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R: Iterative Bayesian Model Averaging: training and prediction

iterateBMAglm.train.predict

R Documentation

Iterative Bayesian Model Averaging: training and prediction

Description

Classification and variable selection on microarray data. This is a multivariate technique to select a small number of relevant variables (typically genes) to classify microarray samples. This function performs the training, and prediction steps. The data is assumed to consist of two classes. Logistic regression is used for classification.

Usage

iterateBMAglm.train.predict (train.expr.set, test.expr.set, train.class, p=100, nbest=10, maxNvar=30, maxIter=20000, thresProbne0=1)

Arguments

`train.expr.set`	an `ExpressionSet` object. We assume the rows in the expression data represent variables (genes), while the columns represent samples or experiments. This training data is used to select relevant genes (variables) for classification.
`test.expr.set`	an `ExpressionSet` object. We assume the rows in the expression data represent variables (genes), while the columns represent samples or experiments. The variables selected using the training data is used to classify samples on this test data.
`train.class`	class vector for the observations (samples or experiments) in the training data. Class numbers are assumed to start from 0, and the length of this class vector should be equal to the number of rows in train.dat. Since we assume 2-class data, we expect the class vector consists of zero's and one's.
`p`	a number indicating the maximum number of top univariate genes used in the iterative BMA algorithm. This number is assumed to be less than the total number of genes in the training data. A larger p usually requires longer computational time as more iterations of the BMA algorithm are potentially applied. The default is 100.
`nbest`	a number specifying the number of models of each size returned to `bic.glm` in the `BMA` package. The default is 10.
`maxNvar`	a number indicating the maximum number of variables used in each iteration of `bic.glm` from the `BMA` package. The default is 30.
`maxIter`	a number indicating the maximum of iterations of `bic.glm`. The default is 20000.
`thresProbne0`	a number specifying the threshold for the posterior probability that each variable (gene) is non-zero (in percent). Variables (genes) with such posterior probability less than this threshold are dropped in the iterative application of `bic.glm`. The default is 1 percent.

Details

This function consists of the training phase and the prediction phase. The training phase consists of first ordering all the variables (genes) by a univariate measure called between-groups to within-groups sums-of-squares (BSS/WSS) ratio, and then iteratively applying the bic.glm algorithm from the BMA package. The prediction phase uses the variables (genes) selected in the training phase to classify the samples in the test set.

Value

A vector consisting of the predicted probability that each test sample belongs to class 1 is returned.

Note

The BMA and Biobase packages are required.

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.

Examples

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

ret.vec <- iterateBMAglm.train.predict (train.expr.set=trainData, test.expr.set=testData, trainClass, p=100)

## compute the Brier Score
data (testClass)
brier.score (ret.vec, 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/iterateBMAglm_train_predict.Rd_%03d_medium.png", width=480, height=480)
> ### Name: iterateBMAglm.train.predict
> ### Title: Iterative Bayesian Model Averaging: training and prediction
> ### Aliases: iterateBMAglm.train.predict
> ### Keywords: multivariate classif
> 
> ### ** Examples
> 
> library (Biobase)
> library (BMA)
> library (iterativeBMA)
> data(trainData)
> data(trainClass)
> data (testData)
> 
> ret.vec <- iterateBMAglm.train.predict (train.expr.set=trainData, test.expr.set=testData, trainClass, p=100)
[1] "5: explored up to variable ## 100"
There were 50 or more warnings (use warnings() to see the first 50)
> 
> ## compute the Brier Score
> data (testClass)
> brier.score (ret.vec, testClass)
[1] 2.221017
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>