fda.usc (version 0.9.4)

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.RPD Descriptive } B.2-Functional Outliers detection methods: ll{ outliers.depth.trim outliers.depth.pond outliers.thres.lrt outliers.lrt quantile.outliers.trim quantile.outliers.pond } 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 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 linear regression fregre.lm and functional generalized linear regression fregre.glm 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.lm fregre.glm fregre.plm predict.fregre.fd predict.fregre.glm predict.fregre.plm } 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 nonparametric methods of supervised classification. -Kernel classification methods: ll{ classif.knn.fd classif.kernel.fd classif.kernel.fb summary.classif.fd predict.classif.fd } 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 cond.F cond.quantile cond.mode FDR Kernel Kernel.asymmetric Kernel.integrate metric.lp pc.cor pc.fdata semimetric.NPFDA semimetric.basis }

Details

ll{ Package: fda.usc Type: Package Version: 0.9.4 Date: 2011-03-08 License: GPL-2 LazyLoad: yes }