wle.wrappednormal: Wrapped Normal Weighted Likelihood Estimates
In wle: Weighted Likelihood Estimation

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

Computes the weighted likelihood estimates for the parameters of a Wrapped Normal distribution: the mean direction and the concentration parameter (and the scale parameter).

wle.wrappednormal(x, mu, rho, sd, K, boot = 30, group, num.sol = 1, raf = "HD",
    smooth = 0.0031, tol = 10^(-6), equal = 10^(-3), min.sd = 0.001,
    min.k = 10, max.iter = 100, use.smooth = TRUE, alpha=NULL, p = 2,
verbose = FALSE, control.circular=list())

## S3 method for class 'wle.wrappednormal'
print(x, digits = max(3, getOption("digits") - 3), ...)

`x`	a vector. The object is coerced to class `circular`.
`mu`	if a values if provided the parameter is considered known.
`rho`	if a values if provided the parameter (and `sd`) is considered known.
`sd`	if a values if provided the parameter (and `rho`) is considered known.
`K`	number of elements used to approximate the density of the wrapped normal.
`boot`	the number of starting points based on boostrap subsamples to use in the search of the roots.
`group`	the dimension of the bootstap subsamples.
`num.sol`	maximum number of roots to be searched.
`raf`	type of Residual adjustment function to be use: `raf="HD"`: Hellinger Distance RAF, `raf="NED"`: Negative Exponential Disparity RAF, `raf="SCHI2"`: Symmetric Chi-Squared Disparity RAF.
`smooth`	the value of the smoothing parameter.
`tol`	the absolute accuracy to be used to achieve convergence of the algorithm.
`equal`	the absolute value for which two roots are considered the same. (This parameter must be greater than `tol`).
`min.sd`	minimum value for the `sd` parameter.
`min.k`	minimum number of elements used to approximate the density of the wrapped normal.
`max.iter`	maximum number of iterations.
`use.smooth`	logical, if `TRUE` a smoothed model is used, default is `TRUE`.
`alpha`	if not `NULL` overrides the value of `p`. See the next argument `p`. This is a different parameterization, `alpha=-1/2` provides Hellinger Distance RAF, `alpha=-1` provides Kullback-Leibler RAF and `alpha=-2` provides Neyman's Chi-Square RAF.
`p`	this parameter works only when `raf="HD"`. `p=2` provide Hellinger Distance RAF, `p=-1` provide Kullback-Leibler RAF and `p=Inf` provide Neyman's Chi-Square RAF.
`verbose`	logical, if `TRUE` warnings are printed.
`control.circular`	the attribute of the resulting objects (`mu`)
`digits`	integer indicating the precision to be used.
`...`	further parameters in `print.wle.vonmises`.

Parameters p and raf will be change in the future. See the reference below for the definition of all the RAF.

Returns a list with the following components:

`call`	the match.call().
`mu`	the estimate of the mean direction or the value supplied. If `num.sol` > 1 then `mu` may have length greater than 1, i.e, one value for each root found.
`rho`	the estimate of the concentration parameter or the value supplied. If `num.sol` > 1 then `rho` may have length greater than 1, i.e, one value for each root found.
`sd`	the estimate of the standard deviation parameter or the value supplied. If `num.sol` > 1 then `sd` may have length greater than 1, i.e, one value for each root found.
`tot.weights`	the sum of the weights divide by the number of observations, one value for each root found.
`weights`	the weights associated to each observation, one column vector for each root found.
`f.density`	the non-parametric density estimation.
`m.density`	the smoothed model.
`delta`	the Pearson residuals.
`tot.sol`	the number of solutions found.
`not.conv`	the number of starting points that does not converge after the `max.iter` iteration are reached.