Stepwise classification of cancer samples using multiple data sets. This package implements the classification strategy using two heterogeneous data sets without actually combining them. Package uses the data type for which full measurements are available at the first stage, and the data type for which only partial measurements are available at the second stage. For incoming new samples package quantifies how much improvement will be obtained if covariates of new samples for the data types at the second stage are measured. This packages suits for the application where study goal is not only obtain high classification accuracy, but also requires economically cheap classifier.
|Date of publication||None|
|Maintainer||Askar Obulkasim <firstname.lastname@example.org>|
Classifier: A function to perform classification task.
Classifier.par: A function to perform classification task by multi-core...
CNS: Central Nervous System (CNS) cancer data set.
Curve.generator: A function to generate accuracy curve by passing different...
Proximity: A function to calculate the proximity matrix.
RS.generator: A function to generate the reclassification score.
Step.pred: A function to generate RS cutoff point based the given...
stepwiseCM-package: Stepwise classification of cancer samples using...