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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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Version

Install

install.packages('fda.usc')

Monthly Downloads

2,504

Version

2.0.2

License

GPL-2

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/moviedo5/fda.usc

Homepage

http://www.jstatsoft.org/v51/i04/

Maintainer

Manuel Oviedo de la Fuente

Last Published

February 17th, 2020

Functions in fda.usc (2.0.2)

The cross-validation (CV) score

Kernel.integrate

Integrate Smoothing Kernels.

Kernel.asymmetric

Asymmetric Smoothing Kernel

Sampling Variance estimates

Smoothing matrix by nonparametric methods

Descriptive measures for functional data.

Proximities between functional data

Functional ANOVA with Random Project.

Converts fdata class object into fd class object

Impact points selection of functional predictor and regression using local maxima distance correlation (LMDC)

Kernel Classifier from Functional Data

Functional Classification usign k-fold CV

fdata S3 Group Generic Functions

Performance measures for regression and classification models

The generalized correlated cross-validation (GCCV) score

Symmetric Smoothing Kernels.

DD-Classifier Based on DD-plot

Mithochondiral calcium overload (MCO) data set

False Discorvery Rate (FDR)

PCvM statistic for the Functional Linear Model with scalar response

Classifier from Functional Data

Fitting Functional Generalized Linear Models

Smoothing matrix with roughness penalties by basis representation.

Fitting Functional Generalized Kernel Additive Models.

Functional classification using ML algotithms

Conditional Distribution Function

Classification Fitting Functional Generalized Kernel Additive Models

Conditional quantile

Conditional mode

create.fdata.basis

Create Basis Set for Functional Data of fdata class

Distance Correlation Statistic and t-Test

fda.usc-package

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

Computation of depth measures for functional data

The deviance score

Provides the depth measure for multivariate data

Functional Penalized PLS regression with scalar response

Delsol, Ferraty and Vieu test for no functional-scalar interaction

Provides the depth measure for a list of p--functional data objects

Partial least squares components for functional data.

The generalized correlated cross-validation (GCCV) score.

Principal components for functional data

fda.usc.internal

fda.usc internal functions

Fitting Functional Linear Models

Functional Regression with scalar response using basis representation.

fregre.basis.fr

Functional Regression with functional response using basis representation.

fregre.bootstrap

Bootstrap regression

outliers for functional dataset

fregre.basis.cv

Cross-validation Functional Regression with scalar response using basis representation.

Functional regression with scalar response using non-parametric kernel estimation

Distance Matrix Computation

Calculation of the smoothing parameter (h) for a functional data

influence.fregre.fd

Functional influence measures

Functional data centred (subtract the mean of each discretization point)

Functional penalized PLS regression with scalar response using selection of number of PLS components

Penalty matrix for higher order differences

Plot functional data: fdata class object

metric.hausdorff

Compute the Hausdorff distances between two curves.

Functional penalized PC regression with scalar response using selection of number of PC components

ldata class definition and utilities

Semi-functional partially linear model with scalar response.

Fit Functional Linear Model Using Generalized Least Squares

Computes the derivative of functional data object.

Fitting Functional Generalized Spectral Additive Models

Fit of Functional Generalized Least Squares Model Iteratively

Variable Selection using Functional Additive Models

Data-driven sampling of random directions guided by sample of functional data

Approximates Lp-metric distances for functional data.

predict.classif.DD

Predicts from a fitted classif.DD object.

DTW: Dynamic time warping

predict.fregre.gkam

Predict method for functional linear model

A wrapper for the na.omit and na.fail function for fdata object

Classification Fitting Functional Generalized Linear Models

Ornstein-Uhlenbeck process

summary.fdata.comp

Correlation for functional data by Principal Component Analysis

Kullback--Leibler distance

Goodness-of fit test for the functional linear model using random projections

Classification Fitting Functional Generalized Additive Models

rp.flm.statistic

Statistics for testing the functional linear model using random projections

Distance Matrix Computation for ldata and mfdata class object

Utils for generate functional data

summary.classif

Summarizes information from kernel classification methods.

summary.fregre.fd

Summarizes information from fregre.fd objects.

ANOVA for heteroscedastic data

fanova.onefactor

One--way anova model for functional data

Simpson integration

fdata.bootstrap

Bootstrap samples of a functional statistic

ops.fda.usc Options Settings

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

Approximates Lp-norm for functional data.

F-test for the Functional Linear Model with scalar response

summary.fregre.gkam

Summarizes information from fregre.gkam objects.

Converts raw data or other functional data classes into fdata class.

semimetric.basis

Proximities between functional data

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

Functional Regression with scalar response using Principal Components Analysis

Inner products of Functional Data Objects o class (fdata)

Quantile for influence measures

predict.fregre.fd

Predict method for functional linear model (fregre.fd class)

Select the number of basis using GCV method.

Wild bootstrap residuals

Smoothing of functional data using nonparametric kernel estimation

Weighting tools

predict.classif

Predicts from a fitted classif object.

Simulate several random processes.