Supervised Sparse Soft-Structured PCA with msma
In msma: Multiblock Sparse Multivariable Analysis

knitr::opts_chunk$set(collapse=TRUE, comment="#>")
library(msma)

Overview

Version 4.0 adds opt-in S4PCA for a single X matrix. The default structure.method="none" preserves Version 3.2 behavior.

set.seed(4)
X <- scale(matrix(rnorm(50*12),50,12))
Z <- as.numeric(scale(.7*X[,1]-.4*X[,5]+rnorm(50)))
fit <- msma(X,Z=Z,comp=3,lambdaX=.1,muX=.8,
            structure.method="soft",gammaX=.1,niterS4=30,
            scaling=FALSE,intseed=4)
fit$W
fit$overlap_all
fit$overlap_selected
fit$diagnostics

Hard exclusion is selected with structure.method="exclusive". Version 4.0 initially limits structured PCA to single-matrix PCA, scalar comp and lambdaX, and vector or one-column Z.

sessionInfo()

Repeated split conformal model selection

The candidate grid can be evaluated by repeated calibration splits. The code below uses a deliberately small grid for illustration.

selection <- s4pca_conformal_select(
  X, Z,
  lambdaX = c(0.05, 0.10),
  gammaX = c(0, 0.10),
  comp = 2:3,
  repeats = 5,
  alpha = 0.10,
  max_overlap = 0.50,
  muX = 0.8,
  intseed = 4
)
selection$selected
selection$summary
selection$fit