---
title: "Supervised Sparse Soft-Structured PCA with msma"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Supervised Sparse Soft-Structured PCA with msma}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
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.

```{r example}
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`.

```{r session}
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.

```{r selection, eval=FALSE}
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
```