fda.usc v2.0.2

Readme

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.
No Results!

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

Conditional quantile

aemet data

Conditional mode

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.

No Results!

Details

Type	Package
Date	2020-02-17
License	GPL-2
URL	https://github.com/moviedo5/fda.usc, http://www.jstatsoft.org/v51/i04/
BugReports	https://github.com/moviedo5/fda.usc
LazyLoad	yes
NeedsCompilation	yes
Repository	CRAN
Encoding	UTF-8
RoxygenNote	7.0.2
Packaged	2020-02-17 17:32:08 UTC; moviedo
Date/Publication	2020-02-17 19:00:34 UTC

imports	doParallel , foreach , graphics , grDevices , iterators , methods , nlme , parallel , stats , utils
depends	fda , MASS , mgcv , R (>= 2.10) , splines
suggests	rmarkdown
Contributors	Manuel Febrero Bande, Pedro Galeano, Alicia Nieto, Eduardo Garcia-Portugues