sd.fts {ftsa} | R Documentation |
Computes standard deviation of functional time series at each variable.
sd.fts(x, method = c("coordinate", "FM", "mode", "RP", "RPD"), trim = 0.25,...)
x |
An object of class fts . |
method |
Method for computing median. |
trim |
Percentage of trimming. |
... |
Other arguments. |
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.
A list containing x
= variables and y
= standard deviation rates.
Han Lin Shang
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.
mean.fts
, median.fts
, var.fts
,
quantile.fts
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")