Perform PCA on data with missing values in R
R C++
Switch branches/tags
Nothing to show
Clone or download
Latest commit 08a44e5 Jan 30, 2016


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