modal.region: Modal regions

In circular: Circular Statistics

Modal regions

Description

Evaluate the modal regions for a data set. Only the version for circular data is implemented.

Usage

modal.region(x, ...)
## Default S3 method:
modal.region(x, ...)
## S3 method for class 'circular'
modal.region(x, z=NULL, q=0.95, bw, adjust = 1,
  type = c("K", "L"), kernel = c("vonmises", "wrappednormal"),
  na.rm = FALSE, step=0.01, eps.lower=10^(-4), eps.upper=10^(-4), ...)

Arguments

x	numeric or an object of class circular.
z	numeric or object of class circular. The grid were the kernel density estimate will be evaluated. If NULL equally spaced points in the interval [0,2*pi) with step step.
q	numeric in the interval [0,1]. The quantile of the modal region.
bw	the smoothing bandwidth to be used. When the kernel is vonmises the bandwidth is equal to the concentration parameter.
adjust	the bandwidth used is actually adjust*bw. This makes it easy to specify values like “half the default bandwidth”.
type	Not Yet Used.
kernel	a character string giving the smoothing kernel to be used. This must be one of "vonmises" or "wrappednormal", that are kernels of type "K".
na.rm	logical; if TRUE, missing values are removed from x. If FALSE any missing values cause an error.
step	numeric. Used in the construction of the regular grid z.
eps.lower, eps.upper	the cut point in the density is searched in the interval [min(density)*(1+eps.lower),max(density)*(1-eps.upper)].
...	further arguments passed to the next methods.

Details

Only the version for circular data is actually implemented.

Value

A list of class modal.region.circular with the following elements

zeros	extremes of modal regions, possible as a matrix
areas	a list with two components: tot with the total (area under the density) probability, which should approximately equal to q and areas with the probability of each modal region.
density	the object from function density.circular.
q	the modal region order as in input.
level	the cut point at the density scale.

Author(s)

Claudio Agostinelli

References

L.G.R. Oliveira-Santos, C.A. Zucco and C. Agostinelli (2013) Using conditional circular kernel density functions to test hypotheses on animal circadian activity. Animal Behaviour, 85(1) 269-280.

See Also

totalvariation.circular

Examples

x <- rvonmises(100, circular(pi), 10)
res <- modal.region(x, bw=50)
plot(res)

circular documentation built on Sept. 11, 2024, 8:21 p.m.