iterativeBMA-package: The Iterative Bayesian Model Averaging (BMA) algorithm
In iterativeBMA: The Iterative Bayesian Model Averaging (BMA) algorithm

Description Details Author(s) References See Also Examples

The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402).

Package:	iterativeBMA
Type:	Package
Version:	0.1.0
Date:	2005-12-30
License:	GPL version 2 or higher

The function iterateBMAglm.train selects relevant variables by iteratively applying the bic.glm function from the BMA package. The data is assumed to consist of two classes. The function iterateBMAglm.train.predict combines the training and prediction phases, and returns the predicted posterior probabilities that each test sample belongs to class 1. The function iterateBMAglm.train.predict.test combines the training, prediction and test phases, and returns a list consisting of the numbers of selected genes and models using the training data, the number of classification errors and the Brier Score on the test set.

Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter

Maintainer: Ka Yee Yeung <kayee@u.washington.edu>

Yeung, K.Y., Bumgarner, R.E. and Raftery, A.E. (2005) Bayesian Model Averaging: Development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 21: 2394-2402.

iterateBMAglm.train.predict, iterateBMAglm.train.predict.test, bma.predict, brier.score

library (Biobase)
library (BMA)
library (iterativeBMA)
data(trainData)
data(trainClass)

## training phase: select relevant genes
ret.bic.glm <- iterateBMAglm.train (train.expr.set=trainData, trainClass, p=100)

## get the selected genes with probne0 > 0
ret.gene.names <- ret.bic.glm$namesx[ret.bic.glm$probne0 > 0]

data (testData)

## get the subset of test data with the genes from the last iteration of bic.glm
curr.test.dat <- t(exprs(testData)[ret.gene.names,])

## to compute the predicted probabilities for the test samples
y.pred.test <- apply (curr.test.dat, 1, bma.predict, postprobArr=ret.bic.glm$postprob, mleArr=ret.bic.glm$mle)

## compute the Brier Score if the class labels of the test samples are known
data (testClass)
brier.score (y.pred.test, testClass)

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