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 = NULL, rho = NULL, sd = NULL, K = NULL, 
  tol = 1e-05, min.sd = 1e-3, min.k = 10, max.iter = 100, 
  verbose = FALSE, control.circular=list())
# S3 method for mle.wrappednormal
print(x, digits = max(3, getOption("digits") - 3), …)

Arguments

a vector. The object is coerced to class circular.

if NULL the maximum likelihood estimate of the mean direction is calculated, otherwise the value is coerced to an object of class circular.

rho

if NULL 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.

control.circular

the attribute of the resulting objects (mu)

digits

integer indicating the precision to be used.

…

further arguments passed to or from other methods.

Value

Returns a list with the following components:

call

the match.call result.

the estimate of the mean direction or the value supplied as an object of class circular.

rho

the estimate of the concentration parameter or the value supplied

the estimate of the standard deviation or the value supplied.

est.mu

TRUE if the estimator is reported.

est.rho

TRUE if the estimator is reported.

convergence

TRUE 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

# NOT RUN {
x <- rwrappednormal(n=50, mu=circular(0), rho=0.5)
mle.wrappednormal(x) # estimation of mu and rho (and sd)
mle.wrappednormal(x, mu=circular(0)) # estimation of rho (and sd) only
# }