For a private package ("soon" to be on CRAN),
I started looking at and thinking about the function confusion() from the
mda package and wondered if it wouldn't make sense to provide the generic
function in base R and also think about making the default for the `true'
argument equal to fitted(object).
One could write methods for confusion() for almost all classifiers
which would be a nice universal approach.
Also, the value (a confusion matrix contingency table with some "useful"
attributes) could be made a bit more explicit.
Good or not so good idea? alternatives?
Martin Maechler <maechler@stat.math.ethz.ch> http://stat.ethz.ch/~maechler/
Seminar fuer Statistik, ETH-Zentrum LEO D10 Leonhardstr. 27
ETH (Federal Inst. Technology) 8092 Zurich SWITZERLAND
phone: x-41-1-632-3408 fax: ...-1228 <><
Confusion Matrices
Description:
Compute the confusion matrix between two factors, or for an fda or
mda object.
Usage:
confusion(object, true, ...)
confusion(object, data, ...)
Arguments:
object: the predicted factor, or an fda or mda model object.
true: the true factor.
data: a data frame (list) containing the test data.
...: further arguments to be passed to or from methods.
Details:
This is a generic function.
Value:
For the default method essentially `table(object, true)', but with
some useful attribute(s).
See Also:
`fda', `predict.fda'
Examples:
data(iris)
irisfit <- fda(Species ~ ., data = iris)
confusion(predict(irisfit, iris), iris$Species)
## Setosa Versicolor Virginica
## Setosa 50 0 0
## Versicolor 0 48 1
## Virginica 0 2 49
## attr(, "error"):
## [1] 0.02
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