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fda.usc (version 1.0.0)

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

Description

The R package fda.usc carries out exploratory and descriptive analysis of functional data such as depth measurements or functional atypical curves detection, functions to compute functional regression models with a scalar response, supervised and unsupervised classification methods and functional analysis of variance.

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Version

Install

install.packages('fda.usc')

Monthly Downloads

5,150

Version

1.0.0

License

GPL-2

Maintainer

Manuel Oviedo de la Fuente

Last Published

October 19th, 2012

Functions in fda.usc (1.0.0)

ANOVA for heteroscedastic data

F-test for the Functional Linear Model with scalar response

Principal components for functional data

Functional ANOVA with Random Project.

fda.usc.internal

fda.usc internal functions

Computes the derivative of functional data object.

Descriptive measures for functional data.

PCvM statistic for the Functional Linear Model with scalar response

The cross-validation (CV) score

Functional regression with scalar response using non-parametric kernel estimation

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

Fitting Functional Linear Models

Fitting Functional Generalized Spectral Additive Models

fregre.bootstrap

Bootstrap regression

Conditional Distribution Function

Classification Fitting Functional Generalized Additive Models

Conditional quantile

Classification Fitting Functional Generalized Linear Models

Classification Fitting Functional Generalized Kernel Additive Models

False Discorvery Rate (FDR)

Quantile for influence measures

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

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

The deviance score .

Kernel.integrate

Integrate Smoothing Kernels.

fdata.bootstrap

Bootstrap samples of a functional statistic

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

Functional PLS regression with scalar response

Kernel Classifier from Functional Data

Detecting outliers for functional dataset

Conditional mode

Provides the depth measure (mode) for functional data

Smoothing matrix by nonparametric methods.

Provides the depth measure for functional data

Sampling Variance estimates

predict.fregre.lm

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

create.fdata.basis

Create Basis Set for Functional Data of fdata class

Smoothing of functional data using nonparametric kernel estimation

Functional influence measures

predict.fregre.glm

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

semimetric.NPFDA

Proximities between functional data (semi-metrics)

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

Kernel.asymmetric

Asymmetric Smoothing Kernel

summary.fdata.comp

Correlation for functional data by Principal Component Analysis

Generate random process of fdata class.

Select the number of basis using GCV method.

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

semimetric.basis

Proximities between functional data

K-Means Clustering for functional data

predict.fregre.gsam

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

predict.fregre.fd

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

Provides the depth measure using random projections for functional data

Converts fdata class object into fd class object

Semi-functional partially linear model with scalar response.

fda.usc-package

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

The generalized cross-validation (GCV) score.

Proximities between functional data

summary.classif

Summarizes information from kernel classification methods.

Inner products of Functional Data Objects o class (fdata)

Simpson integration

Distance Matrix Computation

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

fdata S3 Group Generic Functions

Symmetric Smoothing Kernels.

Aproximates Lp-metric distances for functional data.

Fitting Functional Generalized Linear Models

Partial least squares components for functional data.

Provides the depth measure by random projections using derivatives

Fitting Functional Generalized Kernel Additive Models.

fregre.basis.cv

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

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

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

Gold section bootstrap sampling

predict.classif

Predicts from a fitted classif object.

predict.fregre.gkam

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

Plot functional data: fdata.

Aproximates Lp-norm for functional data.

Smoothing matrix with roughness penalties by basis representation.

summary.fregre.fd

Summarizes information from fregre.fd objects.

summary.fregre.gkam

Summarizes information from fregre.gkam objects.

Fraiman-Muniz depth measure

Functional Regression with scalar response using basis representation.

predict.fregre.plm

Predict method for semi-functional linear regression model.