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fda.usc (version 0.9.8.1)

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

Description

This package implements functional data methods.

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Version

Install

install.packages('fda.usc')

Monthly Downloads

5,150

Version

0.9.8.1

License

GPL-2

Homepage

http://eio.usc.es/pub/MAESFE/

Maintainer

Oviedo la Fuente

Last Published

July 3rd, 2012

Functions in fda.usc (0.9.8.1)

depth.FM

Fraiman-Muniz depth measure

inprod.fdata

Inner products of Functional Data Objects o class (fdata)

CV.S

The cross-validation (CV) score

min.np

Smoothing of functional data using nonparametric kernel estimation

fdata.deriv

Computes the derivative of functional data object.

fda.usc-package

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

fregre.basis

Functional Regression with scalar response using basis representation.

fdata.bootstrap

Bootstrap samples of a functional statistic

Descriptive

Descriptive measures for functional data.

dfv.test

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

fregre.gsam

Fitting Functional Generalized Spectral Additive Models

poblenou

poblenou data

Kernel

Symmetric Smoothing Kernels.

predict.fregre.lm

Predict method for functional linear model of fregre.lm fits object

S.basis

Smoothing matrix with roughness penalties by basis representation.

fregre.np

Functional regression with scalar response using non-parametric kernel estimation

Outliers.fdata

Detecting outliers for functional dataset

fdata2fd

Converts fdata class object into fd class object

Var.y

Sampling Variance estimates

fregre.pc

Functional (Ridge) Regression with scalar response using Principal Components Analysis.

classif.glm

Classification Fitting Functional Generalized Linear Models

fdata

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

predict.fregre.glm

Predict method for functional linear model of fregre.glm fits object

S.np

Smoothing matrix by nonparametric methods.

rproc2fdata

Generate random process of fdata class.

cond.F

Conditional Distribution Function

fregre.pls

Functional PLS regression with scalar response

plot.fdata

Plot functional data: fdata.

classif.gkam

Classification Fitting Functional Generalized Kernel Additive Models

fregre.lm

Fitting Functional Linear Models

anova.RPm

Functional ANOVA with Random Project.

fda.usc.internal

fda.usc internal functions

fregre.basis.cv

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

predict.fregre.gsam

Predict method for functional generalized spectral additive model of fregre.gsam fits object

FDR

False Discorvery Rate (FDR)

summary.fregre.fd

Summarizes information from fregre.fd objects.

depth.mode

Provides the depth measure (mode) for functional data

Kernel.integrate

Integrate Smoothing Kernels.

Depth

Provides the depth measure for functional data

summary.fdata.comp

Correlation for functional data by Principal Component Analysis

phoneme

phoneme data

depth.RPD

Provides the depth measure by random projections using derivatives

dev.S

The deviance score .

predict.fregre.gkam

Predict method for functional generalized kernel additive model of fregre.gkam fits object

influence.quan

Quantile for influence measures

semimetric.basis

Proximities between functional data

aemet

aemet data

rber.gold

Gold section bootstrap sampling

anova.hetero

ANOVA for heteroscedastic data

flm.Ftest

F-test for the Functional Linear Model with scalar response

fdata.cen

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

kmeans.fd

K-Means Clustering for functional data

fregre.pc.cv

Vaidation criteria for Functional Principal Component (and Ridge) Regression using selection of number of Principal Components

predict.classif

Predicts from a fitted classif object.

fregre.gkam

Fitting Functional Generalized Kernel Additive Models.

h.default

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

cond.quantile

Conditional quantile

flm.test

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

metric.lp

Aproximates Lp-metric distances for functional data.

Kernel.asymmetric

Asymmetric Smoothing Kernel

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

GCV.S

The generalized cross-validation (GCV) score.

dis.cos.cor

Proximities between functional data

predict.fregre.plm

Predict method for semi-functional linear regression model.

semimetric.NPFDA

Proximities between functional data (semi-metrics)

fregre.plm

Semi-functional partially linear model with scalar response.

classif.gsam

Classification Fitting Functional Generalized Additive Models

classif.np

Kernel Classifier from Functional Data

fdata2pc

Principal components for functional data

fregre.pls.cv

Functional PLS regression with scalar response using selection of number of PLS components

int.simpson

Simpson integration

fregre.glm

Fitting Functional Generalized Linear Models

min.basis

Select the number of basis using GCV method.

influnce.fdata

Functional influence measures

fregre.bootstrap

Bootstrap regression

create.fdata.basis

Create Basis Set for Functional Data of fdata class

norm.fdata

Aproximates Lp-norm for functional data.

summary.fregre.gkam

Summarizes information from fregre.gkam objects.

summary.classif

Summarizes information from kernel classification methods.

depth.RP

Provides the depth measure using random projections for functional data

fregre.np.cv

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

PCvM.statistic

PCvM statistic for the Functional Linear Model with scalar response

fdata2pls

Partial least squares components for functional data.

fdata.methods

fdata S3 Group Generic Functions

predict.fregre.kgam

Predict method for functional kernel generalized additive model of fregre.kgam fits object

fregre.kgam

Fitting Functional Generalized Additive Models.

classif.kgam

Classification Fitting Functional Generalized Additive Models

summary.fregre.kgam

Summarizes information from fregre.kgam objects.