circular (version 0.5-1)

modal.region: Modal regions

Description

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

Usage

modal.region(x, ...)
# S3 method for default
modal.region(x, ...)
# S3 method for 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), ...)

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.

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.

Author

Claudio Agostinelli

Details

Only the version for circular data is actually implemented.

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)