classif.np: Kernel Classifier from Functional Data

In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

Kernel Classifier from Functional Data

Description

Fits Nonparametric Supervised Classification for Functional Data.

Usage

classif.np(
  group,
  fdataobj,
  h = NULL,
  Ker = AKer.norm,
  metric,
  weights = "equal",
  type.S = S.NW,
  par.S = list(),
  ...
)

classif.knn(
  group,
  fdataobj,
  knn = NULL,
  metric,
  weights = "equal",
  par.S = list(),
  ...
)

classif.kernel(
  group,
  fdataobj,
  h = NULL,
  Ker = AKer.norm,
  metric,
  weights = "equal",
  par.S = list(),
  ...
)

Arguments

group	Factor of length n
fdataobj	fdata class object.
h	Vector of smoothing parameter or bandwidth.
Ker	Type of kernel used.
metric	Metric function, by default metric.lp.
weights	weights.
type.S	Type of smothing matrix S. By default S is calculated by Nadaraya-Watson kernel estimator (S.NW).
par.S	List of parameters for type.S: w, the weights.
...	Arguments to be passed for metric.lp o other metric function and Kernel function.
knn	Vector of number of nearest neighbors considered.

Details

Make the group classification of a training dataset using kernel or KNN estimation: Kernel.

Different types of metric funtions can be used.

Value

• fdataobj: fdata class object.

• group: Factor of length n.

• group.est: Estimated vector groups.

• 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.

• h.opt: Optimal smoothing parameter or bandwidht estimated.

• D: Matrix of distances of the optimal quantile distance hh.opt.

• prob.classification: Probability of correct classification by group.

• misclassification: Vector of probability of misclassification by number of neighbors knn.

• h: Vector of smoothing parameter or bandwidht.

• C: A call of function classif.kernel.

Note

If fdataobj is a data.frame the function considers the case of multivariate covariates.
metric.dist function is used to compute the distances between the rows of a data matrix (as dist function.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente [e-mail]

References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

Ferraty, F. and Vieu, P. (2006). NPFDA in practice. Free access on line at https://www.math.univ-toulouse.fr/~ferraty/SOFTWARES/NPFDA/

See Also

See Also as predict.classif

Examples

## Not run: 
data(phoneme)
mlearn <- phoneme[["learn"]]
glearn <- phoneme[["classlearn"]]
h <- 9:19
out <- classif.np(glearn,mlearn,h=h)
summary(out)
head(round(out$prob.group,4))

## End(Not run)

fda.usc documentation built on April 4, 2025, 4:35 a.m.