FVPA {fdapace} | R Documentation |
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 |
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 |
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)