CrossValidate-class {CrossValidate}

Class "CrossValidate"

Description

A class that contains the results of internal cross-validation (by multiple splits into training and test sets) of an algorithm that builds a model to predict a binary outcome.

Objects from the Class

Objects should be created by calls to the constructor function, CrossValidate.

Slots

nIterations:

An integer; the number of times the data was split into training and test sets.

trainPercent:

A number between 0 and 1; the fraction of data used in each training set.

outcome:

A binary factor containing the true outcome for each sample.

trainOutcome:

A data frame containing the true outcomes for each member of the training set. The value 'NA' is used for samples that were reserved for testing. Each column is a different split into training and test sets.

trainPredict:

A data frame containing the predicted outcome from the model for each member of the training set. The value 'NA' is used for samples that were reserved for testing. Each column is a different split into training and test sets.

validOutcome:

A data frame containing the true outcomes for each member of the test set. The value 'NA' is used for samples that were used for training. Each column is a different split into training and test sets.

validPredict:

A data frame containing the predicted outcome from the model for each member of the test set. The value 'NA' is used for samples that were used for training. Each column is a different split into training and test sets.

extras:

A list, whose length equals the number of plsits into trainin and test sets. Each entry contains any "extra" information collected during the fitting of the model; the kinds of items stored here depend on the actual classification algorithm used.

Methods

summary

signature(object = "CrossValidate"): Produces a summary of the performance of the algorithm on both the trinaing sets and the test sets, in terms of specificity, sensitivity, and positive or negative predictive value. Specifically, this method returns an object of the CrossValSummary-class.

Author(s)

Kevin R. Coombes <[e-mail]>

References

Braga-Neto U, Dougherty ER.
Is cross-validation valid for small-sample microarray classification?
Bioinformatics, 2004; 20:374–380.

Jiang W, Varma S, Simon R.
Calculating confidence intervals for prediction error in microarray classification using resampling.
Stat Appl Genet Mol Biol. 2008; 7:Article8.

Fu LM, Youn ES.
Improving reliability of gene selection from microarray functional genomics data.
IEEE Trans Inf Technol Biomed. 2003; 7:191–6.

Man MZ, Dyson G, Johnson K, Liao B.
Evaluating methods for classifying expression data.
J Biopharm Stat. 2004; 14:1065–84.

Fu WJ, Carroll RJ, Wang S.
Estimating misclassification error with small samples via bootstrap cross-validation.
Bioinformatics, 2005; 21:1979–86.

Ancona N, Maglietta R, Piepoli A, D'Addabbo A, Cotugno R, Savino M, Liuni S, Carella M, Pesole G, Perri F.
On the statistical assessment of classifiers using DNA microarray data.
BMC Bioinformatics, 2006; 7:387.

Lecocke M, Hess K.
An empirical study of univariate and genetic algorithm-based feature selection in binary classification with microarray data.
Cancer Inform, 2007; 2:313–27.

Lee S.
Mistakes in validating the accuracy of a prediction classifier in high-dimensional but small-sample microarray data.
Stat Methods Med Res, 2008; 17:635–42.

See Also

See CrossValidate for the constructor function.

Examples

showClass("CrossValidate")