Predicted Probabilities from Bayesian Model Averaging

Description

This function computes the predicted posterior probability that each test sample belongs to class 1. It assumes 2-class data, and requires the true class labels to be known.

Usage

bma.predict (newdataArr, postprobArr, mleArr)

Arguments

Details

Let Y be the response variable (class labels for samples in our case). In Bayesian Model Averaging (BMA), the posterior probability of Y=1 given the training set is the weighted average of the posterior probability of Y=1 given the training set and model M multiplied by the posterior probability of model M given the training set, summing over a set of models M.

Value

A real number between zero and one, representing the predicted posterior probability.

References

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.

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

brier.score, iterateBMAglm.train

Examples

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(iterativeBMA)
Loading required package: BMA
Loading required package: survival
Loading required package: leaps
Loading required package: robustbase

Attaching package: 'robustbase'

The following object is masked from 'package:survival':

    heart

Loading required package: inline
Loading required package: rrcov
Scalable Robust Estimators with High Breakdown Point (version 1.3-11)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:robustbase':

    rowMedians

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/iterativeBMA/bma_predict.Rd_%03d_medium.png", width=480, height=480)
> ### Name: bma.predict
> ### Title: Predicted Probabilities from Bayesian Model Averaging
> ### Aliases: bma.predict
> ### Keywords: classif
> 
> ### ** Examples
> 
> 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)
[1] "5: explored up to variable ## 100"
There were 50 or more warnings (use warnings() to see the first 50)
> 
> ## 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)
[1] 2.106802
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>