Model Predictions
Description
Perform prediction on a trained supervisedPRIM model. Output
to either predicted class or positive class probability is supported.
Usage
## S3 method for class 'supervisedPRIM'
predict(object, newdata, classProb = FALSE, ...)
Arguments
object
A trained model of class supervisedPRIM returned by supervisedPRIM
newdata
The new data on which to create predictions
classProb
Should the function return the estimated class
...
additional arguments (ignored) probabilities instead of the predicted class?
Author(s)
David Shaub
Examples
# Train a model to determine if a flower is setosa
data(iris)
yData <- factor(ifelse(iris$Species == "setosa", "setosa", "other"), levels = c("setosa", "other"))
xData <- iris
xData$Species <- NULL
primModel <- supervisedPRIM(x = xData, y = yData)
# Predict on the original dataset
predictions <- predict(primModel, newdata = xData)
Fit PRIM model to a labeled dataset
Description
perform supervised classification using Patient Rule Induction Method (PRIM)
Usage
supervisedPRIM(x, y, peel.alpha = 0.05, paste.alpha = 0.01,
mass.min = 0.05, threshold.type = 1, ...)
Arguments
x
matrix of data values
y
binary vector of 0/1 response values
peel.alpha
peeling quantile tuning parameter
paste.alpha
pasting quantile tuning parameter
mass.min
minimum mass tuning parameter
threshold.type
threshold direction indicator: 1 = ">= threshold", -1 = "<= threshold"
...
additional arguments to pass to prim.box
Details
Fit
Value
an object of class supervisedPRIM. See additional details
in prim.box
Author(s)
David Shaub
Examples
# Train a model to determine if a flower is setosa
data(iris)
yData <- factor(ifelse(iris$Species == "setosa", "setosa", "other"), levels = c("setosa", "other"))
xData <- iris
xData$Species <- NULL
primModel <- supervisedPRIM(x = xData, y = yData)