sd.fts {ftsa}R Documentation

Standard deviation functions for functional time series

Description

Computes standard deviation of functional time series at each variable.

Usage

sd.fts(x, method = c("coordinate", "FM", "mode", "RP", "RPD"), 
 trim = 0.25,...)

Arguments

x An object of class fts.
method Method for computing median.
trim Percentage of trimming.
... Other arguments.

Details

If method = "coordinate", it computes coordinate-wise standard deviation functions.

If method = "FM", it computes the standard deviation functions of trimmed functional data ordered by the functional depth of Fraiman and Muniz (2001).

If method = "mode", it computes the standard deviation functions of trimmed functional data ordered by h-modal functional depth.

If method = "RP", it computes the standard deviation functions of trimmed functional data ordered by random projection depth.

If method = "RPD", it computes the standard deviation functions of trimmed functional data ordered by random projection derivative depth.

Value

A list containing x = variables and y = standard deviation rates.

Author(s)

Han Lin Shang

References

O. Hossjer and C. Croux (1995) "Generalized univariate signed rank statistics for testing and estimating a multivariate location parameter", Nonparametric Statistics, 4(3), 293-308.

A. Cuevas and M. Febrero and R. Fraiman (2006) "On the use of bootstrap for estimating functions with functional data", Computational Statistics & Data Analysis, 51(2), 1063-1074.

M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2008) "Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels", Environmetrics, 19(4), 331-345.

See Also

mean.fts, median.fts, var.fts, quantile.fts

Examples

sd(x = ausmale, method = "coordinate")
sd(x = ausmale, method = "FM")
sd(x = ausmale, method = "mode")
sd(x = ausmale, method = "RP")
sd(x = ausmale, method = "RPD")

[Package ftsa version 1.3 Index]