In bbuchsbaum/multivarious: Extensible Data Structures for Multivariate Analysis

knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width=6, fig.height=4)
# Assuming necessary multivarious functions are loaded
# e.g., via devtools::load_all() or library(multivarious)
library(multivarious)
library(tibble) # For summary output

The composed partial projector (compose_partial_projector) lets you snap together any number of ordinary projector objects (PCA, PLS, cPCA++, block projectors, ...) and treat the whole chain as if it were a single map from the original input space to the final output space:

[ \mathbb R^{p_{\text{orig}}} \longrightarrow \mathbb R^{q_{\text{final}}} ]

Typical Motives:

| Why compose? | What you get | |---------------------------------------------------------------------|-----------------------------------------------------------------------| | Pre-whitening, centring or wavelet-decomposition before the "real" model | Keep the preparation and the model in one tidy object. | | Block-wise modelling (e.g. one PCA per sensor block) | Treat the concatenation of block-specific results as a single projector. | | Dimensionality milk-run – reduce > filter > reduce again | A single set of scores from the final stage to feed to a classifier. |

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1. Quick start – two PCAs in series

Let's compose two PCA steps:

set.seed(1)

X  <- matrix(rnorm(30*15), 30, 15)   # raw data, 30 samples, 15 variables
p1 <- pca(X, ncomp = 8)              # first reduction: 15 -> 8 components
p2 <- pca(scores(p1), ncomp = 7)     # second reduction: 8 -> 4 components

# Compose the two projectors
pipe <- compose_partial_projector(
           first  = p1,
           second = p2)

print(pipe)

# Project original data through the entire pipeline
S <- project(pipe, X)          # 30 × 4 scores – as if the two steps were one
dim(S)

# Get a summary of the pipeline stages
summary(pipe)

The summary() output provides a clear overview of the stages, their names, input/output dimensions, and underlying class.

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2. Partial projections – "zoom in" on selected variables

partial_project() works on composed projectors, allowing you to apply projections using only a subset of variables at specific stages.

You supply the colind argument as either:

• A vector: Applies only to the first stage. Subsequent stages receive the full output from the preceding stage.
• A list: One entry per stage. Use NULL for a stage that should receive the full input from the previous stage.

# Example 1: Use only variables 1:5 for the *first* PCA stage.
# The second PCA stage receives the full 8 components from the (partial) first stage.
S15 <- partial_project(pipe, X[, 1:5, drop=FALSE], colind = 1:5)
cat("Dimensions after partial projection (cols 1:5 in first stage):", dim(S15), "\n")

# Example 2: Multi-stage pipeline (conceptual)
# Imagine a 3-stage pipeline: wavelets -> PCA (block1) -> PCA (global)
# pipe2 <- wavelet_projector(...) %>>% 
#          pca(..., ncomp = 10)   %>>% 
#          pca(..., ncomp = 3)

# To focus on coefficients 12:20 *after* the wavelet step (i.e., input to stage 2):
# S_sel <- partial_project(pipe2, X, # Assuming X is appropriate input for wavelets
#                          colind = list(NULL, 12:20, NULL))
# Note: The indices in the list always refer to the dimensions *entering* that specific stage.

Behind the scenes, the composed projector manages the mapping of indices through the pipeline.

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3. Reconstruction & inverse projection

Since each stage typically provides a way to reverse its projection (often via inverse_projection()), the composed projector can also reconstruct the original data from the final scores.

# Reconstruct original data from the final scores 'S'
X_hat <- reconstruct(pipe, S)
cat("Dimensions of reconstructed data:", dim(X_hat), "\n")

# Check reconstruction accuracy
# Note: Since the pipeline involves dimensionality reduction (15 -> 8 -> 4),
# reconstruction will not be exact. The error reflects the information lost.
max_reconstruction_error <- max(abs(X - X_hat))
cat("Maximum absolute reconstruction error:", format(max_reconstruction_error, digits=3), "\n")
# stopifnot(max_reconstruction_error < 1e-5) # Removed: This check is too strict for lossy reconstruction

# Get the overall coefficient matrix (p_orig x q_final)
V <- coef(pipe)
cat("Dimensions of overall coefficient matrix:", dim(V), "\n")

# Get the overall pseudo-inverse matrix (q_final x p_orig)
Vplus <- inverse_projection(pipe)
cat("Dimensions of overall inverse projection matrix:", dim(Vplus), "\n")

Both the forward (coef) and inverse (inverse_projection) matrices for the entire pipeline are calculated and potentially cached for efficiency.

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4. House-keeping helpers

Some useful helper functions:

• %>>%: A pipe operator specifically for composing projectors. It preserves stage names if the projectors are named. r # pipe3 <- pca1 %>>% pca2 %>>% pca3
• truncate(pipe, ncomp = k): Safely reduces the number of components kept from the last stage of the pipeline.
• variables_used(pipe) / vars_for_component(pipe, k): (Potential future helpers) Intended to trace which original variables contribute to the final scores, especially useful if any stages perform variable selection.

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6. Where next?

Composed projectors open up possibilities:

• Combine pre-processing (e.g., centering, scaling), dimensionality reduction (PCA, PLS), and perhaps an orthogonal rotation (Varimax, Procrustes) into a single, deployable modeling artifact.
• Future enhancements might allow tracing the lineage of specific final components back to the exact original variables that contribute most significantly, leveraging the internal index mapping.

Happy composing!

bbuchsbaum/multivarious documentation built on July 16, 2025, 11:04 p.m.