getFunctionalOutliers: Gets functional outliers from a sample of curves

In snandi/Registration: Contains functions for registration algorithms, simulation and testing

Description

Gets functional outliers from a sample of curves using methods described in fda.usc

Usage

getFunctionalOutliers(Curves, Xaxis, Names = list(main = "Main", xlab =
  "PixelPosition", ylab = "Intensity"), DepthType = c("FM", "Mode", "RTukey",
  "RProj"), N_Bootstrap = 500, Trim = c("Yes", "No"), TrimPct = 0.05)

Arguments

Curves	A matrix (or dataframe) of curves, with each column being a separate curve
Xaxis	The abscissa
Names	list(main = 'Main', xlab = 'PixelPosition', ylab = 'Intensity')
DepthType	Either of the four c( 'FM', 'Mode', 'RTukey', 'RProj' )
N_Bootstrap	Number of boostrap samples. Defaults to 500.
Trim	Whether to Trim the samples or not.
TrimPct	How much to Trim the samples or not

Value

A vector of column names that are detected as outliers.

Author(s)

Subhrangshu Nandi, PhD Statistics, UW Madison; [e-mail] or [e-mail]

References

• 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.

• 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

outliers.depth.trim, outliers.depth.pond

Examples

data( growth, package = 'fda' )
Mat1 <- growth[['hgtm']]
Arguments <- growth[['age']]
getFunctionalOutliers( 
  Curves = Mat1, 
  Xaxis = Arguments, 
  Names = list(main = 'Main', xlab = 'PixelPosition', ylab = 'Intensity'),
  DepthType = 'FM',
  N_Bootstrap = 500,
  Trim = 'Yes',
  TrimPct = 0.05
)

snandi/Registration documentation built on May 30, 2019, 5:04 a.m.