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

[Up] [Top]

Documentation for package ‘fda.usc’ version 1.0.4

Help Pages

A C D F G H I K L M N O P Q R S T V misc

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

-- A --

Adot PCvM statistic for the Functional Linear Model with scalar response
aemet aemet data
AKer.cos Asymmetric Smoothing Kernel
AKer.epa Asymmetric Smoothing Kernel
AKer.norm Asymmetric Smoothing Kernel
AKer.quar Asymmetric Smoothing Kernel
AKer.tri Asymmetric Smoothing Kernel
AKer.unif Asymmetric Smoothing Kernel
anova.hetero ANOVA for heteroscedastic data
anova.RPm Functional ANOVA with Random Project.
anova.RPm.boot Functional ANOVA with Random Project.
argvals fda.usc internal functions

-- C --

c.fdata fda.usc internal functions
classif.depth Classifier from Functional Data
classif.gkam Classification Fitting Functional Generalized Kernel Additive Models
classif.glm Classification Fitting Functional Generalized Linear Models
classif.gsam Classification Fitting Functional Generalized Additive Models
classif.kernel Kernel Classifier from Functional Data
classif.knn Kernel Classifier from Functional Data
classif.np Kernel Classifier from Functional Data
cond.F Conditional Distribution Function
cond.mode Conditional mode
cond.quantile Conditional quantile
create.fdata.basis Create Basis Set for Functional Data of fdata class
create.pc.basis Create Basis Set for Functional Data of fdata class
create.pls.basis Create Basis Set for Functional Data of fdata class
create.raw.fdata Create Basis Set for Functional Data of fdata class
CV.S The cross-validation (CV) score

-- D --

Depth Provides the depth measure for functional data
depth.FM Provides the depth measure for functional data
depth.HD Provides the depth measure for multivariate data
depth.MhD Provides the depth measure for multivariate data
depth.mode Provides the depth measure for functional data
Depth.Multivariate Provides the depth measure for multivariate data
depth.PD Provides the depth measure for multivariate data
depth.RP Provides the depth measure for functional data
depth.RPD Provides the depth measure for functional data
depth.RT Provides the depth measure for functional data
depth.SD Provides the depth measure for multivariate data
Descriptive Descriptive measures for functional and multivariate data.
dev.S The deviance score .
dfv.statistic Delsol, Ferraty and Vieu test for no functional-scalar interaction
dfv.test Delsol, Ferraty and Vieu test for no functional-scalar interaction
dim.fdata fda.usc internal functions
dis.cos.cor Proximities between functional data

-- F --

fda.usc Functional Data Analysis and Utilities for Statistical Computing (fda.usc)
fdata Converts raw data or other functional data classes into fdata class.
fdata.bootstrap Bootstrap samples of a functional statistic
fdata.cen Functional data centred (subtract the mean of each discretization point)
fdata.deriv Computes the derivative of functional data object.
fdata2fd Converts fdata class object into fd class object
fdata2pc Principal components for functional data
fdata2pls Partial least squares components for functional data.
fdata2ppc Principal components for functional data
fdata2ppls Partial least squares components for functional data.
FDR False Discorvery Rate (FDR)
flm.Ftest F-test for the Functional Linear Model with scalar response
flm.test Goodness-of-fit test for the Functional Linear Model with scalar response
fregre.basis Functional Regression with scalar response using basis representation.
fregre.basis.cv Cross-validation Functional Regression with scalar response using basis representation.
fregre.bootstrap Bootstrap regression
fregre.gkam Fitting Functional Generalized Kernel Additive Models.
fregre.glm Fitting Functional Generalized Linear Models
fregre.gsam Fitting Functional Generalized Spectral Additive Models
fregre.lm Fitting Functional Linear Models
fregre.np Functional regression with scalar response using non-parametric kernel estimation
fregre.np.cv Cross-validation functional regression with scalar response using kernel estimation.
fregre.pc Functional (Ridge) Regression with scalar response using Principal Components Analysis.
fregre.pc.cv Vaidation criteria for Functional Principal Component (and Ridge) Regression using selection of number of Principal Components
fregre.plm Semi-functional partially linear model with scalar response.
fregre.pls Functional PLS regression with scalar response
fregre.pls.cv Functional PLS regression with scalar response using selection of number of PLS components
fregre.ppc Functional Penalized PC (or PLS) regression with scalar response
fregre.ppc.cv Functional penalized PC (or PLS) regression with scalar response using selection of number of PC (or PLS) components
fregre.ppls Functional Penalized PC (or PLS) regression with scalar response
fregre.ppls.cv Functional penalized PC (or PLS) regression with scalar response using selection of number of PC (or PLS) components
Ftest.statistic F-test for the Functional Linear Model with scalar response
func.mean Descriptive measures for functional and multivariate data.
func.med.FM Descriptive measures for functional and multivariate data.
func.med.HD Descriptive measures for functional and multivariate data.
func.med.MhD Descriptive measures for functional and multivariate data.
func.med.mode Descriptive measures for functional and multivariate data.
func.med.PD Descriptive measures for functional and multivariate data.
func.med.RP Descriptive measures for functional and multivariate data.
func.med.RPD Descriptive measures for functional and multivariate data.
func.med.RT Descriptive measures for functional and multivariate data.
func.med.SD Descriptive measures for functional and multivariate data.
func.trim.FM Descriptive measures for functional and multivariate data.
func.trim.HD Descriptive measures for functional and multivariate data.
func.trim.MhD Descriptive measures for functional and multivariate data.
func.trim.mode Descriptive measures for functional and multivariate data.
func.trim.PD Descriptive measures for functional and multivariate data.
func.trim.RP Descriptive measures for functional and multivariate data.
func.trim.RPD Descriptive measures for functional and multivariate data.
func.trim.RT Descriptive measures for functional and multivariate data.
func.trim.SD Descriptive measures for functional and multivariate data.
func.trimvar.FM Descriptive measures for functional and multivariate data.
func.trimvar.mode Descriptive measures for functional and multivariate data.
func.trimvar.RP Descriptive measures for functional and multivariate data.
func.trimvar.RPD Descriptive measures for functional and multivariate data.
func.trimvar.RT Descriptive measures for functional and multivariate data.
func.var Descriptive measures for functional and multivariate data.

-- G --

GCV.S The generalized cross-validation (GCV) score.

-- H --

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

-- I --

IKer.cos Integrate Smoothing Kernels.
IKer.epa Integrate Smoothing Kernels.
IKer.norm Integrate Smoothing Kernels.
IKer.quar Integrate Smoothing Kernels.
IKer.tri Integrate Smoothing Kernels.
IKer.unif Integrate Smoothing Kernels.
influence.fdata Functional influence measures
influence.quan Quantile for influence measures
inprod.fdata Inner products of Functional Data Objects o class (fdata)
int.simpson Simpson integration
is.fdata fda.usc internal functions

-- K --

Ker.cos Symmetric Smoothing Kernels.
Ker.epa Symmetric Smoothing Kernels.
Ker.norm Symmetric Smoothing Kernels.
Ker.quar Symmetric Smoothing Kernels.
Ker.tri Symmetric Smoothing Kernels.
Ker.unif Symmetric Smoothing Kernels.
Kernel Symmetric Smoothing Kernels.
Kernel.asymmetric Asymmetric Smoothing Kernel
Kernel.integrate Integrate Smoothing Kernels.
kgam.H Fitting Functional Generalized Kernel Additive Models.
kmeans.assig.groups K-Means Clustering for functional data
kmeans.center.ini K-Means Clustering for functional data
kmeans.centers.update K-Means Clustering for functional data
kmeans.fd K-Means Clustering for functional data

-- L --

lines.fdata Plot functional data: fdata.

-- M --

Math.fdata fdata S3 Group Generic Functions
metric.dist Distance Matrix Computation
metric.lp Aproximates Lp-metric distances for functional data.
min.basis Select the number of basis using GCV method.
min.np Smoothing of functional data using nonparametric kernel estimation
missing.fdata fda.usc internal functions

-- N --

ncol.fdata fda.usc internal functions
norm.fd Aproximates Lp-norm for functional data.
norm.fdata Aproximates Lp-norm for functional data.
nrow.fdata fda.usc internal functions

-- O --

omit.fdata fda.usc internal functions
omit2.fdata fda.usc internal functions
Ops.fdata fdata S3 Group Generic Functions
order.fdata A wrapper for the 'order' function
outliers.depth.pond Detecting outliers for functional dataset
outliers.depth.trim Detecting outliers for functional dataset
Outliers.fdata Detecting outliers for functional dataset
outliers.lrt Detecting outliers for functional dataset
outliers.thres.lrt Detecting outliers for functional dataset

-- P --

P.penalty Penalty matrix for higher order differences
PCvM.statistic PCvM statistic for the Functional Linear Model with scalar response
phoneme phoneme data
plot.fdata Plot functional data: fdata.
poblenou poblenou data
predict.classif Predicts from a fitted classif object.
predict.fregre.fd Predict method for functional linear model (fregre.fd class)
predict.fregre.gkam Predict method for functional regression model
predict.fregre.glm Predict method for functional regression model
predict.fregre.gsam Predict method for functional regression model
predict.fregre.lm Predict method for functional regression model
predict.fregre.plm Predict method for functional regression model
print.classif Summarizes information from kernel classification methods.
print.fregre.fd Summarizes information from fregre.fd objects.
print.fregre.gkam Summarizes information from fregre.gkam objects.
pvalue.FDR False Discorvery Rate (FDR)

-- Q --

quantile.outliers.pond Detecting outliers for functional dataset
quantile.outliers.trim Detecting outliers for functional dataset

-- R --

rangeval fda.usc internal functions
rproc2fdata Generate random process of fdata class.
rwild Wild bootstrap residuals

-- S --

S.basis Smoothing matrix with roughness penalties by basis representation.
S.KNN Smoothing matrix by nonparametric methods.
S.LLR Smoothing matrix by nonparametric methods.
S.np Smoothing matrix by nonparametric methods.
S.NW Smoothing matrix by nonparametric methods.
semimetric.basis Proximities between functional data
semimetric.deriv Proximities between functional data (semi-metrics)
semimetric.fourier Proximities between functional data (semi-metrics)
semimetric.hshift Proximities between functional data (semi-metrics)
semimetric.mplsr Proximities between functional data (semi-metrics)
semimetric.NPFDA Proximities between functional data (semi-metrics)
semimetric.pca Proximities between functional data (semi-metrics)
summary.anova Functional ANOVA with Random Project.
summary.classif Summarizes information from kernel classification methods.
Summary.fdata fdata S3 Group Generic Functions
summary.fdata.comp Correlation for functional data by Principal Component Analysis
summary.fregre.fd Summarizes information from fregre.fd objects.
summary.fregre.gkam Summarizes information from fregre.gkam objects.

-- T --

tecator tecator data
title.fdata Plot functional data: fdata.

-- V --

Var.e Sampling Variance estimates
Var.y Sampling Variance estimates

-- misc --

!=.fdata fda.usc internal functions
*.fdata fda.usc internal functions
+.fdata fda.usc internal functions
-.fdata fda.usc internal functions
/.fdata fda.usc internal functions
==.fdata fda.usc internal functions
[.fdata fda.usc internal functions
[.fdist fda.usc internal functions
^.fdata fda.usc internal functions