fda.usc (version 0.9.4)

predict.classif: Predicts from a fitted classif object.

Description

Classifier of Functional Data by kernel method using Functional Data Object of class classif.

Usage

## S3 method for class 'classif':
predict(object, new.fdataobj,type="class",\dots)

Arguments

object

Object object estimated by: k nearest neighbors method classif.knn, kernel method classif.kernel.

new.fdataobj

New functional explanatory data of fdata class.

type

!=''class'', for each functional data shows the probability of each group membership.

...

Further arguments passed to or from other methods.

Value

• group.pred
  Vector of groups or classes predicted
• prob.group
  For each functional data shows the probability of each group membership.

Details

Returns the groups or classes predicted using a previously trained model.

References

Ferraty, F. and Vieu, P. (2006). Nonparametricc functional data analysis. Springer Series in Statistics, New York. Ferraty, F. and Vieu, P. (2006). NPFDA in practice. Free access on line at http://www.lsp.ups-tlse.fr/staph/npfda/ Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York. Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

See Also

See also classif.knn, classif.kernel and summary.classif .

Examples

data(phoneme)
mlearn<-phoneme[["learn"]]
glearn<-phoneme[["classlearn"]]
mtest<-phoneme[["test"]]
gtest<-phoneme[["classtest"]]

#	ESTIMATION knn
out1=classif.knn(glearn,mlearn,knn=c(3,5,7))
summary.classif(out1)
#	PREDICTION knn
pred1=predict.classif(out1,mtest)
table(pred1,gtest)
MisclasPredict <- sum(pred1 != gtest)/length(gtest)
MisclasPredict

#	ESTIMATION kernel
h=2^(0:5)
out2=classif.kernel(glearn,mlearn,h=h)
summary.classif(out2)
#	PREDICTION kernel
pred2=predict.classif(out2,mtest)
table(pred2,gtest)
MisclasPredict <- sum(pred2!= gtest)/length(gtest)
MisclasPredict