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cancerclass (version 1.16.0)

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

Description

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. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements.

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Version

Version

1.16.0

License

GPL 3

Maintainer

Daniel Kosztyla

Last Published

February 15th, 2017

Functions in cancerclass (1.16.0)

fit

Fitting of a predictor
loo

Leave-one-out cross-validation
predict-methods

Predict Method for 'predictor' Class
nvalidation-class

Class "nvalidation"
predictor-class

Class "predictor"
plot3d

Plot3d method for 'validtion and 'nvalidation' classes
nvalidate

Classification in a multiple random validation protocol in pependence of the number of features used for predictor construction
plot

Plot Method for 'validation, nvalidation, prediction, predictor' Classes
validate

Classification in a Multiple Random Validation Protocol in Dependence of the Training Set Size
cancerclass-package

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

Internal Functions in the cancerclass Package
GOLUB

GOLUB DATA
validation-class

Class "validation"
summary,prediction-methods

Summary Method for 'prediction' Class
prediction-class

Class "prediction"