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fda.usc (version 1.2.3)

Functional Data Analysis and Utilities for Statistical Computing

Description

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.

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Version

Install

install.packages('fda.usc')

Monthly Downloads

2,504

Version

1.2.3

License

GPL-2

Maintainer

Manuel Oviedo de la Fuente

Last Published

April 28th, 2016

Functions in fda.usc (1.2.3)

False Discorvery Rate (FDR)

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

Classification Fitting Functional Generalized Additive Models

Depth for a multivariate dataset

Provides the depth measure for multivariate data

The cross-validation (CV) score

Depth for multivariate fdata

Provides the depth measure for a list of p--functional data objects

Detecting outliers for functional dataset

Classifier from Functional Data

Classification Fitting Functional Recursive Partitioning and Regression Trees

fda.usc-package

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

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

Kernel.asymmetric

Asymmetric Smoothing Kernel

Functional ANOVA with Random Project.

Conditional Distribution Function

metric.hausdorff

Compute the Hausdorff distances between two curves.

fregre.basis.cv

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

create.fdata.basis

Create Basis Set for Functional Data of fdata class

PCvM statistic for the Functional Linear Model with scalar response

Proximities between functional data

fdata S3 Group Generic Functions

Smoothing matrix by nonparametric methods.

Smoothing matrix with roughness penalties by basis representation.

fdata.bootstrap

Bootstrap samples of a functional statistic

Depth for univariate fdata

Provides the depth measure for functional data

Fitting Functional Linear Models

The deviance score .

fda.usc.internal

fda.usc internal functions

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

ANOVA for heteroscedastic data

Smoothing of functional data using nonparametric kernel estimation

Fitting Functional Generalized Spectral Additive Models

gridfdata, rcombfdata

Utils for generate functional data

Computes the derivative of functional data object.

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

fregre.bootstrap

Bootstrap regression

Mithochondiral calcium overload (MCO) data set

Penalty matrix for higher order differences

Kernel Classifier from Functional Data

Partial least squares components for functional data.

Classification Fitting Functional Generalized Kernel Additive Models

predict.classif

Predicts from a fitted classif object.

F-test for the Functional Linear Model with scalar response

Functional Penalized PLS regression with scalar response

Select the number of basis using GCV method.

fregre.basis.fr

Functional Regression with functional response using basis representation.

Functional penalized PC (or PLS) regression with scalar response using selection of number of PC (or PLS) components

Sampling Variance estimates

Fitting Functional Generalized Kernel Additive Models.

summary.fregre.gkam

Summarizes information from fregre.gkam objects.

semimetric.basis

Proximities between functional data

Functional Regression with scalar response using basis representation.

Functional penalized PC regression with scalar response using selection of number of PC components

Kullback--Leibler distance

Conditional quantile

Functional regression with scalar response using non-parametric kernel estimation

summary.fregre.fd

Summarizes information from fregre.fd objects.

Semi-functional partially linear model with scalar response.

Aproximates Lp-norm for functional data.

Simulate several random processes.

fregre.ppc,fregre.ppls

Functional Penalized PC (or PLS) regression with scalar response

semimetric.NPFDA

Proximities between functional data (semi-metrics)

Aproximates Lp-metric distances for functional data.

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

Conditional mode

Classification Fitting Functional Generalized Linear Models

Functional Regression with scalar response using Principal Components Analysis.

Descriptive measures for functional data.

The generalized cross-validation (GCV) score.

predict.classif.DD

Predicts from a fitted classif.DD object.

Symmetric Smoothing Kernels.

anova.onefactor

One--way anova model for functional data

Kernel.integrate

Integrate Smoothing Kernels.

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

Distance Matrix Computation

Principal components for functional data

DD-Classifier Based on DD-plot

Wild bootstrap residuals

predict.functional.response

Predict method for functional response model

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

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

Simpson integration

Fitting Functional Generalized Linear Models

rp.flm.statistic

Statistic for testing the FLM using random projections

summary.classif

Summarizes information from kernel classification methods.

Plot functional data: fdata.

A wrapper for the split and unlist function for fdata object

Functional influence measures

A wrapper for the order function

Converts fdata class object into fd class object

K-Means Clustering for functional data

Quantile for influence measures

predict.fregre.fd

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

Inner products of Functional Data Objects o class (fdata)

summary.fdata.comp

Correlation for functional data by Principal Component Analysis

predict.fregre.GAM

Predict method for functional regression model