fda.usc v2.0.1

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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.

Readme

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

CRAN\_Status\_Badge Licence

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

Type Package
Date 2019-12-13
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 2019-12-16 13:48:08 UTC; moviedo
Date/Publication 2019-12-16 16:00:08 UTC

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