Learn R Programming
rainbow (version 1.6)

foutliers: Functional outlier detection methods.

Description

Functional outlier detection methods.

Usage

foutliers(data, method = c("robMah", "lrt", "depth.trim", "depth.pond", 
 "HUoutliers"), dfunc = depth.mode, nb = 200, suav = 0.05, trim = 0.1, 
  order = 2, lambda = 3.29,...)

Arguments

data
An object of class fds or fts.
method
Outlier detection method.
dfunc
When method="lrt" or method="depth.trim" or method="depth.pond", users can specify the type of depth functions with possible choices of depth.FM, depth.mode, depth.RP, depth.RPD.
nb
When method="lrt", users can specify the number of bootstrap samples.
suav
When method="lrt", users can specify the smoothing parameter used in the smoothed bootstrap samples to determine the cutoff value.
trim
When method="lrt" or method="depth.trim" or method="depth.pond", users can specify the trimming percentage.
order
When method="HUoutliers", users can specify the number of principal components.
lambda
When method="HUoutliers", users can specify the value of tuning parameter.
...
Other arguments.

Value

  • A list containing the following components is returned.
  • outliersDetected outliers.
  • cutoffThreshold value to separate outliers from non-outliers, when method="lrt", method="depth.trim", and method="depth.pond".
  • depth.totalDepth measure of each functional curve.
  • depth.outDepth measure of functional outliers.

Details

When method="lrt", the outlier detection method corresponds to the approach of Febrero et al. (2007) using the likelihood ratio test. When method="depth.trim", the outlier detection method corresponds to the approach of Febrero et al. (2008) using the functional depth with trimmed curves. When method="depth.pond", the outlier detection method corresponds to the approach of Febrero et al. (2008) using the functional depth with all curves. When method="HUoutliers", the outlier detection method corresponds to the approach of Hyndman and Ullah (2008) using the integrated square forecast errors. When method="robMah", the outlier detection method corresponds to the approach of Rousseeuw and Leroy (1987) using the robust Mahalanobis distance.

References

P. Rousseeuw and A. Leroy (1987) Robust regression and outlier detection, John Wiley and Sons, New York. A. Atkinson (1994) "Fast very robust methods for the detection of multiple outliers", Journal of the American Statistical Association, 89(428), 1329-1339. R. J. Hyndman and M. S. Ullah (2007) "Robust forecasting of mortality and fertility rates: A functional data approach", Computational Statistics and Data Analysis, 51(10), 4942-4956. 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. R. J. Hyndman and H. L. Shang. (2009) "Rainbow plots, bagplots, and boxplots for functional data", Journal of Computational and Graphical Statistics, in press.

Examples

Run this code
foutliers(data = ElNino, method = "lrt")
foutliers(data = ElNino, method = "depth.trim")
foutliers(data = ElNino, method = "depth.pond")
foutliers(data = ElNino, method = "HUoutliers")
foutliers(data = ElNino, method = "robMah")

Run the code above in your browser using DataLab