mle.wrappednormal: Wrapped Normal Maximum Likelihood Estimates

Description

Computes the maximum likelihood estimates for the parameters of a Wrapped Normal distribution: mean and concentration parameter.

Usage

mle.wrappednormal(x, mu, rho, sd, K, tol = 1e-05, min.sd = 0.001, min.k = 10, 
    max.iter = 100, verbose = FALSE)
## S3 method for class 'mle.wrappednormal':
print(x,
        digits = max(3, getOption("digits") - 3), ...)

Arguments

a vector. The object is coerced to class circular.

if missing the maximum likelihood estimate of the mean direction is calculated.

rho

if missing the maximum likelihood estimate of the concentration parameter is calculated.

standard deviation of the (unwrapped) normal. Used as an alternative parametrization.

number of terms to be used in approximating the density.

tol

precision of the estimation.

min.sd

minimum value should be reached by the search procedure for the standard deviation parameter.

min.k

minimum number of terms used in approximating the density.

max.iter

maximum number of iterations.

verbose

logical, if TRUE information on the convergence process are printed.

digits

integer indicating the precision to be used.

...

further arguments passed to or from other methods.

Value

Returns a list with the following components:
callthe match.call().
muthe estimate of the mean direction or the value supplied.
rhothe estimate of the concentration parameter or the value supplied
sdthe estimate of the standard deviation or the value supplied.
est.muTRUE if the estimator is reported.
est.rhoTRUE if the estimator is reported.
convergenceTRUE if the convergence is achieved.

References

Jammalamadaka, S. Rao and SenGupta, A. (2001). Topics in Circular Statistics, Section 4.2.1, World Scientific Press, Singapore.

Examples

Run this code

x <- rwrappednormal(n=50, mu=0, rho=0.5)
mle.wrappednormal(x) # estimation of mu and rho (and sd)
mle.wrappednormal(x, mu=0) # estimation of rho (and sd) only