joint_gauss_model: Gaussian model of functional data using joint Model

In fdasrvf: Elastic Functional Data Analysis

Gaussian model of functional data using joint Model

Description

This function models the functional data using a Gaussian model extracted from the principal components of the srvfs using the joint model

Usage

joint_gauss_model(warp_data, n = 1, no = 5)

Arguments

warp_data	fdawarp object from time_warping of aligned data
n	number of random samples (n = 1)
no	number of principal components (n=4)

Value

Returns a fdawarp object containing

fs	random aligned samples
gams	random warping function samples
ft	random function samples
qs	random srvf samples

References

Tucker, J. D., Wu, W., Srivastava, A., Generative Models for Function Data using Phase and Amplitude Separation, Computational Statistics and Data Analysis (2012), 10.1016/j.csda.2012.12.001.

Jung, S. L. a. S. (2016). "Combined Analysis of Amplitude and Phase Variations in Functional Data." arXiv:1603.01775.

Examples

out1 <- joint_gauss_model(simu_warp, n = 10)

fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.