---
title: "Covariance Regularisation"
output: rmarkdown::html_vignette
bibliography: references.bib
vignette: >
  %\VignetteIndexEntry{Covariance Regularisation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

## Motivation

Inverse-regression methods commonly require covariance standardisation. When
predictors are strongly collinear or the predictor dimension is not small
relative to the sample size, the sample covariance matrix may be poorly
conditioned or singular. Regularisation changes the bias-variance trade-off to
obtain a more stable inverse.

`risdr` includes sample covariance, ridge shrinkage, OAS
[@chenetal2010oas], LW [@ledoitwolf2004], and MEC.

## Comparison on a controlled design

```{r}
library(risdr)

sim <- simulate_risdr_data(
  n = 100,
  p = 40,
  d = 2,
  rho = 0.9,
  seed = 7001
)

estimates <- list(
  sample = cov_sample(sim$X),
  ridge = cov_ridge(sim$X, lambda = 0.10),
  oas = cov_oas(sim$X),
  lw = cov_lw(sim$X),
  mec = cov_mec(sim$X, sim$y, nslices = 6)
)

diagnostics <- do.call(
  rbind,
  lapply(names(estimates), function(name) {
    value <- cov_diagnostics(estimates[[name]])
    data.frame(
      estimator = name,
      minimum_eigenvalue = value$min_eigenvalue,
      maximum_eigenvalue = value$max_eigenvalue,
      condition_number = value$condition_number,
      effective_rank = value$effective_rank
    )
  })
)

diagnostics
```

Condition number and effective rank describe numerical behaviour, not
predictive adequacy. They should be interpreted alongside downstream and
subspace metrics.

## Ridge estimator

For sample covariance `\mathbf{S}` and scaled identity target
`\mathbf{T}`, the ridge estimator is

\[
\widehat{\boldsymbol{\Sigma}}_{\lambda}
= (1-\lambda)\mathbf{S}+\lambda\mathbf{T},
\qquad 0\leq\lambda\leq 1.
\]

```{r}
ridge_fit <- fit_risdr(
  sim$X,
  sim$y,
  sdr_method = "sir",
  cov_method = "ridge",
  d = 2,
  d_max = 5,
  cov_args = list(lambda = 0.15)
)
```

## Post-estimation stabilisation

Covariance estimation and numerical stabilisation are separate operations.
An estimated covariance may be stabilised using an eigenvalue floor, an
additive ridge term, or a nearest positive-definite projection.

```{r}
sample_covariance <- cov_sample(sim$X)

floor_covariance <- stabilize_cov(
  sample_covariance,
  method = "eigenfloor",
  eps = 1e-6
)

ridge_covariance <- stabilize_cov(
  sample_covariance,
  method = "ridge",
  lambda = 1e-4
)

nearest_covariance <- stabilize_cov(
  sample_covariance,
  method = "nearest_pd",
  keep_diag = TRUE
)
```

The `fit_risdr()` interface separates these controls:

```{r}
separated_controls <- fit_risdr(
  sim$X,
  sim$y,
  sdr_method = "dr",
  cov_method = "ridge",
  stabilization = "eigenfloor",
  d = 2,
  d_max = 5,
  cov_args = list(lambda = 0.10),
  stabilization_args = list(eps = 1e-7)
)
```

## Legacy Simulation A outputs

```{r}
simulation_path <- system.file(
  "extdata",
  "simulation",
  "simulation_A_final_covariance_DR_summary_tidy.csv",
  package = "risdr"
)
simulation_a <- utils::read.csv(simulation_path)

aggregate(
  cbind(
    mean_subspace_distance,
    mean_RMSE,
    mean_condition_number,
    mean_runtime_seconds
  ) ~ cov_method,
  data = simulation_a,
  FUN = mean
)
```

The supplied Simulation A record favours OAS and ridge for conditioning. Its
prediction columns require rerun because the original script regenerated the
random true basis for the test sample. The table is retained here for
provenance and must not be treated as a newly validated result.

## Maximum Entropy Covariance

The Maximum Entropy Covariance (MEC) estimator follows the covariance
estimation approach proposed by @olorede2019newcovarianceestimatorsufficient
for sufficient dimension reduction in high-dimensional and undersized-sample
settings. The estimator uses response slicing, selects the slice covariance with the largest stabilised log determinant, and combines it with the pooled covariance through convex shrinkage. This definition is authoritative for the package documentation, software papers, and subsequent releases.

## References  
<div id="refs"></div>
