circ.scatterplot {HDiR}

Circular scatterplot for plug-in HDRs

Description

This function produces a circular scatterplot with points coloured according to the HDRs in which they fall.

Usage

circ.scatterplot(sample,tau=c(0.25,0.5,.75),
bw=bw.CV(circular(sample),upper=100),tau.method="quantile",
plot.density=TRUE,col=NULL,shrink=NULL,cex=NULL,lty=NULL)

Arguments

sample

Numeric vector of angles in radians.

tau

Numeric vector of probabilities. According to Saavedra-Nieves and Crujeiras (2021), 1-tau represents the probability coverages required for HDRs.

bw

Smoothing parameter to be used. Following Oliveira et al. (2014), the value of the smoothing parameter can be chosen by using the functions bw.rt, bw.CV, bw.pi or bw.boot. It could be also selecting by considering circ.boot.bw, the new smoothing parameter proposed in Saavedra-Nieves and Crujeiras (2021). Default bw=bw.CV providing a cross-validation bandwidth.

tau.method

Character value selecting the rule to estimate the threshold of the HDR. This must be one of "quantile" or "trapezoidal". The first option estimates the threshold using the quantile method proposed in Hyndman(1996); the second one, using the trapezoidal rule for numerical integration. Default tau.method= "quantile".

plot.density

Logical string. If TRUE, the kernel density estimator is added to the scatterplot. Default plot.density=TRUE.

col

Vector containing the color numbers for plotting the scatterplot. If NULL, a default color palette is used.

shrink

Parameter that controls the size of the plotted circle. Default is 2. Larger values shrink the circle, while smaller values enlarge the circle.

cex

Point character size for representing the data on the scatterplot. Default is 0.5.

lty

A numeric vector indicating the line types to represent the thresholds of HDRs. Line type can be specified as an integer (0=blank, 1=solid (default), 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash). Default lty=2.

Details

A detailed definition of directional HDRs and of their plug-in estimators is given in Saavedra-Nieves and Crujeiras (2021).
Package NPCirc is used to estimate the circular density using the classical kernel density estimator. See Oliveira et al. (2014) for more details.
Moreover, the density quantile algorithm proposed in Hyndman (1996) or the trapezoidal rule can be used to compute the threshold of HDR.
The scatterplot is created colouring the sample points according to which HDR they fall.

Value

A scatterplot showing the points coloured according to which HDR they fall. Futhermore, a list where the number of components is equal to the number HDR estimated or, equivalently, to the length of tau vector. Each component contains the sample points in each HDR from the smallest value of tau to the biggest one.

Author(s)

Paula Saavedra-Nieves and Rosa M. Crujeiras.

References

Hyndman, R.J. (1996). Computing and graphing highest density regions, The American Statistician, 50, 120-126.
Oliveira, M., Crujeiras R.M. and Rodríguez-Casal, A. (2014). NPCirc: an R package for nonparametric circular methods. Journal of Statistical Software, 61(9), 1-26. https://www.jstatsoft.org/v61/i09/.
Saavedra-Nieves, P. and Crujeiras, R. M. (2021). Nonparametric estimation of directional highest density regions. Advances in Data Analysis and Classification, 1-36.

Examples

# Scatterplot for orientations of females for saltator specie
data(sandhoppers)
attach(sandhoppers)
saltatorF<-angle[(species=="salt")&(sex=="F")]
circ.scatterplot(saltatorF)
# Scatterplot for sample of size 100 of model 14 in NPCirc
library(NPCirc)
set.seed(1)
sample<- rcircmix(100, model=14)
circ.scatterplot(sample,tau=c(0.2,0.5,0.8))