iterativeBMA (version 1.30.0)

predictBicSurv: Predicted patient risk scores from iterative Bayesian Model Averaging

Description

This function predicts the risk scores for patient samples in the test set.

Usage

predictBicSurv(newdata.vec, postprob.vec, mle.mat)

Arguments

newdata.vec

A vector consisting of the data from a test sample.

postprob.vec

A vector consisting of the posterior probability of each BMA selected model.

mle.mat

A matrix with one row per model and one column per variable giving the maximum likelihood estimate of each coefficient for each bic.surv selected model.

Value

• A real number representing the predicted risk score of a given patient sample.

Details

The function begins by computing the risk score of each model k in the selected set of models M. The risk score for a model k = sum (coefficient in model k * corresponding expression level in newdata.vec). The function then predicts a patient risk score by summing the product of the posterior probability of model k and the risk score of model k over all models in M. In other words, predicted patient risk score = sum (postprob model k * risk score model k). This function is called by iterateBMAsurv.train.predict.assess.

References

Annest, A., Yeung, K.Y., Bumgarner, R.E., and Raftery, A.E. (2008). Iterative Bayesian Model Averaging for Survival Analysis. Manuscript in Progress. Raftery, A.E. (1995). Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111-196, Cambridge, Mass.: Blackwells. Volinsky, C., Madigan, D., Raftery, A., and Kronmal, R. (1997) Bayesian Model Averaging in Proprtional Hazard Models: Assessing the Risk of a Stroke. Applied Statistics 46: 433-448. 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.

See Also

iterateBMAsurv.train.predict.assess, iterateBMAsurv.train.wrapper, predictiveAssessCategory, trainData, trainSurv, trainCens, testData

Examples

library (BMA)
library (iterativeBMAsurv)
data(trainData)
data(trainSurv)
data(trainCens)
data(testData)

## Training phase: select relevant genes. Assume the training data is sorted 
## and includes the desired number of top-ranked genes.
ret.list <- iterateBMAsurv.train.wrapper (x=trainData, surv.time=trainSurv, cens.vec=trainCens, nbest=5)
ret.bma <- ret.list$obj

## Get the selected genes with probne0 > 0
selected.genes <- ret.list$curr.names[ret.bma$probne0 > 0]

## Get the subset of test data with the genes from the last iteration of bic.surv
curr.test.dat <- testData [, selected.genes]

## Compute the predicted risk scores for the test samples
y.pred.test <- apply (curr.test.dat, 1, predictBicSurv, postprob.vec=ret.bma$postprob, mle.mat=ret.bma$mle)