README.md

In hredestig/pcaMethods: A collection of PCA methods

pcaMethods

R package for performing principal component analysis PCA with applications to missing value imputation. Provides a single interface to performing PCA using

• SVD: a fast method which is also the standard method in R but which is not applicable for data with missing values.
• NIPALS: an iterative fast method which is applicable also to data with missing values.
• PPCA: Probabilistic PCA which is applicable also on data with missing values. Missing value estimation is typically better than NIPALS but also slower to compute and uses more memory. A port to R of the implementation by Jakob Verbeek.
• BPCA: Bayesian PCA which performs very well in the presence of missing values but is slower than PPCA. A port of the matlab implementation by Shigeyuki Oba.
• NLPCA: Non-linear PCA which can find curves in data and in presence of such can perform accurate missing value estimation. Matlab port of the implementation by Mathias Scholz.

pcaMethods is a Bioconductor package and you can install it by

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("pcaMethods")

Documentation

browseVignettes("pcaMethods")
?<function_name>

hredestig/pcaMethods documentation built on Sept. 30, 2023, 10:38 a.m.