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())
"print"(x, digits = max(3, getOption("digits") - 3), ...)circular.raf="HD": Hellinger Distance RAF,
raf="NED": Negative Exponential Disparity RAF,
raf="SCHI2": Symmetric Chi-Squared Disparity RAF.
tol).TRUE, the estimate for kappa is
computed with a bias corrected method. Default is FALSE,
i.e. no bias correction. TRUE a bias corrected method is
used to estimate the concentration parameter for the initial values.TRUE a smoothed model is used,
default is TRUE.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.raf="HD". p=2
provides Hellinger Distance RAF, p=-1
provides Kullback-Leibler RAF and p=Inf provides Neyman's
Chi-Square RAF.TRUE warnings are printed.mu)print.wle.vonmises.num.sol > 1 then mu may have length greater than 1, i.e, one value for each root found.num.sol > 1 then kappa may have length greater than 1, i.e, one value for each root found.max.iter iteration are reached.p and raf will be change in the future. See
the reference below for the definition of all the RAF.
circular, mle.vonmises.
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)
Run the code above in your browser using DataLab