fda.usc-package

Functional Data Analysis and Utilities for Statistical Computing (fda.usc)

This package carries out exploratory and descriptive analysis of functional data exploring its most important features: such as depth measurements or functional outliers detection, among others. It also helps to explain and model the relationship between a dependent variable and independent (regression models) and make predictions. Methods for supervised or unsupervised classification of a set of functional data regarding a feature of the data are also included. Finally, it can perform analysis of variance model (ANOVA) for functional data.

Keywords
package
Details

Sections of fda.usc-package:

A.- Functional Data Representation
B.- Functional Outlier Detection
C.- Functional Regression Model
D.- Functional Supervised Classification
E.- Functional Non-Supervised Classification
F.- Functional ANOVA
G.- Auxiliary functions

A.- Functional Data Representation The functions included in this section allow to define, transform, manipulate and represent a functional dataset in many ways including derivatives, non-parametric kernel methods or basis representation.

fdata
plot.fdata
fdata.deriv
CV.S
GCV.S
optim.np
optim.basis
S.NW
S.LLR
S.basis
Var.e
Var.y

B.- Functional Depth and Functional Outlier Detection

The functional data depth calculated by the different depth functions implemented that could be use as a measure of centrality or outlyingness. B.1-Depth methods Depth:

depth.FM
depth.mode
depth.RP
depth.RT
depth.RPD
Descriptive

B.2-Functional Outliers detection methods:

outliers.depth.trim
outliers.depth.pond
outliers.thres.lrt
outliers.lrt

C.- Functional Regression Models

C.1. Functional explanatory covariate and scalar response The functions included in this section allow the estimation of different functional regression models with a scalar response and a single functional explicative covariate.

fregre.pc
fregre.pc.cv
fregre.pls
fregre.pls.cv
fregre.basis
fregre.basis.cv
fregre.np
fregre.np.cv

C.2. Test for the functional linear model (FLM) with scalar response.

flm.Ftest, F-test for the FLM with scalar response
flm.test, Goodness-of-fit test for the FLM with scalar response
PCvM.statistic, PCvM statistic for the FLM with scalar response

C.3. Functional and non functional explanatory covariates. The functions in this section extends those regression models in previous section in several ways.

fregre.plm: Semifunctional Partial Linear Regression (an extension of lm model)
fregre.lm: Functional Linear Regression (an extension of lm model)
fregre.glm: Functional Generalized Linear Regression (an extension of glm model)
fregre.gsam: Functional Generalized Spectral Additive Regression (an extension of gam model)
fregre.gkam: Functional Generalized Kernel Additive Regression (an extension of fregre.np model)

C.4. Functional response model (fregre.basis.fr) allows the estimation of functional regression models with a functional response and a single functional explicative covariate.

C.5. fregre.gls fits functional linear model using generalized least squares. fregre.igls fits iteratively a functional linear model using generalized least squares.

C.6. fregre.gsam.vs, Variable Selection using Functional Additive Models

D.- Functional Supervised Classification This section allows the estimation of the groups in a training set of functional data fdata class by different nonparametric methods of supervised classification. Once these classifiers have been trained, they can be used to predict on new functional data. Package allows the estimation of the groups in a training set of functional data by different methods of supervised classification.

D.1 Univariate predictor (x,y arguments, fdata class)

classif.knn
classif.kernel

D.2 Multiple predictors (formula,data arguments, ldata class)

classif.glm
classif.gsam
classif.gkam

D.3 Depth classifiers (fdata or ldata class)

classif.DD
classif.depth

D.4 Functional Classification usign k-fold CV

classif.kfold

E.- Functional Non-Supervised Classification This section allows the estimation of the groups in a functional data set fdata class by kmeans method.

kmeans.fd

F.- Functional ANOVA

fanova.onefactor
fanova.RPm
fanova.hetero

G.- Utilities and auxiliary functions:

fdata.bootstrap
fdata2fd
fdata2pc
fdata2pls
summary.fdata.comp
cond.F
cond.quantile
cond.mode
FDR
Kernel
Kernel.asymmetric
Kernel.integrate
metric.lp
metric.kl
metric.DTW
metric.hausdorff
metric.dist
semimetric.NPFDA
semimetric.basis

Package: fda.usc
Type: Package
Version: 2.0.1
Date: 2019-12-12
License: GPL-2
LazyLoad: yes

References

Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/

Aliases
  • fda.usc-package
  • fda.usc
Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

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