Description Usage Arguments Details Value Author(s) References See Also Examples
Computes the weighted likelihood estimates for the parameters of a von Mises distribution: the mean direction and the concentration parameter.
1 2 3 4 5 6 | 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), ...)
|
x |
a vector. The object is coerced to class |
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:
|
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 |
max.iter |
maximum number of iterations. |
bias |
logical, if |
mle.bias |
logical, if |
max.kappa |
maximum value for the concentration parameter. |
min.kappa |
minimum value for the concentration parameter. |
use.smooth |
logical, if |
alpha |
if not |
p |
this parameter works only when |
verbose |
logical, if |
control.circular |
the attribute of the resulting object ( |
digits |
integer indicating the precision to be used. |
... |
further parameters in |
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
|
kappa |
the estimate of the concentration parameter or the
value supplied. If |
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 |
Claudio Agostinelli
C. Agostinelli. Robust estimation for circular data. Computational Statistics & Data Analysis, 51(12):5867-5875, 2007.
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