Perform varimax rotation on a low rank matrix factorization

Usage

# S3 method for svd_like
vsp(
  x,
  rank,
  ...,
  centerer = NULL,
  scaler = NULL,
  recenter = FALSE,
  renormalize = FALSE,
  kaiser_normalize_u = FALSE,
  kaiser_normalize_v = FALSE,
  rownames = NULL,
  colnames = NULL
)

Arguments

x: Either a graph adjacency matrix, igraph::igraph or tidygraph::tbl_graph. If x is a matrix or Matrix::Matrix then x[i, j] should correspond to the edge going from node i to node j.
rank: The number of factors to calculate.
...: These dots are for future extensions and must be empty.
centerer: TODO
scaler: TODO
recenter: Should the varimax factors be re-centered around the original factor means? Only used when center = TRUE, defaults to FALSE.
renormalize: Should the regularized graph laplacian be used instead of the raw adjacency matrix? Defaults to TRUE. If center = TRUE, A will first be centered and then normalized.
kaiser_normalize_u: Whether or not to use Kaiser normalization when rotating the left singular vectors U. Defaults to FALSE.
kaiser_normalize_v: Whether or not to use Kaiser normalization when rotating the right singular vectors V. Defaults to FALSE.
rownames: Character vector of row names of x. These row names are propagated into the row names of the U and Z. Defaults to NULL.
colnames: Character vector of column names of x. These column names are propagated into the row names of the V and Y. Defaults to NULL.

Examples


library(LRMF3)
library(RSpectra)

s <- svds(ml100k, k = 2)
mf <- as_svd_like(s)
fa <- vsp(mf, rank = 2)