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iterativeBMAsurv (version 1.30.0)

The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis

Description

The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data.

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Version

Version

1.30.0

License

GPL (>= 2)

Maintainer

Ka Yeung

Last Published

February 15th, 2017

Functions in iterativeBMAsurv (1.30.0)

iterativeBMAsurv-internal

Internal functions for iterativeBMAsurv
printTopGenes

Write a training set including the top-ranked G variables from a sorted matrix to file
iterativeBMAsurv-package

The Iterative Bayesian Model Averaging (BMA) algorithm for survival analysis
iterateBMAsurv.train

Iterative Bayesian Model Averaging: training
testData

Sample Test Data for the Iterative BMA Algorithm for Survival Analysis
predictiveAssessCategory

Risk Groups: assignment of patient test samples
imageplot.iterate.bma.surv

An image plot visualization tool
testSurv

Sample Test Data for the Iterative BMA Algorithm for Survival Analysis
trainData

Sample Training Data for the Iterative BMA Algorithm for Survival Analysis
testCens

Sample Test Data for the Iterative BMA Algorithm for Survival Analysis
predictBicSurv

Predicted patient risk scores from iterative Bayesian Model Averaging
trainSurv

Sample Training Data for the Iterative BMA Algorithm for Survival Analysis
crossVal

Cross Validation for Iterative Bayesian Model Averaging
trainCens

Sample Training Data for the Iterative BMA Algorithm for Survival Analysis
singleGeneCoxph

Univariate Cox Proportional Hazards Model for selecting top log-ranked predicitve variables
iterateBMAsurv.train.wrapper

Iterative Bayesian Model Averaging: training
iterateBMAsurv.train.predict.assess

Iterative Bayesian Model Averaging: training, prediction, assessment