cancerclass-package: Development and validation of diagnostic tests from...

In cancerclass: Development and validation of diagnostic tests from high-dimensional molecular data

Description

This package implements classification and validation methods for high-dimensional applications, such as gene expression data. The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol [1]. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements.

Details

Package:	cancerclass
Type:	Package
Version:	1.5.1
Date:	2013-09-04
License:	GPL (>=2)

Author(s)

Jan Budczies [e-mail], Daniel Kosztyla [e-mail]

References

[1] Michiels S, Koscielny S, Hill C (2005), Prediction of cancer outcome with microarrays: a multiple random validation strategy, Lancet 365:488-492.

See Also

fit, GOLUB1, loo, nvalidate, nvalidation-class, plot, plot,nvalidation-method, plot,prediction-method, plot,predictor-method, plot,validation-method, plot3d, plot3d,nvalidation-method, plot3d,validation-method, predict, prediction-class, predictor-class, summary, validate, validation-class, cancerclass-internal, ilogit, calc.roc, calc.auc, get.d, get.d2, get.prop, get.ntrain, prepare, filter

cancerclass documentation built on Nov. 8, 2020, 5:31 p.m.