fda.usc v2.0.2

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Functional Data Analysis and Utilities for Statistical Computing

Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

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

Package overview

fda.usc 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. It can perform functional ANOVA, hypothesis testing, functional response models and many others.

Installation

You can install the current fda.usc version from CRAN with:

install.packages("fda.usc")


or the latest patched version from Github with:

library(devtools)
devtools::install_github("moviedo5/fda.usc")


Issues & Feature Requests

For issues, bugs, feature requests etc. please use the Github Issues. Input is always welcome.

Documentation

A hands on introduction to can be found in the reference vignette.

Details on specific functions are in the reference manual.

Cheatsheet fda.usc reference card.

References

Febrero-Bande, M. and 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/

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Functions in fda.usc

 Name Description CV.S The cross-validation (CV) score Kernel.integrate Integrate Smoothing Kernels. Kernel.asymmetric Asymmetric Smoothing Kernel Var.y Sampling Variance estimates S.np Smoothing matrix by nonparametric methods Descriptive Descriptive measures for functional data. dis.cos.cor Proximities between functional data fanova.RPm Functional ANOVA with Random Project. fdata2fd Converts fdata class object into fd class object LMDC.select Impact points selection of functional predictor and regression using local maxima distance correlation (LMDC) classif.np Kernel Classifier from Functional Data classif.kfold Functional Classification usign k-fold CV fdata.methods fdata S3 Group Generic Functions accuracy Performance measures for regression and classification models GCV.S The generalized correlated cross-validation (GCCV) score Kernel Symmetric Smoothing Kernels. classif.DD DD-Classifier Based on DD-plot MCO Mithochondiral calcium overload (MCO) data set FDR False Discorvery Rate (FDR) PCvM.statistic PCvM statistic for the Functional Linear Model with scalar response classif.depth Classifier from Functional Data fregre.glm Fitting Functional Generalized Linear Models S.basis Smoothing matrix with roughness penalties by basis representation. fregre.gkam Fitting Functional Generalized Kernel Additive Models. classif.ML Functional classification using ML algotithms cond.F Conditional Distribution Function classif.gkam Classification Fitting Functional Generalized Kernel Additive Models cond.quantile Conditional quantile aemet aemet data cond.mode Conditional mode create.fdata.basis Create Basis Set for Functional Data of fdata class dcor.xy Distance Correlation Statistic and t-Test fda.usc-package Functional Data Analysis and Utilities for Statistical Computing (fda.usc) depth.fdata Computation of depth measures for functional data dev.S The deviance score depth.mdata Provides the depth measure for multivariate data fregre.pls Functional Penalized PLS regression with scalar response dfv.test Delsol, Ferraty and Vieu test for no functional-scalar interaction depth.mfdata Provides the depth measure for a list of p--functional data objects fdata2pls Partial least squares components for functional data. GCCV.S The generalized correlated cross-validation (GCCV) score. fdata2pc Principal components for functional data fda.usc.internal fda.usc internal functions fregre.lm Fitting Functional Linear Models fregre.basis Functional Regression with scalar response using basis representation. fregre.basis.fr Functional Regression with functional response using basis representation. fregre.bootstrap Bootstrap regression Outliers.fdata outliers for functional dataset fregre.basis.cv Cross-validation Functional Regression with scalar response using basis representation. fregre.np Functional regression with scalar response using non-parametric kernel estimation metric.dist Distance Matrix Computation h.default Calculation of the smoothing parameter (h) for a functional data phoneme phoneme data influence.fregre.fd Functional influence measures fdata.cen Functional data centred (subtract the mean of each discretization point) fregre.pls.cv Functional penalized PLS regression with scalar response using selection of number of PLS components P.penalty Penalty matrix for higher order differences plot.fdata Plot functional data: fdata class object metric.hausdorff Compute the Hausdorff distances between two curves. fregre.pc.cv Functional penalized PC regression with scalar response using selection of number of PC components ldata ldata class definition and utilities fregre.plm Semi-functional partially linear model with scalar response. fregre.gls Fit Functional Linear Model Using Generalized Least Squares fdata.deriv Computes the derivative of functional data object. fregre.gsam Fitting Functional Generalized Spectral Additive Models fregre.igls Fit of Functional Generalized Least Squares Model Iteratively fregre.gsam.vs Variable Selection using Functional Additive Models rdir.pc Data-driven sampling of random directions guided by sample of functional data poblenou poblenou data metric.lp Approximates Lp-metric distances for functional data. predict.classif.DD Predicts from a fitted classif.DD object. metric.DTW DTW: Dynamic time warping predict.fregre.gkam Predict method for functional linear model na.omit.fdata A wrapper for the na.omit and na.fail function for fdata object classif.glm Classification Fitting Functional Generalized Linear Models subset.fdata Subsetting r.ou Ornstein-Uhlenbeck process summary.fdata.comp Correlation for functional data by Principal Component Analysis metric.kl Kullback--Leibler distance rp.flm.test Goodness-of fit test for the functional linear model using random projections classif.gsam Classification Fitting Functional Generalized Additive Models rp.flm.statistic Statistics for testing the functional linear model using random projections metric.ldata Distance Matrix Computation for ldata and mfdata class object rcombfdata Utils for generate functional data summary.classif Summarizes information from kernel classification methods. summary.fregre.fd Summarizes information from fregre.fd objects. fanova.hetero ANOVA for heteroscedastic data fanova.onefactor One--way anova model for functional data int.simpson Simpson integration fdata.bootstrap Bootstrap samples of a functional statistic ops.fda.usc ops.fda.usc Options Settings flm.test Goodness-of-fit test for the Functional Linear Model with scalar response kmeans.center.ini K-Means Clustering for functional data predict.fregre.fr Predict method for functional response model norm.fdata Approximates Lp-norm for functional data. flm.Ftest F-test for the Functional Linear Model with scalar response summary.fregre.gkam Summarizes information from fregre.gkam objects. tecator tecator data fdata Converts raw data or other functional data classes into fdata class. semimetric.basis Proximities between functional data fregre.np.cv Cross-validation functional regression with scalar response using kernel estimation. semimetric.NPFDA Proximities between functional data (semi-metrics) predict.fregre.gls Predictions from a functional gls object fregre.pc Functional Regression with scalar response using Principal Components Analysis inprod.fdata Inner products of Functional Data Objects o class (fdata) influence_quan Quantile for influence measures predict.fregre.fd Predict method for functional linear model (fregre.fd class) optim.basis Select the number of basis using GCV method. rwild Wild bootstrap residuals optim.np Smoothing of functional data using nonparametric kernel estimation weights4class Weighting tools predict.classif Predicts from a fitted classif object. rproc2fdata Simulate several random processes. 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