Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- de Campos, Luis M.; Cano, Andrés; Castellano, Javier G.; Moral, Serafín: Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions (2019)
- Imbert, Alyssa; Vialaneix, Nathalie: Exploring, handling, imputing and evaluating missing data in statistical analyses: a review of existing approaches (2018)
- Mair, Patrick: Modern psychometrics with R (2018)
- Islam, Shofiqul; Anand, Sonia; Hamid, Jemila; Thabane, Lehana; Beyene, Joseph: Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration (2017)
- Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016) not zbMATH
- Samuel V. Scarpino, Ross Gillette, David Crews: multiDimBio: An R Package for the Design, Analysis, and Visualization of Systems Biology Experiments (2014) arXiv