| fda.usc-package {fda.usc} | R Documentation |
Functional Data Analysis and Utilities for Statistical Computing (fda.usc)
Description
This devel version 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.
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 |
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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 |
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classif.gsam |
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classif.gkam |
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D.3 Depth classifiers (fdata or ldata class)
classif.DD |
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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:
| Package: | fda.usc |
| Type: | Package |
| Version: | 2.0.3 |
| Date: | 2021-06-03 |
| License: | GPL-2 |
| LazyLoad: | yes |
| https://github.com/moviedo5/fda.usc/ |
Author(s)
Authors: Manuel Febrero Bande manuel.febrero@usc.es and Manuel Oviedo de la Fuente manuel.oviedo@udc.es
Contributors: Pedro Galeano, Alicia Nieto-Reyes, Eduardo Garcia-Portugues eduardo.garcia@usc.es and STAPH group https://www.math.univ-toulouse.fr/~ferraty/
Maintainer: Manuel Oviedo de la Fuente manuel.oviedo@udc.es
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. doi:10.18637/jss.v051.i04