# Functional Data Analysis and Utilities for Statistical Computing

## Documentation for package ‘fda.usc’ version 1.3.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A B C D F G H I K L M N O P Q R S T U 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.onefactor | One-way anova model for functional data
anova.RPm | Functional ANOVA with Random Project.
anova.RPm.boot | Functional ANOVA with Random Project.
anyNA.fdata | fda.usc internal functions
argvals | fda.usc internal functions

## -- B --

bcdcor.dist | Distance Correlation Statistic and t-Test

## -- C --

c.fdata | fda.usc internal functions
classif.DD | DD-Classifier Based on DD-plot
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
classif.tree | Classification Fitting Functional Recursive Partitioning and Regression Trees
cond.F | Conditional Distribution Function
cond.mode | Conditional mode
cond.quantile | Conditional quantile
count.na.fdata | fda.usc internal functions
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 --

dcor.dist | Distance Correlation Statistic and t-Test
dcor.test | Distance Correlation Statistic and t-Test
dcor.xy | Distance Correlation Statistic and t-Test
Depth | Provides the depth measure for functional data
depth.FM | Provides the depth measure for functional data
depth.FMp | Provides the depth measure for a list of p-functional data objects
depth.FSD | Provides the depth measure for functional data
depth.KFSD | Provides the depth measure for functional data
depth.mode | Provides the depth measure for functional data
depth.modep | Provides the depth measure for a list of p-functional data objects
Depth.Multivariate | Provides the depth measure for multivariate data
Depth.pfdata | Provides the depth measure for a list of p-functional data objects
depth.RP | Provides the depth measure for functional data
depth.RPD | Provides the depth measure for functional data
depth.RPp | Provides the depth measure for a list of p-functional data objects
depth.RT | Provides the depth measure for functional data
Descriptive | Descriptive measures for functional 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.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.gls | Fit Functional Linear Model Using Generalized Least Squares
fregre.gsam | Fitting Functional Generalized Spectral Additive Models
fregre.igls | Fit of Functional Generalized Least Squares Model Iteratively
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 Regression with scalar response using Principal Components Analysis.
fregre.pc.cv | Functional penalized PC regression with scalar response using selection of number of PC components
fregre.plm | Semi-functional partially linear model with scalar response.
fregre.pls | Functional Penalized PLS regression with scalar response
fregre.pls.cv | Functional penalized 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 data.
func.mean.formula | Descriptive measures for functional data.
func.med.FM | Descriptive measures for functional data.
func.med.mode | Descriptive measures for functional data.
func.med.RP | Descriptive measures for functional data.
func.med.RPD | Descriptive measures for functional data.
func.med.RT | Descriptive measures for functional data.
func.trim.FM | Descriptive measures for functional data.
func.trim.mode | Descriptive measures for functional data.
func.trim.RP | Descriptive measures for functional data.
func.trim.RPD | Descriptive measures for functional data.
func.trim.RT | Descriptive measures for functional data.
func.trimvar.FM | Descriptive measures for functional data.
func.trimvar.mode | Descriptive measures for functional data.
func.trimvar.RP | Descriptive measures for functional data.
func.trimvar.RPD | Descriptive measures for functional data.
func.trimvar.RT | Descriptive measures for functional data.
func.var | Descriptive measures for functional data.

## -- G --

GCCV.S | The generalized correlated cross-validation (GCCV) score.
GCV.S | The generalized cross-validation (GCV) score.
gridfdata | Utils for generate functional data

## -- 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
is.na.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 --

length.fdata | fda.usc internal functions
lines.fdata | Plot functional data: fdata.

## -- M --

Math.fdata | fdata S3 Group Generic Functions
MCO | Mithochondiral calcium overload (MCO) data set
mdepth.HS | Provides the depth measure for multivariate data
mdepth.LD | Provides the depth measure for multivariate data
mdepth.MhD | Provides the depth measure for multivariate data
mdepth.RP | Provides the depth measure for multivariate data
mdepth.SD | Provides the depth measure for multivariate data
mdepth.TD | Provides the depth measure for multivariate data
metric.dist | Distance Matrix Computation
metric.hausdorff | Compute the Hausdorff distances between two curves.
metric.kl | Kullback-Leibler distance
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 --

na.fail.fdata | A wrapper for the na.omit and na.fail function for fdata object
na.omit.fdata | A wrapper for the na.omit and na.fail function for fdata object
NCOL.fdata | fda.usc internal functions
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
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.bifd | Plot functional data: fdata.
plot.fdata | Plot functional data: fdata.
poblenou | poblenou data
predict.classif | Predicts from a fitted classif object.
predict.classif.DD | Predicts from a fitted classif.DD object.
predict.fregre.fd | Predict method for functional linear model (fregre.fd class)
predict.fregre.fr | Predict method for functional response model
predict.fregre.gkam | Predict method for functional regression model
predict.fregre.glm | Predict method for functional regression model
predict.fregre.gls | Predictions from a functional gls object
predict.fregre.gsam | Predict method for functional regression model
predict.fregre.igls | Predictions from a functional gls object
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
rcombfdata | Utils for generate functional data
rp.flm.statistic | Statistic for testing the FLM using random projections
rp.flm.test | Goodness-of-fit test for the Functional Linear Model with scalar response using random projections
rproc2fdata | Simulate several random processes.
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)
split.fdata | A wrapper for the split and unlist function for fdata object
subset.fdata | Subsetting
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.

## -- U --

unlist.fdata | A wrapper for the split and unlist function for fdata object

## -- 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