crossVal
(Package: iterativeBMAsurv) :
Cross Validation for Iterative Bayesian Model Averaging
This function performs k runs of n-fold cross validation on a training dataset for survival analysis on microarray data, where k and n are specified by the user.
● Data Source:
BioConductor
● Keywords: survival
● Alias: crossVal
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Create a visualization of the models and variables selected by the iterative BMA algorithm.
● Data Source:
BioConductor
● Keywords: survival
● Alias: imageplot.iterate.bma.surv
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Survival analysis and variable selection on microarray data. This is a multivariate technique to select a small number of relevant variables (typically genes) to perform survival analysis on microarray data. This function performs the training phase. It repeatedly calls bic.surv from the BMA package until all variables are exhausted. The variables in the dataset are assumed to be pre-sorted by rank.
● Data Source:
BioConductor
● Keywords: multivariate, survival
● Alias: iterateBMAsurv.train
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Survival analysis and variable selection on microarray data. This is a multivariate technique to select a small number of relevant variables (typically genes) to perform survival analysis on microarray data. This function performs the training, prediction, and assessment steps. The data is not assumed to be pre-sorted by rank before this function is called.
● Data Source:
BioConductor
● Keywords: multivariate, survival
● Alias: iterateBMAsurv.train.predict.assess
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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.
● Data Source:
BioConductor
● Keywords: multivariate, survival
● Alias: iterateBMAsurv.train.wrapper
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Internal functions for iterativeBMAsurv, not meant to be called directly.
● Data Source:
BioConductor
● Keywords: internal
● Alias: assignRiskGroup, crossVal.final.calc, crossVal.fold, crossVal.init, crossVal.run, crossVal.tabulate, imageplot.bma.mod, iterateBMAinit, iterativeBMAsurv-internal
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The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data..
● Data Source:
BioConductor
● Keywords: multivariate, survival
● Alias: iterativeBMAsurv, iterativeBMAsurv-package
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predictBicSurv
(Package: iterativeBMAsurv) :
Predicted patient risk scores from iterative Bayesian Model Averaging
This function predicts the risk scores for patient samples in the test set.
● Data Source:
BioConductor
● Keywords: survival
● Alias: predictBicSurv
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This function assigns a risk group (high-risk or low-risk) to each patient sample in the test set based on the value of the patient's predicted risk score. The cutPoint between high-risk and low-risk is designated by the user.
● Data Source:
BioConductor
● Keywords: survival
● Alias: predictiveAssessCategory
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printTopGenes
(Package: iterativeBMAsurv) :
Write a training set including the top-ranked G variables from a sorted matrix to file
This function takes a matrix of rank-ordered variables and writes a training set containing the top G variables in the matrix to file.
● Data Source:
BioConductor
● Keywords: print, univar
● Alias: printTopGenes
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