ftsa (version 1.3)

mean.fts: Mean functions for functional time series

Description

Computes mean of functional time series at each variable.

Usage

mean.fts(x, method = c("coordinate", "FM", "mode", "RP", "RPD"), 
 na.rm = TRUE, ...)

Value

A list containing x = variables and y = mean rates.

Details

If method = "coordinate", it computes the coordinate-wise functional mean. If method = "FM", it computes the mean of trimmed functional data ordered by the functional depth of Fraiman and Muniz (2001). If method = "mode", it computes the mean of trimmed functional data ordered by $h$-modal functional depth. If method = "RP", it computes the mean of trimmed functional data ordered by random projection depth. If method = "RPD", it computes the mean of trimmed functional data ordered by random projection derivative depth.

References

O. Hossjer and C. Croux (1995) "Generalized univariate signed rank statistics for testing and estimating a multivariate location parameter", Journal of 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

median.fts, var.fts, sd.fts, quantile.fts

Examples

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