A logical value indicating whether NA values should be stripped before the computation proceeds.
alpha
Tuning parameter when method="radius".
beta
Trimming percentage, by default it is 0.25, when method="radius".
weight
Hard thresholding or soft thresholding.
...
Other arguments.
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.
If method = "radius", it computes the mean of trimmed functional data ordered by the notion of alpha-radius.
Value
A list containing x = variables and y = mean rates.
Author(s)
Rob J Hyndman, Han Lin Shang
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.
A. Cuevas and M. Febrero and R. Fraiman (2007), "Robust estimation and classification for functional data via projection-based depth notions", Computational Statistics, 22(3), 481-496.
M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2007) "A functional analysis of NOx levels: location and scale estimation and outlier detection", Computational Statistics, 22(3), 411-427.
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.
M. Febrero and P. Galeano and W. Gonzalez-Manteiga (2010) "Measures of influence for the functional linear model with scalar response", Journal of Multivariate Analysis, 101(2), 327-339.
J. A. Cuesta-Albertos and A. Nieto-Reyes (2010) "Functional classification and the random Tukey depth. Practical issues", Combining Soft Computing and Statistical Methods in Data Analysis, Advances in Intelligent and Soft Computing, Volume 77, 123-130.
D. Gervini (2012) "Outlier detection and trimmed estimation in general functional spaces", Statistica Sinica, 22(4), 1639-1660.