Functional outlier detection methods.
Functional outlier detection methods.
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,...)data |
An object of class |
method |
Outlier detection method. |
dfunc |
When |
nb |
When |
suav |
When |
trim |
When |
order |
When |
lambda |
When |
... |
Other arguments. |
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.
A list containing the following components is returned.
outliers |
Detected outliers. |
cutoff |
Threshold value to separate outliers from non-outliers, when |
depth.total |
Depth measure of each functional curve. |
depth.out |
Depth measure of functional outliers. |
Han Lin Shang
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. (2010) "Rainbow plots, bagplots, and boxplots for functional data", Journal of Computational and Graphical Statistics, 19(1), 29-45.
foutliers(data = ElNino_OISST_region_1and2, method = "depth.trim")
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.