BssWssFast
(Package: iterativeBMA) :
Between-groups sum-of-squares to within-groups sum-of-squares
This is a univariate technique to select relevant genes in classification of microarray data. In classifying samples of microarray data, this ratio is computed for each gene. A large between-groups to within-groups sum-of-squares ratio indicates a potentially relevant gene.
● Data Source:
BioConductor
● Keywords: univar
● Alias: BssWssFast
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bma.predict
(Package: iterativeBMA) :
Predicted Probabilities from Bayesian Model Averaging
This function computes the predicted posterior probability that each test sample belongs to class 1. It assumes 2-class data, and requires the true class labels to be known.
● Data Source:
BioConductor
● Keywords: classif
● Alias: bma.predict
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brier.score
(Package: iterativeBMA) :
Brier Score: assessment of prediction accuracy
The Brier Score is a probabilistic number of errors that takes the predicted probabilities into consideration. A small Brier Score indicates high prediction accuracy. This function assumes 2-class data, and requires the true class labels to be known.
● Data Source:
BioConductor
● Keywords: classif
● Alias: brier.score
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Create a visualization of the models and variables selected by the iterative BMA algorithm.
● Data Source:
BioConductor
● Keywords: classif
● Alias: imageplot.iterate.bma
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iterateBMAglm.train
(Package: iterativeBMA) :
Iterative Bayesian Model Averaging: training step
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 phase. The data is assumed to consist of two classes. Logistic regression is used for classification.
● Data Source:
BioConductor
● Keywords: classif, multivariate
● Alias: iterateBMAglm.train
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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.
● Data Source:
BioConductor
● Keywords: classif, multivariate
● Alias: iterateBMAglm.train.predict
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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, prediction and testing steps. The data is assumed to consist of two classes, and the classes of the test data is assumed to be known. Logistic regression is used for classification.
● Data Source:
BioConductor
● Keywords: classif, multivariate
● Alias: iterateBMAglm.train.predict.test
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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.
● Data Source:
BioConductor
● Keywords: classif, multivariate
● Alias: iterateBMAglm.wrapper
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Internal functions for iterativeBMA, not meant to be called directly.
● Data Source:
BioConductor
● Keywords: internal
● Alias: bma.punct.string, convertModelName, convertSingleName, imageplot.bma.mod, iterateBMAglm, iterateBMAinit, iterativeBMA-internal, maxExpValueKY, minExpValueKY, zero.threshold
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The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402).
● Data Source:
BioConductor
● Keywords: classif, multivariate
● Alias: iterativeBMA, iterativeBMA-package
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