scb.mean: Build Simultaneous Confidence Bands for Mean Functions
In ddegras/SCBmeanfd: Simultaneous Confidence Bands for the Mean of Functional Data

Description Usage Arguments Details Value References See Also Examples

Fit a local linear estimator and build simultaneous confidence bands (SCB) for the mean of functional data.

scb.mean(x, y, bandwidth, level = .95, degree = 1, 
	scbtype = c("normal","bootstrap","tGKF","all","no"), 
	gridsize = length(x), keep.y = TRUE, nrep = 2e4, nboot = 5e3, 
	parallel = c("no", "multicore", "snow"), 
	ncpus = getOption("boot.ncpus",1L), cl = NULL)

`x`	a numeric vector of x data. Missing values are not accepted.
`y`	a matrix or data frame with functional observations (= curves) stored in rows. The number of columns of `y` must match the length of `x`. Missing values are not accepted.
`bandwidth`	the kernel bandwidth smoothing parameter.
`level`	the level of the simultaneous confidence bands.
`degree`	the degree of the local polynomial fit.
`scbtype`	the type of simultaneous confidence bands to build: "normal", "bootstrap", "tGKF", "all", or "no".
`gridsize`	the size of the grid used to evaluate the mean function estimates and SCB. Defaults to length(x).
`keep.y`	logical; if `TRUE`, keep `y` in the result.
`nrep`	number of replicates for the Gaussian SCB method (20,000 by default).
`nboot`	number of replicates for the bootstrap SCB method (5,000 by default).
`parallel`	the computation method for the SCB. By default, computations are sequential (`"no"`). The bootstrap method uses function `boot` and can be run in parallel using the package `parallel`. In this case both options `"multicore"` and `"snow"` are available.
`ncpus`	number of cores to use for parallel computing when `parallel = "multicore"`.
`cl`	name of the cluster to use for parallel computing when `parallel = "snow"`.

The local polynomial fitting uses a standard normal kernel and is implemented via the locpoly function. Bootstrap SCB are implemented with the boot function and typically require more computation time than normal SCB.

An object of class "SCBand" with components:

`x`	the x data.
`y`	the y data if `keep.y` is `TRUE`, else `NULL`.
`call`	the function call.
`nonpar`	a nonparametric estimate.
`bandwidth`	the argument `bandwidth`.
`degree`	the degree of local polynomial used. Currently, only local linear estimation is supported.
`level`	the argument `level`.
`scbtype`	the argument `type`.
`qnorm`	the quantile used to build the normal SCB.
`qboot`	the quantile used to build the bootstrap SCB.
`qtGKF`	the quantile used to build the tGKF-SCB.
`normscb`	a matrix containing the normal SCB stored in columns.
`bootscb`	a matrix containing the bootstrap SCB stored in columns.
`tGKFscb`	a matrix containing the tGKF-SCB stored in columns.
`gridsize`	the argument `gridsize` if nonnull, else length(x).
`nrep`	the argument `nrep`.
`nboot`	the argument `nboot`.

Depending on scbtype, some of the fields qnorm, normscb, nrep, qboot, normboot, nboot, qtGKF, and tGKFscb may be NULL.

Degras, D. (2011). Simultaneous confidence bands for nonparametric regression with functional data. Statistica Sinica 21, 1735–1765.

Degras, D. (2017). Simultaneous confidence bands for the mean of functional data. WIREs Computational Statistics e1397.

Telschow, F.J.E. and Schwartzman, A. (2019). Simultaneous confidence bands for functional data using the Gaussian kinematic formula. arXiV

scb.equal, scb.model

## Not run: 
## Plasma citrate data
data(plasma)
time <- 8:21
h <- cv.select(time, plasma, 1, c(.5, 1))
scbplasma <- scb.mean(time, plasma, bandwidth = h, scbtype = "both", gridsize = 100)
scbplasma
plot(scbplasma, cex = .2, legend.cex = .85, xlab = "Time", ylab = "Concentration", 
  main = "Plasma citrate data")

## End(Not run)