = 2, lambdaX = 0.10)
fit_spca$nzwbX
fit_spca$selectXnames

Supervised sparse PCA

Z supplies external supervision. The strength of supervision on X is controlled by muX.

fit_sup_pca <- msma(
  X = X, Z = Z, comp = 2,
  lambdaX = 0.05, muX = 0.20,
  intseed = 1
)
fit_sup_pca$predictiv

PLS

Supplying both X and Y produces PLS.

fit_pls <- msma(X = X, Y = Y, comp = 2)
fit_pls

plot(fit_pls, axes = 1, XY = "XY")
plot(fit_pls, axes = 2, XY = "XY")

Sparse and supervised PLS are requested by adding lambdaX, lambdaY, and optionally Z, muX, and muY.

fit_spls <- msma(
  X = X, Y = Y, Z = Z, comp = 2,
  lambdaX = 0.10, lambdaY = 0.10,
  muX = 0.10, muY = 0.10,
  intseed = 1
)
fit_spls$nzwbX
fit_spls$nzwbY

Prediction

pred <- predict(fit_pls, newX = X, newY = Y)
names(pred)

Session information

sessionInfo()

msma documentation built on Oct. 3, 2026, 9:07 a.m.