01-const: Fit models and make predictions with a constant classifier

In Condens8R: Classes and Methods for Learning Complex Decision Tree Models

Fit models and make predictions with a constant classifier

Description

These functions are used to apply the generic train-and-test mechanism to a classifier that simply returns the same constant value for all inputs.

Usage

learnConstant(data, status, params, predfun)
predictConstant(newdata, details, status, ...)
makeLeaf(value, status = factor(c("L", "R")))

Arguments

data	The data matrix, with rows as features ("genes") and columns as the samples to be classified.
status	A factor, with two levels, classifying the samples. The length must equal the number of data columns.
params	A list of additional parameters used by the classifier; see Details.
predfun	The function used to make predictions on new data, using the trained classifier. Should always be set to predictConstant.
newdata	Another data matrix, with the same number of rows as data.
details	A list of additional parameters describing details about the particular classifier; see Details.
...	Optional extra parameters required by the generic "predict" method.
value	The constant value to be returned by the predictor. This should be one of the levels of the status vairable.

Details

The input arguments to both learnConstant and predictConstant are dictated by the requirements of the general train-and-test mechanism provided by the Modeler-class.

The "Constant" classifier is designed for use in the decision trees that are part of this package. They will be used as the "predictors" in leaf nodes, which always make the same prediction on any samples that make their way down to that node.

We provied the makeLeaf fnction as a convenience to create constant classifiers, by specifying their preferred value. (This value could also be provided as an extra/optional pafameter to the Modeler constructor.) For must uses, it is easier to simply use makeLeaf and ignore the learnConstant and predictConstant. The result of fitting the model using learnConstant or makeLeaf is a member of the FittedModel-class.

Value

The learnConstant and the makeLeaf functions return an object of the FittedModel-class, representing a Constant classifier that has been fitted on a training data set.

The predictConstant function returns a factor containing the predictions of the model when applied to the new data set, of length equal tothe number of columns (samples).

Author(s)

Kevin R. Coombes <[e-mail]>

See Also

See Modeler-class and Modeler for details about how to train and test models. See FittedModel-class and FittedModel for details about the structure of the object returned by learnConstant.

Examples

# simulate some data
data <- matrix(rnorm(100*20), ncol=20)
status <- factor(rep(c("A", "B"), each=10))

# create a constnt modeler.
fm <- makeLeaf("A", factor(c("A", "B")))

# Make predictions on some new simulated data
predictConstant(data, fm@details, status)

Condens8R documentation built on May 28, 2025, 3 a.m.