wle.vonmises {wle}R Documentation

von Mises Weighted Likelihood Estimates

Description

Computes the weighted likelihood estimates for the parameters of a von Mises distribution: the mean direction and the concentration parameter.

Usage

wle.vonmises(x, boot = 30, group, num.sol = 1, raf = "HD", smooth, tol =
10^(-6), equal = 10^(-3), max.iter = 500, bias = FALSE, mle.bias =
FALSE, max.kappa = 500, min.kappa = 0.01, use.smooth = TRUE, alpha =
NULL, p = 2, verbose = FALSE, control.circular = list())
## S3 method for class 'wle.vonmises':
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x a vector. The object is coerced to class circular.
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).
max.iter maximum number of iterations.
bias logical, if TRUE, the estimate for kappa is computed with a bias corrected method. Default is FALSE, i.e. no bias correction.
mle.bias logical, if TRUE a bias corrected method is used to estimate the concentration parameter for the initial values.
max.kappa maximum value for the concentration parameter.
min.kappa minimum value for the concentration parameter.
use.smooth logical, if TRUE a smoothed model is used, default is TRUE.
alpha 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 provides Hellinger Distance RAF, p=-1 provides Kullback-Leibler RAF and p=Inf provides Neyman's Chi-Square RAF.
verbose logical, if TRUE warnings are printed.
control.circular the attribute of the resulting object (mu)
digits integer indicating the precision to be used.
... further parameters in print.wle.vonmises.

Details

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

Value

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.
kappa the estimate of the concentration parameter or the value supplied. If num.sol > 1 then kappa 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.

Author(s)

Claudio Agostinelli

References

C. Agostinelli (2006) Robust Estimation for Circular Data, under revision.

See Also

circular, mle.vonmises.

Examples


if (require(circular)) {
    x <- c(rvonmises(n=50, mu=circular(0), kappa=10), rvonmises(n=5, mu=circular(pi/2), kappa=20))
    wle.vonmises(x, smooth=20, group=5)
} else {
   cat("Please, install the package 'circular' in order to use this function.\n")
}


[Package wle version 0.9-3 Index]