intersect.modal.region: Intersection between model region and a given interval.
In circular: Circular Statistics

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

Find an estimates of the probability of the intersection between a modal region and a given interval.

intersect.modal.region(x, ...)
## Default S3 method:
intersect.modal.region(x, ...)
## S3 method for class 'circular'
intersect.modal.region(x, breaks, 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), ...)

`x`	numeric or an object of class `circular`.
`breaks`	a matrix with two columns. Each row specifies a sub-interval.
`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.

Only the version for circular data is actually implemented.

For the circular method a list with the following three components

`tot`	the total area.
`areas`	information for each subinterval.
`breaks`	the extremes of each subinterval.

Claudio Agostinelli

modal.region

  x <- rvonmises(100, circular(pi), 10)  
  res <- intersect.modal.region(x, breaks=circular(matrix(c(pi,pi+pi/12, pi-pi/12, pi), ncol=2, byrow=TRUE)), bw=50)
  res$tot

  x <- rvonmises(100, circular(0), 10)
  res <- intersect.modal.region(x, breaks=circular(matrix(c(pi,pi+pi/12), ncol=2)), bw=50)
  res$tot
  
  res <- intersect.modal.region(x, breaks=circular(matrix(c(pi/12, 2*pi-pi/12), ncol=2, byrow=TRUE)), bw=50)
  res$tot