| mle.wrappednormal {circular} | R Documentation |
Computes the maximum likelihood estimates for the parameters of a Wrapped Normal distribution: mean and concentration parameter.
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 class 'mle.wrappednormal':
print(x, digits = max(3, getOption("digits") - 3), ...)
x |
a vector. The object is coerced to class
circular. |
mu |
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. |
sd |
standard deviation of the (unwrapped) normal. Used as an alternative parametrization. |
K |
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. |
Returns a list with the following components:
call |
the match.call(). |
mu |
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 |
sd |
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. |
Claudio Agostinelli
Jammalamadaka, S. Rao and SenGupta, A. (2001). Topics in Circular Statistics, Section 4.2.1, World Scientific Press, Singapore.
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