Computes functional classification using functional (and non functional) explanatory variables by rpart, nnet, svm or random forest model
classif.nnet(formula, data, basis.x = NULL, weights = "equal", size, ...)classif.rpart(
formula,
data,
basis.x = NULL,
weights = "equal",
type = "1vsall",
...
)
classif.svm(
formula,
data,
basis.x = NULL,
weights = "equal",
type = "1vsall",
...
)
classif.ksvm(formula, data, basis.x = NULL, weights = "equal", ...)
classif.randomForest(
formula,
data,
basis.x = NULL,
weights = "equal",
type = "1vsall",
...
)
classif.lda(
formula,
data,
basis.x = NULL,
weights = "equal",
type = "1vsall",
...
)
classif.qda(
formula,
data,
basis.x = NULL,
weights = "equal",
type = "1vsall",
...
)
classif.naiveBayes(formula, data, basis.x = NULL, laplace = 0, ...)
an object of class formula (or one that can be coerced
to that class): a symbolic description of the model to be fitted. The
details of model specification are given under Details.
List that containing the variables in the model.
List of basis for functional explanatory data estimation.
Weights:
if character string ='equal' same weights for each observation (by default) and
='inverse' for inverse-probability of weighting.
if numeric vector of length n, Weight values of each observation.
number of units in the hidden layer. Can be zero if there are skip-layer units.
Further arguments passed to or from other methods.
If type is"1vsall" (by default)
a maximum probability scheme is applied: requires G binary classifiers.
If type is "majority" (only for multicalss classification G > 2)
a voting scheme is applied: requires G (G - 1) / 2 binary classifiers.
value used for Laplace smoothing (additive smoothing). Defaults to 0 (no Laplace smoothing).
Return classif object plus:
formula formula.
data List that containing the variables in the model.
group Factor of length n
group.est Estimated vector groups
prob.classification Probability of correct classification by group.
prob.group Matrix of predicted class probabilities. For each
functional point shows the probability of each possible group membership.
max.prob Highest probability of correct classification.
type Type of classification scheme: 1 vs all or majority voting.
fit list of binary classification fitted models.
The first item in the data list is called "df" and is a data
frame with the response and non functional explanatory variables, as
glm.
Functional covariates of class fdata or fd are introduced in
the following items in the data list. basis.x is a list of
basis for represent each functional covariate. The b object can be
created by the function: create.pc.basis, pca.fd
create.pc.basis, create.fdata.basis o
create.basis. basis.b is a list of basis for
represent each functional beta parameter. If basis.x is a list of
functional principal components basis (see create.pc.basis or
pca.fd) the argument basis.b is ignored.
Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.
McCullagh and Nelder (1989), Generalized Linear Models 2nd ed. Chapman and Hall.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S, New York: Springer. Regression for R. R News 1(2):20-25
See Also as: rpart. Alternative method:
classif.np, classif.glm,
classif.gsam and classif.gkam.
# NOT RUN {
data(phoneme)
mlearn<-phoneme[["learn"]]
glearn<-phoneme[["classlearn"]]
mtest<-phoneme[["test"]]
gtest<-phoneme[["classtest"]]
dataf<-data.frame(glearn)
dat=list("df"=dataf,"x"=mlearn)
a1<-classif.rpart(glearn~x,data=dat)
summary(a1)
newdat<-list("x"=mtest)
p1<-predict(a1,newdat,type="class")
table(gtest,p1)
sum(p1==gtest)/250
# }
# NOT RUN {
# }
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