vsp: Semi-Parametric Factor Analysis via Vintage Sparse PCA
In RoheLab/vsp: Vintage Sparse PCA for Semi-Parametric Factor Analysis

View source: R/vsp.R

vsp	R Documentation

Semi-Parametric Factor Analysis via Vintage Sparse PCA

Description

This code implements TODO.

Usage

vsp(x, rank, ...)

## Default S3 method:
vsp(x, rank, ...)

## S3 method for class 'matrix'
vsp(
  x,
  rank,
  ...,
  center = FALSE,
  recenter = FALSE,
  degree_normalize = TRUE,
  renormalize = FALSE,
  tau_row = NULL,
  tau_col = NULL,
  kaiser_normalize_u = FALSE,
  kaiser_normalize_v = FALSE,
  rownames = NULL,
  colnames = NULL,
  match_columns = TRUE
)

## S3 method for class 'Matrix'
vsp(
  x,
  rank,
  ...,
  center = FALSE,
  recenter = FALSE,
  degree_normalize = TRUE,
  renormalize = FALSE,
  tau_row = NULL,
  tau_col = NULL,
  kaiser_normalize_u = FALSE,
  kaiser_normalize_v = FALSE,
  rownames = NULL,
  colnames = NULL,
  match_columns = TRUE
)

## S3 method for class 'dgCMatrix'
vsp(
  x,
  rank,
  ...,
  center = FALSE,
  recenter = FALSE,
  degree_normalize = TRUE,
  renormalize = FALSE,
  tau_row = NULL,
  tau_col = NULL,
  kaiser_normalize_u = FALSE,
  kaiser_normalize_v = FALSE,
  rownames = NULL,
  colnames = NULL,
  match_columns = TRUE
)

## S3 method for class 'igraph'
vsp(x, rank, ..., edge_weights = 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.
`center`	Should the adjacency matrix be row and column centered? Defaults to `FALSE`.
`recenter`	Should the varimax factors be re-centered around the original factor means? Only used when `center = TRUE`, defaults to `FALSE`.
`degree_normalize`	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.
`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.
`tau_row`	Row regularization term. Default is `NULL`, in which case we use the row degree. Ignored when `degree_normalize = FALSE`.
`tau_col`	Column regularization term. Default is `NULL`, in which case we use the column degree. Ignored when `degree_normalize = FALSE`.
`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`.
`match_columns`	Should the columns of `Y` be re-ordered such that `Y[, i]` corresponds to `Z[, i]` to the extent possible? Defaults to `TRUE`. Typically helps with interpretation, and often makes `B` more diagonally dominant.
`edge_weights`	When `x` is an igraph::igraph, an edge attribute to use to form a weighted adjacency matrix.