outliers for functional dataset
Procedure for detecting funcitonal outliers.
outliers.depth.pond( fdataobj, nb = 200, smo = 0.05, quan = 0.5, dfunc = depth.mode, ... ) outliers.depth.trim( fdataobj, nb = 200, smo = 0.05, trim = 0.01, quan = 0.5, dfunc = depth.mode, ... ) outliers.lrt(fdataobj, nb = 200, smo = 0.05, trim = 0.1, ...) outliers.thres.lrt(fdataobj, nb = 200, smo = 0.05, trim = 0.1, ...)
fdataobj |
|
nb |
The number of bootstrap samples. |
smo |
The smoothing parameter for the bootstrap samples. |
quan |
Quantile to determine the cutoff from the Bootstrap procedure (by default=0.5) |
dfunc |
Type of depth measure, by default |
... |
Further arguments passed to or from other methods. |
trim |
The alpha of the trimming. |
Outlier detection in functional data by likelihood ratio test (outliers.lrt). The threshold for outlier detection is given by the
outliers.thres.lrt.
Outlier detection in functional data by depth measures:
outliers.depth.pond function weights the data according to depth.
outliers.depth.trim function uses trimmed data.
quantile.outliers.pond and quantile.outliers.trim functions provides the quantiles of the bootstrap samples for functional outlier detection by, respectively, weigthed and trimmed procedures. Bootstrap smoothing function (fdata.bootstrap with nb resamples) is applied to these weighted or trimmed data. If smo=0 smoothed bootstrap is not performed. The function returns a vector of size 1xnb with bootstrap replicas of the quantile.
outliers Indexes of functional outlier.
dep.out Depth value of functional outlier.
dep.out Iteration in which the functional outlier is detected.
quantile Threshold for outlier detection.
dep Depth value of functional data.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
Cuevas A, Febrero M, Fraiman R. 2006. On the use of bootstrap for estimating functions with functional data. Computational Statistics and Data Analysis 51: 1063-1074.
Febrero-Bande, M., Galeano, P., and Gonzalez-Manteiga, W. (2008). Outlier detection in functional data by depth measures with application to identify abnormal NOx levels. Environmetrics 19, 4, 331-345.
Febrero-Bande, M., Galeano, P. and Gonzalez-Manteiga, W. (2007). A functional analysis of NOx levels: location and scale estimation and outlier detection. Computational Statistics 22, 3, 411-427.
Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/
See Also: fdata.bootstrap, Depth.
## Not run: data(aemet) nb=20 # Time consuming out.trim<-outliers.depth.trim(aemet$temp,dfunc=depth.FM,nb=nb) plot(aemet$temp,col=1,lty=1) lines(aemet$temp[out.trim[[1]]],col=2) ## End(Not run)
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