effectiveness_crm {airt}

R Documentation

Computes the actual and predicted effectiveness of the collection of algorithms.

Description

This function computes the actual and predicted effectiveness of the collection of algorithms for different tolerance values.

Usage

effectiveness_crm(model)

## S3 method for class 'effectivenesscrm'
autoplot(object, plottype = 1, ...)

Arguments

model	The output of the function cirtmodel.
object	For autoplot: The output of the function effectiveness_crm
plottype	For autoplot: If plottype = 1, then actual effectiveness is plotted, if plottype = 2, then predicted effectiveness is plotted. If plottype = 3, area under the actual effectiveness curve (AUAEC) is plotted against area under the predicted effectiveness curve (AUPEC).
...	Other arguments currently ignored.

Value

A list with the following components:

effectivenessAUC	The area under the actual and predicted effectiveness curves.
actcurves	The x,y coodinates for the actual effectiveness curves for each algorithm.

#'

prdcurves

The x,y coodinates for the predicted effectiveness curves for each algorithm.

Examples

set.seed(1)
x1 <- runif(200)
x2 <- 2*x1 + rnorm(200, mean=0, sd=0.1)
x3 <- 1 - x1 + rnorm(200, mean=0, sd=0.1)
X <- cbind.data.frame(x1, x2, x3)
mod <- cirtmodel(X)
out <- effectiveness_crm(mod)
out
# For the actual effectiveness plot
autoplot(out, plottype = 1)
# For the predicted effectivness plot
autoplot(out, plottype = 2)
# For actual and predicted effectiveness plot
autoplot(out, plottype = 3)

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