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multiCCA

multiCCA implements multiple canonical correlation analysis (MCCA) for multi-block data. The package provides tools for

• kernel MCCA for repeated-measures data,
• functional MCCA for functional observations,
• automatic tuning of regularization parameters,
• diagnostic tools based on the Hopkins statistic,
• visualization of canonical components.

The implementation is designed for multi-view data integration, where multiple sets of features are observed for the same objects.

Installation

You can install the development version from GitHub:

# install.packages("pak")
pak::pak("Halmaris/multiCCA")

Example

Generate a simple multi-block dataset:

library(multiCCA)

set.seed(1)

n <- 20
T_len <- 10

X <- list(
  lapply(seq_len(n), function(i) matrix(rnorm(T_len * 3), T_len, 3)),
  lapply(seq_len(n), function(i) matrix(rnorm(T_len * 2), T_len, 2))
)

Fit kernel MCCA:

fit <- mcca_fit(
  method = "kernel",
  X = X,
  ncomp = 2
)

fit

Predict canonical component scores:

scores <- predict(fit, X)
head(scores[[1]])

Visualization

Plot canonical components for one block:

plot_mcca_scatter(fit)

Compare canonical components between blocks:

plot_mcca_pair(fit)

Model diagnostics

Evaluate clusterability of canonical representations using the Hopkins statistic:

H <- hopkins_vs_components(fit, max_comp = 2)

plot_hopkins_curve(H)

Author

Tomasz Górecki
Adam Mickiewicz University, Poznań

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.