Description Usage Arguments Details Value Note References See Also Examples
View source: R/iterateBMAsurv.R
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
presorted 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 
maxNvar 
A number indicating the maximum number of variables used in
each iteration of 
maxIter 
A number indicating the maximum iterations of 
thresProbne0 
A number specifying the threshold for the posterior
probability that each variable (gene) is nonzero (in
percent). Variables (genes) with such posterior
probability less than this threshold are dropped in
the iterative application of 
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 
.
obj 
An object of class

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. 111196, 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: 433448.
Yeung, K.Y., Bumgarner, R.E. and Raftery, A.E. (2005) Bayesian Model Averaging: Development of an improved multiclass, gene selection and classification tool for microarray data. Bioinformatics 21: 23942402.
iterateBMAsurv.train.predict.assess
,
iterateBMAsurv.train
,
predictiveAssessCategory
,
singleGeneCoxph
,
trainData
,
trainSurv
,
trainCens
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  library (BMA)
library(iterativeBMAsurv)
data(trainData)
data(trainSurv)
data(trainCens)
## Training data should be presorted 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

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