getFunctionalOutliers: Gets functional outliers from a sample of curves

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/getFunctionalOutliers.R

Description

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

Usage

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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; snandi@wisc.edu or nands31@gmail.com

References

See Also

outliers.depth.trim, outliers.depth.pond

Examples

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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.