fda.usc-package: Functional Data Analysis and Utilities for Statistical Computing (fda.usc)
Description
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.
Sections of fda.usc-package:
ll{
A.- Functional Data Representation
B.- Functional Outlier Detection
C.- Functional Regression with Scalar Response
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.
ll{
fdata
plot.fdata
fdata.deriv
CV.S
GCV.S
min.np
min.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:
ll{
depth.FM
depth.mode
depth.RP
depth.RT
depth.RPD
Descriptive
}
B.2-Functional Outliers detection methods:
ll{
outliers.depth.trim
outliers.depth.pond
outliers.thres.lrt
outliers.lrt
}
C.- Functional Regression with Scalar Response
C.1. Functional explanatory covariate
The functions included in this section allow the estimation of different functional regression models with a scalar response and a single functional explicative covariate.
ll{
fregre.pc
fregre.pc.cv
fregre.basis
fregre.basis.cv
fregre.np
fregre.np.cv
fregre.pls
fregre.pls.cv
summary.fregre.fd
predict.fregre.fd
}
C.2. Functional and non functional explanatory covariates.
The functions in this section extends those regression models in previous section in several ways.
Semifunctional partial linear regression fregre.plm is an extension of functional nonparameric regression fregre.np allowing include non-functional variables.
Functional generalized additive model using backfitting algorithm fregre.gkam is an extension of functional nonparameric regression fregre.np allowing include more than one functional variables.
Functional linear regression fregre.lm, functional generalized linear regression fregre.glm and
functional generalized spectral additive model fregre.gsam are an extensions of fregre.basis and fregre.pc allowing include more than one functional variable and other non-functional variables, as lm or glm functions.
ll{
fregre.plm
fregre.gkam
fregre.lm
fregre.glm
fregre.gsam
predict.fregre.fd
predict.fregre.gkam
predict.fregre.plm
predict.fregre.glm
predict.fregre.gsam
}
C.3. Test for the functional linear model (FLM).
ll{
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
}
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.
ll{
classif.knn
classif.kernel
classif.glm
classif.gsam
classif.gkam
summary.classif
predict.classif
}
E.- Functional Non-Supervised Classification
This section allows the estimation of the groups in a functional data set fdata class by kmeans method.
ll{
kmeans.fd
}
F.- Functional ANOVA
ll{
anova.RPm
anova.hetero
}
G.- Utilities and auxiliary functions:
ll{
fdata.bootstrap
fdata2fd
fdata2pc
fdata2pls
summary.fdata.comp
cond.F
cond.quantile
cond.mode
FDR
Kernel
Kernel.asymmetric
Kernel.integrate
metric.lp
metric.dist
semimetric.NPFDA
semimetric.basis
}Details
ll{
Package: fda.usc
Type: Package
Version: 1.0.0
Date: 2012-10-18
License: GPL-2
LazyLoad: yes
Url: http://eio.usc.es/pub/MAESFE/, http://eio.usc.es/pub/gi1914/
}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/