This function is post-hoc, pairwise test version of fkwc_multisample()
Arguments
- data
Functional data in
matrixordata.frameform, where each row is an observation/function and the columns are the grid.- derivs
First order derivative of the functional data in
matrixordata.frameform.- g
A
factorobject that indicates which sample each row of data belongs to.- p
Number of random projections to be generated in order to compute random projection depths of the data.
References
Ramsay, K., & Chenouri, S. (2024). Robust nonparametric hypothesis tests for differences in the covariance structure of functional data. Canadian Journal of Statistics, 52 (1), 43–78. https://doi.org/10.1002/cjs.11767
See also
fda.usc::fdata.deriv: for approximating the first order derivative if unavailable.
Examples
set.seed(111)
t <- seq(0, 1, length.out = 200)
### Generating three sets of brownian curves with different kernels
# Brownian process 1
fd1 <- fda.usc::rproc2fdata(n = 20, t = t, sigma = "brownian",
par.list = list(scale = 10, theta = 1))
fd1_d <- fda.usc::fdata.deriv(fd1)
# Brownian process 2
fd2 <- fda.usc::rproc2fdata(n = 20, t = t, sigma = "brownian",
par.list = list(scale = 1, theta = 1))
fd2_d <- fda.usc::fdata.deriv(fd2)
# Brownian process 3
fd3 <- fda.usc::rproc2fdata(n = 20, t = t, sigma = "brownian",
par.list = list(scale = 1, theta = 5))
fd3_d <- fda.usc::fdata.deriv(fd3)
# Functional data in one matrix and first order derivatives in another matrix
funcdata <- rbind(fd1$data, fd2$data, fd3$data)
funcderivs <- rbind(fd1_d$data, fd2_d$data, fd3_d$data)
fkwc_posthoc(data = funcdata,
derivs = funcderivs,
g = factor(rep(1:3, each = 20)),
p = 1000)
#> 1 2 3
#> 1 NA 3.439663e-07 2.198030e-07
#> 2 3.439663e-07 NA 9.999899e-01
#> 3 2.198030e-07 9.999899e-01 NA