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, ...)

formula

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`

.

data

List that containing the variables in the model.

basis.x

List of basis for functional explanatory data estimation.

weights

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.

size

number of units in the hidden layer. Can be zero if there are skip-layer units.

…

Further arguments passed to or from other methods.

type

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.

laplace

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 { # }