fdapace (version 0.1.1)

FVPA: Functional Variance Process Analysis for sparse or dense functional data

Description

Functional Variance Process Analysis for sparse or dense functional data

Usage

FVPA(y, t, q = 0.1, optns = list())

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

Examples

set.seed(1)
n <- 20
pts <- seq(0, 1, by=0.05)
sampWiener <- Wiener(n, pts)
sampWiener <- Sparsify(sampWiener, pts, 10) 
#fvpaObj <- FVPA(sampWiener$yList, sampWiener$tList)