Model and Parameter Selection in msma

In msma: Multiblock Sparse Multivariable Analysis

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

Overview

The package provides functions for selecting component numbers and regularization parameters:

• ncompsearch(): number of components;
• regparasearch(): regularization parameters;
• optparasearch(): combined selection.

BIC is useful for a quick deterministic search. Cross-validation may be more computationally expensive.

dat <- simdata(n = 35, rho = 0.8, Xps = c(4, 4), Yps = c(3, 3), seed = 4)
X <- dat$X
Y <- dat$Y

Selecting the number of components

search_comp <- ncompsearch(X, comps = 1:3, criterion = "BIC", intseed = 1)
search_comp

plot(search_comp)

For nested analysis, candidates may be supplied as a list for root and super components.

search_nested <- ncompsearch(
  X,
  comps = list(1:4, 1:3),
  criterion = "BIC",
  intseed = 1
)

Selecting regularization parameters

The following example is shown but not evaluated during package building to keep the vignette lightweight.

search_lambda <- regparasearch(
  X = X,
  comp = 2,
  criterion = "BIC",
  maxrep = 5,
  intseed = 1
)
search_lambda

Combined selection

optparasearch() supports four workflows:

• "regparaonly": regularization search for fixed components;
• "ncomp1st": component search followed by regularization search;
• "regpara1st": regularization search followed by component search;
• "simultaneous": repeated joint search.

opt <- optparasearch(
  X = X,
  search.method = "ncomp1st",
  criterion = "BIC",
  intseed = 1
)

fit <- msma(
  X = X,
  comp = opt$optncomp,
  lambdaX = opt$optlambdaX,
  lambdaXsup = opt$optlambdaXsup,
  intseed = 1
)

PLS selection

X-side and Y-side parameters are selected separately in PLS.

opt_pls <- optparasearch(
  X = X, Y = Y,
  search.method = "regparaonly",
  criterion = "BIC",
  intseed = 1
)

fit_pls <- msma(
  X = X, Y = Y,
  comp = opt_pls$optncomp,
  lambdaX = opt_pls$optlambdaX,
  lambdaY = opt_pls$optlambdaY,
  lambdaXsup = opt_pls$optlambdaXsup,
  lambdaYsup = opt_pls$optlambdaYsup,
  intseed = 1
)

Cross-validation

cv <- cvmsma(
  X = X, Y = Y,
  comp = 1,
  lambdaX = c(0.1, 0.1),
  lambdaY = c(0.1, 0.1),
  nfold = 5,
  seed = 1,
  intseed = 1
)
cv

Version 3.2 super-level methods

Model-selection functions accept the super-level method arguments where applicable.

search_snmf <- ncompsearch(
  X,
  comps = list(1:3, 1:3),
  criterion = "BIC",
  sprmethod = "sNMF",
  nneg = "posneg",
  intseed = 1
)

Computational recommendations

• Start with a small candidate grid.
• Use BIC to screen candidate models before cross-validation.
• Fix seed for fold allocation and intseed for model estimation.
• Keep expensive searches as explicit user actions rather than vignette build steps.

Session information

sessionInfo()

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