This function repeatedly calls bic.glm
from the
BMA
package until all variables are exhausted.
The data is assumed to consist of
two classes. Logistic regression is used for classification.
1  iterateBMAglm.wrapper (sortedA, y, nbest=10, maxNvar=30, maxIter=20000, thresProbne0=1)

sortedA 
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 or experiments. 
y 
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 sortedA. Since we assume 2class data, we expect the class vector consists of zero's and one's. 
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 of 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 
In this function, the variables are assumed to be sorted, and
bic.glm
is called repeatedly. In the first application of
the bic.glm
algorithm, the top maxNvar
univariate
ranked genes are used. After each application of the bic.glm
algorithm, the genes with probne0
< thresProbne0
are dropped, and the next univariate ordered genes are added
to the BMA window.
The function iterateBMAglm.train
calls BssWssFast
before
calling this function.
Using this function, users can experiment with alternative
univariate measures.
If all variables are exhausted, an object of class
bic.glm
returned by the last iteration
of bic.glm
. Otherwise, 1 is returned.
The object of class bic.glm
is a list consisting
of the following components:
namesx 
the names of the variables in the last iteration of

postprob 
the posterior probabilities of the models selected. 
deviance 
the estimated model deviances. 
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 nonzero (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
and Biobase
packages are required.
Raftery, A.E. (1995). Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111196, Cambridge, Mass.: Blackwells.
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.
iterateBMAglm.train
,
iterateBMAglm.train.predict
,
iterateBMAglm.train.predict.test
,
BssWssFast
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  library (Biobase)
library (BMA)
library (iterativeBMA)
data(trainData)
data(trainClass)
## Use the BSS/WSS ratio to rank all genes in the training data
sorted.vec < BssWssFast (t(exprs(trainData)), trainClass, numClass = 2)
## get the top ranked 50 genes
sorted.train.dat < t(exprs(trainData[sorted.vec$ix[1:50], ]))
## run iterative bic.glm
ret.bic.glm < iterateBMAglm.wrapper (sorted.train.dat, y=trainClass)
## The above commands are equivalent to the following
ret.bic.glm < iterateBMAglm.train (train.expr.set=trainData, trainClass, p=50)

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