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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. https://dx.doi.org/10.18637/jss.v051.i04

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Install

install.packages('fda.usc')

Monthly Downloads

2,046

Version

2.1.0

License

GPL-2

Issues

Pull Requests

Stars

Forks

Last Published

October 17th, 2022

Functions in fda.usc (2.1.0)

FDR

False Discorvery Rate (FDR)
GCV.S

The generalized correlated cross-validation (GCCV) score
Descriptive

Descriptive measures for functional data.
Kernel

Symmetric Smoothing Kernels.
Kernel.asymmetric

Asymmetric Smoothing Kernel
MCO

Mithochondiral calcium overload (MCO) data set
Kernel.integrate

Integrate Smoothing Kernels.
LMDC.select

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

Performance measures for regression and classification models
Var.y

Sampling Variance estimates
CV.S

The cross-validation (CV) score
GCCV.S

The generalized correlated cross-validation (GCCV) score.
aemet

aemet data
P.penalty

Penalty matrix for higher order differences
classif.glm

Classification Fitting Functional Generalized Linear Models
Outliers.fdata

outliers for functional dataset
classif.gkam

Classification Fitting Functional Generalized Kernel Additive Models
S.np

Smoothing matrix by nonparametric methods
S.basis

Smoothing matrix with roughness penalties by basis representation.
classif.depth

Classifier from Functional Data
classif.gsam

Classification Fitting Functional Generalized Additive Models
classif.ML

Functional classification using ML algotithms
classif.kfold

Functional Classification usign k-fold CV
classif.np

Kernel Classifier from Functional Data
classif.DD

DD-Classifier Based on DD-plot
classif.gsam.vs

Variable Selection in Functional Data Classification
create.fdata.basis

Create Basis Set for Functional Data of fdata class
depth.mdata

Provides the depth measure for multivariate data
cond.mode

Conditional mode
fEqMoments.test

Tests for checking the equality of means and/or covariance between two populations under gaussianity.
depth.fdata

Computation of depth measures for functional data
PCvM.statistic

PCvM statistic for the Functional Linear Model with scalar response
fEqDistrib.test

Tests for checking the equality of distributions between two functional populations.
depth.mfdata

Provides the depth measure for a list of p--functional data objects
dis.cos.cor

Proximities between functional data
dfv.test

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

Conditional quantile
cond.F

Conditional Distribution Function
dcor.xy

Distance Correlation Statistic and t-Test
fanova.onefactor

One--way anova model for functional data
fdata.cen

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

Bootstrap regression
fregre.basis.cv

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

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

Bootstrap samples of a functional statistic
dev.S

The deviance score
flm.test

Goodness-of-fit test for the Functional Linear Model with scalar response
fanova.hetero

ANOVA for heteroscedastic data
fregre.basis.fr

Functional Regression with functional response using basis representation.
fdata2pc

Principal components for functional data
fdata2fd

Converts fdata class object into fd class object
fda.usc-package

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

fdata S3 Group Generic Functions
fanova.RPm

Functional ANOVA with Random Project.
fregre.np.cv

Cross-validation functional regression with scalar response using kernel estimation.
fregre.np

Functional regression with scalar response using non-parametric kernel estimation
fregre.gkam

Fitting Functional Generalized Kernel Additive Models.
fdata.deriv

Computes the derivative of functional data object.
fregre.basis

Functional Regression with scalar response using basis representation.
fdata2pls

Partial least squares components for functional data.
flm.Ftest

F-test for the Functional Linear Model with scalar response
fregre.glm.vs

Variable Selection using Functional Linear Models
fregre.lm

Fitting Functional Linear Models
influence.fregre.fd

Functional influence measures
inprod.fdata

Inner products of Functional Data Objects o class (fdata)
fregre.gsam

Fitting Functional Generalized Spectral Additive Models
fdata2basis

Compute fucntional coefficients from functional data represented in a base of functions
fda.usc.internal

fda.usc internal functions
mfdata

mfdata class definition and utilities
ldata

ldata class definition and utilities
h.default

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

Simpson integration
fregre.glm

Fitting Functional Generalized Linear Models
fregre.igls

Fit of Functional Generalized Least Squares Model Iteratively
fregre.gls

Fit Functional Linear Model Using Generalized Least Squares
ops.fda.usc

ops.fda.usc Options Settings
metric.hausdorff

Compute the Hausdorff distances between two curves.
fregre.gsam.vs

Variable Selection using Functional Additive Models
predict.fregre.fd

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

Functional Regression with scalar response using Principal Components Analysis
influence_quan

Quantile for influence measures
fregre.pls.cv

Functional penalized PLS regression with scalar response using selection of number of PLS components
fregre.pc.cv

Functional penalized PC regression with scalar response using selection of number of PC components
rp.flm.statistic

Statistics for testing the functional linear model using random projections
rdir.pc

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

Semi-functional partially linear model with scalar response.
metric.lp

Approximates Lp-metric distances for functional data.
phoneme

phoneme data
metric.kl

Kullback--Leibler distance
plot.fdata

Plot functional data: fdata class object
rp.flm.test

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

Ornstein-Uhlenbeck process
na.omit.fdata

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

Predicts from a fitted classif.DD object.
kmeans.center.ini

K-Means Clustering for functional data
predict.fregre.fr

Predict method for functional response model
fregre.pls

Functional Penalized PLS regression with scalar response
poblenou

poblenou data
weights4class

Weighting tools
subset.fdata

Subsetting
tecator

tecator data
predict.fregre.gkam

Predict method for functional linear model
semimetric.NPFDA

Proximities between functional data (semi-metrics)
optim.np

Smoothing of functional data using nonparametric kernel estimation
summary.fregre.fd

Summarizes information from fregre.fd objects.
predict.classif

Predicts from a fitted classif object.
predict.fregre.gls

Predictions from a functional gls object
semimetric.basis

Proximities between functional data
summary.fdata.comp

Correlation for functional data by Principal Component Analysis
optim.basis

Select the number of basis using GCV method.
norm.fdata

Approximates Lp-norm for functional data.
rproc2fdata

Simulate several random processes.
metric.ldata

Distance Matrix Computation for ldata and mfdata class object
summary.classif

Summarizes information from kernel classification methods.
metric.dist

Distance Matrix Computation
rwild

Wild bootstrap residuals
rcombfdata

Utils for generate functional data
summary.fregre.gkam

Summarizes information from fregre.gkam objects.
metric.DTW

DTW: Dynamic time warping