KFPCA: GenDataKL – R documentation

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KFPCA

GenDataKL

Generate functional/longitudinal data via KL expansion

Description

Generate functional/longitudinal data via Karhunen–Loève expansion.

Usage

GenDataKL(n, interval, sparse, meanfun, score, eigfun, sd)

Arguments

`n`	number of sample size.
`interval`	A `vector` of length two denoting the supporting interval.
`sparse`	A `vector` denoting the possible numbers of observation size. The elements are chosen with equal chance.
`meanfun`	A function for the mean.
`score`	A n by `nK` `matrix` containing the estimates of the FPC scores, where `nK` is the number of FPCs.
`eigfun`	A `list` containing the eigenfunctions.
`sd`	A scalar denoting the standard deviation of measurement errors.

Value

A list containing the following components:

`Lt`	A `list` of n vectors, where n is the sample size. Each entry contains the observation time in ascending order for each subject.
`Ly`	A `list` of n vectors, where n is the sample size. Each entry contains the measurements of each subject at the observation time correspond to `Lt`.

Examples

n <- 100
interval <- c(0, 10)
lambda_1 <- 9 #the first eigenvalue
lambda_2 <- 1.5 #the second eigenvalue
eigfun <- list()
eigfun[[1]] <- function(x){cos(pi * x/10)/sqrt(5)}
eigfun[[2]] <- function(x){sin(pi * x/10)/sqrt(5)}
score <- cbind(rnorm(n, 0, sqrt(lambda_1)), rnorm(n, 0, sqrt(lambda_2)))
DataNew <- GenDataKL(n, interval = interval, sparse = 6:8,
                     meanfun = function(x){0}, score = score,
                     eigfun = eigfun, sd = sqrt(0.1))

KFPCA

Kendall Functional Principal Component Analysis

v1.0

GPL (>= 3)

Authors

Rou Zhong [aut, cre]

Initial release

GenDataKL

Description

Usage

Arguments

Value

Examples

KFPCA

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