CrossVal-package: Cross validation of high-throughput prediction algorithms

Description Details Author(s) References See Also


The CrossValidate package provides generic tools for performing cross-validation on classification methods in the context of high-throughput data sets such as those produced by gene expression profiling. In order to use a classifier with this implementation of cross-validation, you must first prepare a pair of functions (one for learning models from training data, and one for making predictions on test data). These functions, along with any required meta-parameters, are used to create an object of the Modeler-class. That object is then passed to the CrossValidate function along with the full training data set. The full data set is then repeatedly split into its own training and test sets; you can specify the fraction to be used for training and the number of iterations. The result is a detailed look at the accuracy, sensitivity, specificity, and positive and negative predictive value of the model, as estimated by cross-validation.


Package: CrossValidate
Type: Package
Version: 1.0.1
Date: 2012-05-04
License: Artistic-2.0
LazyLoad: yes


Kevin R. Coombes


Braga-Neto U, Dougherty ER.
Is cross-validation valid for small-sample microarray classification?
Bioinformatics, 2004; 20:374–380.

Jiang W, Varma S, Simon R.
Calculating confidence intervals for prediction error in microarray classification using resampling.
Stat Appl Genet Mol Biol. 2008; 7:Article8.

Fu LM, Youn ES.
Improving reliability of gene selection from microarray functional genomics data.
IEEE Trans Inf Technol Biomed. 2003; 7:191–6.

Man MZ, Dyson G, Johnson K, Liao B.
Evaluating methods for classifying expression data.
J Biopharm Stat. 2004; 14:1065–84.

Fu WJ, Carroll RJ, Wang S.
Estimating misclassification error with small samples via bootstrap cross-validation.
Bioinformatics, 2005; 21:1979–86.

Ancona N, Maglietta R, Piepoli A, D'Addabbo A, Cotugno R, Savino M, Liuni S, Carella M, Pesole G, Perri F.
On the statistical assessment of classifiers using DNA microarray data.
BMC Bioinformatics, 2006; 7:387.

Lecocke M, Hess K.
An empirical study of univariate and genetic algorithm-based feature selection in binary classification with microarray data.
Cancer Inform, 2007; 2:313–27.

Lee S.
Mistakes in validating the accuracy of a prediction classifier in high-dimensional but small-sample microarray data.
Stat Methods Med Res, 2008; 17:635–42.

See Also

The Modeler-package contains numerous classification methods that have been adapted to work within this general cross-validation framework, including: K nearest neighbors (learnKNN), recursive partitioning and regression trees (learnRPART), random forests (learnRF) neural networks (learnNNET), support vector machines (learnSVM), compound covariate predictors (learnCCP), and the TailRank test (learnTailRank).

CrossValidate documentation built on Aug. 4, 2017, 3:01 p.m.