R package superpc: Supervised principal components. Supervised principal components for regression and survival analsysis. Especially useful for high-dimnesional data, including microarray data. This function uses a form of cross-validation to estimate the optimal feature threshold in supervised principal components. To avoid prolems with fitting Cox models to samll validation datastes, it uses the ”pre-validation” approach of Tibshirani and Efron (2002

References in zbMATH (referenced in 8 articles )

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  1. Rodríguez-Girondo, Mar; Salo, Perttu; Burzykowski, Tomasz; Perola, Markus; Houwing-Duistermaat, Jeanine; Mertens, Bart: Sequential double cross-validation for assessment of added predictive ability in high-dimensional omic applications (2018)
  2. Miecznikowski, Jeffrey C.; Gaile, Daniel P.; Chen, Xiwei; Tritchler, David L.: Identification of consistent functional genetic modules (2016)
  3. Boulesteix, Anne-Laure; Hothorn, Torsten: Testing the additional predictive value of high-dimensional molecular data (2010) ioport
  4. Bøvelstad, Hege M.; Nygård, Ståle; Borgan, ørnulf: Survival prediction from clinico-genomic models - a comparative study (2009) ioport
  5. Klein, Hans-Ulrich; Ruckert, Christian; Kohlmann, Alexander; Bullinger, Lars; Thiede, Christian; Haferlach, Torsten; Dugas, Martin: Quantitative comparison of microarray experiments with published leukemia related gene expression signatures (2009) ioport
  6. Lama, Nicola; Boracchi, Patrizia; Biganzoli, Elia: Exploration of distributional models for a novel intensity-dependent normalization procedure in censored gene expression data (2009)
  7. Höfling, Holger; Tibshirani, Robert: A study of pre-validation (2008)
  8. Tibshirani, Robert J.; Efron, Brad: Pre-validation and inference in microarrays (2002)