| CrossValidate {CrossValidate} | R Documentation |
Creating CrossValidate objects
Description
Given a model classifier and a data set, this function performs cross-validation by repeatedly splitting the data into training and testing subsets in order to estimate the performance of this kind of classifer on new data.
Usage
CrossValidate(model, data, status, frac, nLoop, prune=keepAll, verbose=TRUE)
Arguments
model |
An element of the |
data |
A matrix containing the data to be used for cross-validation. As with most gene expression data, columns are the independent samples or observations and rows are the measured features. |
status |
A binary-valued factor with the classes to be predicted. |
frac |
A number between 0 and 1; the fraction of the data that should be used in each iteration to train the model. |
nLoop |
An integer; the number of times to split the data into training and test sets. |
prune |
A function that takes two inoputs, a data matrix and a factor with two levels, and rteturns a logical vector whose length equals the number of rows in the data matrix. |
verbose |
A logical value; should the cross-validation routine report interim progress. |
Details
The CrossValidate package provides generic tools for performing
cross-validation on classificaiton methods in the context of
high-throughput data sets such as those produced by gene expression
microarrays. In order to use a classifier with this implementaiton of
cross-validation, you must first prepare a pair of functions (one for
learning models from training data, and one for making predictions on
test data). These functions, along with any required meta-parameters,
are used to create an object of the Modeler-class. That
object is then passed to the CrossValidate function along
with the full training data set. The full data set is then repeatedly
split into its own training and test sets; you can specify the
fraction to be used for training and the number of iterations. The
result is a detailed look at the accuracy, sensitivity, specificity,
and positive and negative predictive value of the model, as estimated
by cross-validation.
Value
An object of the CrossValidate-class.
Author(s)
Kevin R. Coombes krcoombes@mdanderson.org
References
See the manual page for the CrossValidate-class for a list
of related references.
See Also
See CrossValidate-class for a description of the slots in
the object created by this function.
Examples
dataset <- matrix(rnorm(50*100), nrow=50)
pseudoclass <- factor(rep(c("A", "B"), each=50))
model <- modelerCCP # obviously, other models can be used
numTimes <- 10 # and more is probably better
cv <- CrossValidate(model, dataset, pseudoclass, 0.5, numTimes)
summary(cv)