lfa: Logistic factor analysis

In StoreyLab/lfa: Logistic Factor Analysis for Categorical Data

Logistic factor analysis

Description

Fit logistic factor model of dimension d to binomial data. Computes d - 1 singular vectors followed by intercept.

Usage

lfa(
  X,
  d,
  adjustments = NULL,
  override = FALSE,
  safety = FALSE,
  rspectra = FALSE,
  ploidy = 2,
  tol = .Machine$double.eps,
  m_chunk = 1000
)

Arguments

X	A matrix of SNP genotypes, i.e. an integer matrix of 0's, 1's, 2's and NAs. BEDMatrix is supported. Sparse matrices of class Matrix are not supported (yet).
d	Number of logistic factors, including the intercept
adjustments	A matrix of adjustment variables to hold fixed during estimation. Number of rows must equal number of individuals in X. These adjustments take the place of LFs in the output, so the number of columns must not exceed d-2 to allow for the intercept and at least one proper LF to be included. When present, these adjustment variables appear in the first columns of the output. Not supported when X is a BEDMatrix object.
override	Optional boolean passed to trunc_svd() to bypass its Lanczos bidiagonalization SVD, instead using corpcor::fast.svd(). Usually not advised unless encountering a bug in the SVD code. Ignored if X is a BEDMatrix object.
safety	Optional boolean to bypass checks on the genotype matrices, which require a non-trivial amount of computation. Ignored if X is a BEDMatrix object.
rspectra	If TRUE, use RSpectra::svds() instead of default trunc_svd() or corpcor::fast.svd() options. Ignored if X is a BEDMatrix object.
ploidy	Ploidy of data, defaults to 2 for bi-allelic unphased SNPs
tol	Tolerance value passed to trunc_svd() Ignored if X is a BEDMatrix object.
m_chunk	If X is a BEDMatrix object, number of loci to read per chunk (to control memory usage).

Details

Genotype matrix should have values in 0, 1, 2, or NA. The coding of the SNPs (which case is 0 vs 2) does not change the output.

Value

The matrix of logistic factors, with individuals along rows and factors along columns. The intercept appears at the end of the columns, and adjustments in the beginning if present.

Examples

LF <- lfa(hgdp_subset, 4)
dim(LF)
head(LF)

StoreyLab/lfa documentation built on March 7, 2024, 9:59 p.m.