FVPA: Functional Variance Process Analysis for dense functional...

In fdapace: Functional Data Analysis and Empirical Dynamics

Functional Variance Process Analysis for dense functional data

Description

Functional Variance Process Analysis for dense functional data

Usage

FVPA(y, t, q = 0.1, optns = list(error = TRUE, FVEthreshold = 0.9))

Arguments

y	A list of n vectors containing the observed values for each individual. Missing values specified by NAs are supported for dense case (dataType='dense').
t	A list of n vectors containing the observation time points for each individual corresponding to y.
q	A scalar defining the percentile of the pooled sample residual sample used for adjustment before taking log (default: 0.1).
optns	A list of options control parameters specified by list(name=value); by default: 'error' has to be TRUE, 'FVEthreshold' is set to 0.90. See ‘Details in ?FPCA’.

Value

A list containing the following fields:

sigma2	Variance estimator of the functional variance process.
fpcaObjY	FPCA object for the original data.
fpcaObjR	FPCA object for the functional variance process associated with the original data.

References

Hans-Georg Müller, Ulrich Stadtmüller and Fang Yao, "Functional variance processes." Journal of the American Statistical Association 101 (2006): 1007-1018

Examples

set.seed(1)
n <- 25
pts <- seq(0, 1, by=0.01)
sampWiener <- Wiener(n, pts)
# Data have to dense for FVPA to be relevant!
sampWiener <- Sparsify(sampWiener, pts, 101) 
fvpaObj <- FVPA(sampWiener$Ly, sampWiener$Lt)

fdapace documentation built on Aug. 16, 2022, 5:10 p.m.