Create a Feature-Based RSA Design

Creates a design for feature-based Representational Similarity Analysis (RSA). You can either supply a similarity matrix S (and optionally select dimensions) or directly supply a feature matrix F.

Usage

feature_rsa_design(
  S = NULL,
  F = NULL,
  labels,
  k = 0,
  max_comps = 10,
  block_var = NULL
)

Arguments

S

A symmetric similarity matrix representing the feature space relationships. If NULL, you must supply F.

F

A feature space matrix (observations by features). If supplied, this overrides S and k.

labels

Vector of labels corresponding to the rows/columns of S or observations of F.

k

Integer specifying the number of feature dimensions to retain when using S. If 0 (default), automatically determines dimensions using eigenvalue threshold > 1 (minimum 2 dimensions kept). This parameter is ignored if F is supplied directly (k becomes ncol(F)).

max_comps

Initial upper limit for the number of components to be derived from the feature space F by subsequent `feature_rsa_model` methods (PCA, PLS). This value is automatically capped by the final feature dimensionality `k`. Default 10.

block_var

Optional blocking variable for cross-validation. If provided and `crossval` is `NULL` in `feature_rsa_model`, a blocked cross-validation scheme will be generated using this vector.

Value

A feature_rsa_design object (S3 class) containing:

S

The input similarity matrix (if used)

F

Feature space projection matrix (k dimensions)

labels

Vector of observation labels

k

The final number of feature dimensions used

max_comps

The upper limit on components (<= k)

block_var

Optional blocking variable for cross-validation

Details

This function defines the feature space representation for the analysis. If F is supplied directly, it is used as-is, and `k` becomes `ncol(F)`. If only S is supplied, an eigen decomposition of S is performed. `k` determines how many eigenvectors form the feature matrix F. If `k=0`, dimensions with eigenvalues > 1 are kept (minimum 2). `max_comps` sets an upper bound for the number of components that model-fitting methods (like PCA, PLS in `feature_rsa_model`) can use, and it cannot exceed the final feature dimensionality `k`.