dot-poissonPearsonResidualsPca: Principal component analysis on Pearson residuals

In rafalab/smallcount: Methods for Small Counts Stored in a Sparse Matrix

Principal component analysis on Pearson residuals

Description

Principal component analysis on Pearson residuals

Usage

.poissonPearsonResidualsPca(y, k)

Arguments

y	Sparse matrix (can be a matrix, dgCMatrix, or SparseMatrix)
k	Number of principal components to return

Value

List with components:

• sdevStandard deviations of principal components

• rotationMatrix of variable loadings (i.e., matrix containing the eigenvectors of the covariance/correlation matrix as columns)

• xMatrix of rotated data (rotated after applying the transformations specified)

rafalab/smallcount documentation built on June 1, 2025, 2:10 p.m.