iterateBMAsurv.train.wrapper: Iterative Bayesian Model Averaging: training
In iterativeBMAsurv: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis

Description Usage Arguments Details Value Note References See Also Examples

This function is a wrapper for iterateBMAsurv.train, which repeatedly calls bic.surv from the BMA package until all variables are exhausted. At the point when this function is called, the variables in the dataset are assumed to be pre-sorted by rank.

1 2	iterateBMAsurv.train.wrapper (x, surv.time, cens.vec, nbest=10, maxNvar=25, maxIter=200000, thresProbne0=1, verbose=FALSE, suff.string="")

`x`	Data matrix where columns are variables and rows are observations. The variables (columns) are assumed to be sorted using a univariate measure. In the case of gene expression data, the columns (variables) represent genes, while the rows (observations) represent samples.
`surv.time`	Vector of survival times for the patient samples. Survival times are assumed to be presented in uniform format (e.g., months or days), and the length of this vector should be equal to the number of rows in x.
`cens.vec`	Vector of censor data for the patient samples. In general, 0 = censored and 1 = uncensored. The length of this vector should equal the number of rows in x and the number of elements in surv.time.
`nbest`	A number specifying the number of models of each size returned to `bic.surv` in the `BMA` package. The default is 10.
`maxNvar`	A number indicating the maximum number of variables used in each iteration of `bic.surv` from the `BMA` package. The default is 25.
`maxIter`	A number indicating the maximum iterations of `bic.surv`. The default is 200000.
`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.surv`. The default is 1 percent.
`verbose`	A boolean variable indicating whether or not to print interim information to the console. The default is FALSE.
`suff.string`	A string for writing to file.

In this wrapper function for iterateBMAsurv.train, the variables are assumed to be sorted, and bic.surv is called repeatedly until all the variables have been exhausted. In the first application of the bic.surv algorithm, the top maxNvar univariate ranked genes are used. After each application of the bic.surv algorithm, the genes with probne0 < thresProbne0 are dropped, and the next univariate ordered genes are added to the bic.surv window. The function iterateBMAsurv.train.predict.assess calls SingleGeneCoxph before calling this function. Using this function directly, users can experiment with alternative univariate measures.

If maxIter is reached or the iterations stop before all variables are exhausted, -1 is returned. If all variables are exhausted, two items are returned:

curr.names

A vector containing the names of the variables (genes) from the last iteration of bic.surv

obj

An object of class bic.surv returned by the last iteration of bic.surv. The object of class bic.surv is a list consisting of the following components:

namesx: the names of the variables in the last iteration of bic.surv.
postprob: The posterior probabilities of the models selected.
label: Labels identifying the models selected.
bic: Values of BIC for the models.
size: The number of independent variables in each of the models.
which: A logical matrix with one row per model and one column per variable indicating whether that variable is in the model.
probne0: The posterior probability that each variable is non-zero (in percent).
postmean: The posterior mean of each coefficient (from model averaging).
postsd: The posterior standard deviation of each coefficient (from model averaging).
condpostmean: The posterior mean of each coefficient conditional on the variable being included in the model.
condpostsd: The posterior standard deviation of each coefficient conditional on the variable being included in the model.
mle: Matrix with one row per model and one column per variable giving the maximum likelihood estimate of each coefficient for each model.
se: Matrix with one row per model and one column per variable giving the standard error of each coefficient for each model.
reduced: A logical indicating whether any variables were dropped before model averaging.
dropped: A vector containing the names of those variables dropped before model averaging.
call: The matched call that created the bma.lm object.

The BMA package is required.

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.

iterateBMAsurv.train.predict.assess, iterateBMAsurv.train, predictiveAssessCategory, singleGeneCoxph, trainData, trainSurv, trainCens

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

## Training data should be pre-sorted before beginning

## Run iterative bic.surv, using nbest=5 for fast computation
ret.list <- iterateBMAsurv.train.wrapper (x=trainData, surv.time=trainSurv, cens.vec=trainCens, nbest=5)

## Extract the 'bic.surv' object
ret.bma <- ret.list$obj

## Extract the names of the genes from the last iteration of 'bic.surv'
gene.names <- ret.list$curr.names