fda.usc (version 1.1.0)

Outliers.fdata: Detecting outliers for functional dataset

Description

Procedure for detecting funcitonal outliers.

Usage

outliers.thres.lrt(fdataobj,nb=200,smo=0.05,trim=0.10,...)
outliers.lrt(fdataobj,nb=200,smo=0.05,trim=0.10,...)
outliers.depth.trim(fdataobj,nb=200,smo=0.05,trim=0.01,
dfunc=depth.mode,...)
outliers.depth.pond(fdataobj,nb=200,smo=0.05,dfunc=depth.mode,...)
## S3 method for class 'outliers.pond':
quantile(x,dfunc=depth.mode,
nb=200,smo=0.05,ns=0.01,\dots)
## S3 method for class 'outliers.trim':
quantile(x,dfunc=depth.mode,trim=0.1,
nb=200,smo=0.05,ns=0.01,\dots)

Arguments

fdataobj,x

fdata class object.

nb

The number of bootstrap samples.

smo

The smoothing parameter for the bootstrap samples.

trim

The alpha of the trimming.

dfunc

Type of depth measure, by default depth.mode.

ns

Significance level, by defaul 1%.

...

Further arguments passed to or from other methods.

Value

• 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.

Details

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: i.-outliers.depth.pond function weights the data according to depth. ii.-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 data ponderation amd trimmed data respectively. 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 quantiles of the bootstrap samples.

References

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

See Also: fdata.bootstrap, Depth.

Examples

data(aemet)
nb=200
# NOT RUN
#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)