CreateOutliersPlot: Functional Principal Component or Functional Singular Value...

In fdapace: Functional Data Analysis and Empirical Dynamics

Functional Principal Component or Functional Singular Value Decomposition Scores Plot using 'bagplot' or 'KDE' methodology

Description

This function will create, using the first components scores, a set of convex hulls of the scores based on 'bagplot' or 'KDE' methodology.

Usage

CreateOutliersPlot(fObj, optns = NULL, ...)

Arguments

fObj	A class object returned by FPCA() or FSVD().
optns	A list of options control parameters specified by list(name=value). See ‘Details’.
...	Additional arguments for the 'plot' function.

Details

Available control options are

ifactor

inflation ifactor for the bag-plot defining the loop of bag-plot or multiplying ifactor the KDE pilot bandwidth matrix. (see ?aplpack::compute.bagplot; ?ks::Hpi respectively; default: 2.58; 2 respectively).

variant

string defining the outlier method used ('KDE', 'NN' or 'bagplot') (default: 'KDE')

unimodal

logical specifying if the KDE estimate should be unimodal (default: FALSE, relevant only for variant='KDE')

maxVar

logical specifying if during slicing we should used the directions of maximum variance (default: FALSE for FPCA, TRUE for FSVD)

nSlices

integer between 3 and 16, denoting the number of slices to be used (default: 4, relevant only for groupingType='slice')

showSlices

logical specifying if during slicing we should show the outline of the slice (default: FALSE)

colSpectrum

character vector to be use as input in the 'colorRampPalette' function defining the outliers colours (default: c("red", "yellow", 'blue'), relevant only for groupingType='slice')

groupingType

string specifying if a slice grouping ('slice') or a standard percentile/bagplot grouping ('standard') should be returned (default: 'standard')

fIndices

a two-component vector with the index of the mode of variation to consider (default: c(1,2) for FPCA and c(1,1) for FSVD)

Value

An (temporarily) invisible copy of a list containing the labels associated with each of sample curves.

References

P. J. Rousseeuw, I. Ruts, J. W. Tukey (1999): The bagplot: a bivariate boxplot, The American Statistician, vol. 53, no. 4, 382-387 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

Examples

set.seed(1)
n <- 420
pts <- seq(0, 1, by=0.05)
sampWiener <- Wiener(n, pts)
sampWiener <- Sparsify(sampWiener, pts, 10)
res <- FPCA(sampWiener$Ly, sampWiener$Lt, 
            list(dataType='Sparse', error=FALSE, kernel='epan', verbose=TRUE))
CreateOutliersPlot(res)

fdapace documentation built on July 3, 2024, 5:08 p.m.