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fdapace (version 0.1.1)

Functional Data Analysis and Empirical Dynamics

Description

Provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled. PACE provides options for functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

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Install

install.packages('fdapace')

Monthly Downloads

3,862

Version

0.1.1

License

BSD_3_clause + file LICENSE

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Repository

https://github.com/hadjipantelis/tPACE

Maintainer

Pantelis Z Hadjipantelis

Last Published

March 14th, 2016

Functions in fdapace (0.1.1)

Check option format

CreateDiagnosticsPlot

Functional Principal Component Analysis Diagnostics plot

CreateScreePlot

Create the scree plot for the fitted eigenvalues

Create the sample path plot based on the results from FPCA().

Create the covariance surface plot based on the results from FPCA() or FPCder().

Make cross-correlation matrix from auto- and cross-covariance matrix

PACE: Principal Analysis by Conditional Expectation

Plot an FPCA object.

Functional Cross Covariance between longitudinal variable Y and scalar variable Z

CreateOutliersPlot

Functional Principal Component Scores Plot using 'bagplot' or 'KDE' methodology

Functional Principal Component Analysis Bandwidth Diagnostics plot

Functional Variance Process Analysis for sparse or dense functional data

Make cross-correlation matrix from auto- and cross-covariance matrix

Set the PCA option list

Selects number of functional principal components for given FPCA output and selection criteria

CreateDesignPlot

Create the design plot of the functional data.

Print an FPCA object

Take derivative of an FPCA object

Format FPCA input

GetNormalisedSample

Normalise sparse functional sample

One dimensional local linear kernel smoother

Number of eggs laid daily from medflies

Functional Principal Component Analysis

Functional Principal Component Analysis Concurrent Regression with Functional dependent variable

CreateModeOfVarPlot

Functional Principal Component Analysis mode of variation plot

Two dimensional local linear kernel smoother.

MakeHCtoZscore02y

Z-score head-circumference for age 0 to 24 months based on WHO standards

MakeBWtoZscore02y

Z-score body-weight for age 0 to 24 months based on WHO standards

Fitted functional sample from FPCA object

Sparsify densely observed functional data

Simulate standard Wiener processes (Brownian motions)

Functional Cross Covariance between longitudinal variable Y and longitudinal variable X

Check data format

HandleNumericsAndNAN

Check if NaN are present in the data and if yes remove them

CreateFuncBoxPlot

Create functional boxplot using 'bagplot', 'KDE' or 'pointwise' methodology

MakeLNtoZscore02y

Z-score height for age 0 to 24 months based on WHO standards