Functional Variance Process Analysis for dense functional data
Functional Variance Process Analysis for dense functional data
FVPA(y, t, q = 0.1, optns = list(error = TRUE, FVEthreshold = 0.9))
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 |
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. |
Hans-Georg Müller, Ulrich Stadtmüller and Fang Yao, "Functional variance processes." Journal of the American Statistical Association 101 (2006): 1007-1018
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)
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