Description Usage Arguments Value Author(s) References See Also Examples
View source: R/getFunctionalOutliers.R
Gets functional outliers from a sample of curves using methods described in fda.usc
1 2 3 | 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)
|
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 |
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 |
A vector of column names that are detected as outliers.
Subhrangshu Nandi, PhD Statistics, UW Madison; snandi@wisc.edu or nands31@gmail.com
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.
outliers.depth.trim
, outliers.depth.pond
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