fboxplot: Functional bagplot and functional HDR boxplot

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

Description

Compute bivariate bagplot, functional bagplot and bivariate HDR boxplot, functional HDR boxplot.

Usage

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fboxplot(data, plot.type = c("functional", "bivariate"),
	type = c("bag", "hdr"), alpha = c(0.05, 0.5), projmethod = c("PCAproj","rapca"),
	factor = 1.96, na.rm = TRUE, xlab = data$xname, ylab = data$yname, 
	shadecols = gray((9:1)/10), pointcol = 1, plotlegend = TRUE, 
	legendpos = "topright", ncol = 2, ...) 

Arguments

data

An object of class fds or fts.

plot.type

Version of boxplot. When plot.type="functional", a functional plot is provided. When plot.type="bivariate", a square bivariate plot is provided.

type

Type of boxplot. When type = "bag", a bagplot is provided. When type = "hdr", a HDR boxplot is provided.

alpha

Coverage probability for the functional HDR boxplot. alpha are the coverage percentages of the outliers and the central region.

factor

When type = "bag", the outer region of a bagplot is the convex hull obtained by inflating the inner region by the bagplot factor.

na.rm

Remove missing values.

xlab

A title for the x axis.

ylab

A title for the y axis.

shadecols

Colors for shaded regions.

pointcol

Color for outliers and mode.

plotlegend

Add a legend to the graph.

legendpos

Legend position. By default, it is the top right corner.

ncol

Number of columns in the legend.

projmethod

Method used for projection.

...

Other arguments.

Details

The functional curves are first projected into a finite dimensional subspace via functional principal component decomposition. For simiplicity, we choose the subspace as R^2. Based on Tukey (1974)'s halfspace bagplot and Hyndman (1996)'s HDR boxplot, we order each data point in R^2 by data depth and data density. Outliers are those that have either lowest depth (distance from the centre) or lowest density.

Value

Function produces a graphical plot.

Author(s)

Rob J Hyndman, Han Lin Shang. Please, report bugs and suggestions to [email protected]

References

J. W. Tukey (1974) "Mathematics and the picturing of data", Proceedings of the International Congress of Mathematicians, 2, 523-532, Canadian Mathematical Congress, Montreal.

P. Rousseeuw, I. Ruts and J. Tukey (1999) "The bagplot: A bivariate boxplot", The American Statistician, 53(4), 382-387.

R. J. Hyndman (1996) "Computing and graphing highest density regions", The American Statistician, 50(2), 120-126.

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.

Y. Sun and M. G. Genton (2011) "Functional boxplots", Journal of Computational and Graphical Statistics, 20(2), 316-334.

Y. Sun and M. G. Genton (2012) "Adjusted functional boxplots for spatio-temporal data visualization and outlier detection", Environmetrics, 23, 54-64.

Y. Sun and M. G. Genton (2012) "Functional median polish", Journal of Agricultural, Biological, and Environmental Statistics, 17, 354-376.

See Also

SVDplot

Examples

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fboxplot(data = ElNino_OISST_region_1and2, plot.type = "functional", 
	type = "bag", projmethod="PCAproj")
fboxplot(data = ElNino_OISST_region_1and2, plot.type = "bivariate", 
	type = "bag", projmethod="PCAproj")

Example output

Loading required package: MASS
Loading required package: pcaPP
Warning messages:
1: In fts(x, notchlow) : Please assign column name for the data matrix.
2: In fts(x, notchupper) : Please assign column name for the data matrix.
3: In fts(x, centercurve) : Please assign column name for the data matrix.

rainbow documentation built on May 2, 2019, 3:30 p.m.