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fda.usc (version 0.9.5)

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

0.9.5

License

GPL-2

Maintainer

Oviedo la Fuente

Last Published

April 27th, 2011

Functions in fda.usc (0.9.5)

fregre.np.cv

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

Depth

Provides the depth measure for functional data

int.simpson

Simpson integration

CV.S

The cross-validation (CV) score

Descriptive

Descriptive measures for functional data.

S.basis

Smoothing matrix with roughness penalties by basis representation.

fda.usc.internal

fda.usc internal functions

anova.hetero

ANOVA for heteroscedastic data

fregre.pc.cv

Functional Regression using selection of number of principal components

fdata.bootstrap

Bootstrap samples of a functional statistic

h.default

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

FDR

False Discorvery Rate (FDR)

metric.lp

Aproximates Lp-metric distances for functional data.

fregre.basis.cv

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

fdata2fd

Converts fdata class object into fd class object

inprod.fdata

Inner products of Functional Data Objects o class (fdata)

min.basis

Select the number of basis using GCV method.

depth.RPD

Provides the depth measure by random projections using derivatives

summary.fregre.fd

Summarizes information from fregre.fd objects.

plot.fdata

Plot functional data: fdata.

fdata.deriv

Computes the derivative of functional data object.

fregre.pls.cv

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

depth.mode

Provides the depth measure (mode) for functional data

fregre.np

Functional regression with scalar response using non-parametric kernel estimation

Outliers.fdata

Detecting outliers for functional dataset

fregre.basis

Functional Regression with scalar response using basis representation.

anova.RPm

Functional ANOVA with Random Project.

Kernel

Symmetric Smoothing Kernels.

classif.knn.fd

k-Nearest Neighbor Classifier from Functional Data

cond.quantile

Conditional quantile

fregre.glm

Fitting Functional Generalized Linear Models

create.fdata.basis

Create Basis Set for Functional Data of fdata class

phoneme

phoneme data

influnce.fdata

Functional influence measures

semimetric.NPFDA

Proximities between functional data (semi-metrics)

fdata

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

predict.fregre.lm

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

classif.kernel.fb

Kernel classifier from Functional Data Training by basis representation

fregre.plm

Semi-functional linear regression with scalar response.

influence.quan

Quantile for influence measures

cond.F

Conditional Distribution Function

Kernel.asymmetric

Asymmetric Smoothing Kernel

summary.classif.fd

Summarizes information from kernel classification methods.

kmeans.fd

K-Means Clustering for functional data

semimetric.basis

Proximities between functional data

predict.fregre.glm

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

fregre.pls

Functional PLS regression with scalar response

predict.fregre.fd

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

fregre.lm

Fitting Functional Linear Models

pc.cor

Correlation for functional data by Principal Component Analysis

tecator

tecator data

depth.FM

Fraiman-Muniz depth measure

GCV.S

The generalized cross-validation (GCV) score.

classif.kernel.fd

Kernel Classifier from Functional Data

norm.fdata

Aproximates Lp-norm for functional data.

fdata.cen

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

cond.mode

Conditional mode

pls.fdata

Partial least squares components for functional data.

predict.fregre.plm

Predict method for semi-functional linear regression model.

predict.classif.fd

Predicts from a fitted classif.fd object.

poblenou

poblenou data

fregre.pc

Functional Regression with scalar response using Principal Components Analysis.

Kernel.integrate

Integrate Smoothing Kernels.

pc.fdata

Principal components for functional data

min.np

Smoothing of functional data using nonparametric kernel estimation

S.np

Smoothing matrix by nonparametric methods.

depth.RP

Provides the depth measure using random projections for functional data

Var.y

Sampling Variance estimates

aemet

aemet data

fda.usc-package

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