fda.usc (version 0.9.4)

summary.classif.fd: Summarizes information from kernel classification methods.

Description

Summary function for classif.knn.fd, classif.kernel.fd or classif.kernel.fb.

Usage

## S3 method for class 'classif.fd':
summary(object,\dots)
## S3 method for class 'classif.fd':
print(x,digits = max(3, getOption("digits") - 3),...)

Arguments

object

Estimated by kernel classification.

x

Estimated by kernel classification.

digits

a non-null value for digits specifies the minimum number of significant digits to be printed in values. The default, NULL, uses getOption(digits).

...

Further arguments passed to or from other methods.

Value

• Shows: ll{ -Probability of correct classification by group prob.classification. -Confusion matrix between the theoretical groups and estimated groups. -Highest probability of correct classification max.prob. } If the object is returned from the function: classif.knn.fd ll{ -Vector of probability of correct classification by number of neighbors knn. -Optimal number of neighbors: knn.opt. } If the object is returned from the function: classif.kernel.fd ll{ -Vector of probability of correct classification by banwidth h. -Functional measure of closeness (optimal distance, h.opt). } If the object is returned from the function: classif.kernel.fb ll{ -Matrix of probability of correct classification by number of basis num.basis and bandwidth h. -Functional measure of proximity(optimal distance, h.opt). -Optimal number of basis: basis.opt and optimal bandwidth: h.opt. }
• object
  Estimated by kernel classification.

Details

object from one of the following functions: ll{ classif.knn.fd classif.kernel.fd classif.kernel.fb }

See Also

See Also as: classif.knn.fd, classif.kernel.fd, classif.kernel.fb and summary.classif.fd

Examples

data(phoneme)
mlearn<-phoneme[["learn"]]
glearn<-phoneme[["classlearn"]]
out=classif.knn.fd(mlearn,glearn,knn=c(3,5,7))
summary.classif.fd(out)
out2=classif.kernel.fd(mlearn,glearn,h=2^(0:5))
summary.classif.fd(out2)
out3=classif.kernel.fb(mlearn,glearn,h=2^(0:5),
par.basis = list(nbasis=c(7,17,27,37)))
summary.classif.fd(out3)