median.fts {ftsa} | R Documentation |
Computes median of functional time series at each variable.
median.fts(x, method = c("hossjercroux", "coordinate", "FM", "mode", "RP", "RPD"), ...)
x |
An object of class fts . |
method |
Method for computing median. |
... |
Other arguments. |
If method = "coordinate"
, it computes a coordinate-wise median.
If method = "hossjercroux"
, it computes the L1-median using the Hossjer-Croux algorithm.
If method = "FM"
, it computes the median of trimmed functional data ordered by the functional depth of Fraiman and Muniz (2001).
If method = "mode"
, it computes the median of trimmed functional data ordered by h-modal functional depth.
If method = "RP"
, it computes the median of trimmed functional data ordered by random projection depth.
If method = "RPD"
, it computes the median of trimmed functional data ordered by random projection derivative depth.
A list containing x
= variables and y
= median rates.
Rob J Hyndman, 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
, var.fts
, sd.fts
, quantile.fts
median(x = ausmale, method = "hossjercroux") median(x = ausmale, method = "coordinate") median(x = ausmale, method = "FM") median(x = ausmale, method = "mode") median(x = ausmale, method = "RP") median(x = ausmale, method = "RPD")