mle.vonmises.bootstrap.ci: Bootstrap Confidence Intervals

Description

Generates simple bootstrap confidence intervals for the parameters of a von Mises distribtution: the mean direction mu, and the concentration parameter kappa.

Usage

mle.vonmises.bootstrap.ci(x, mu = NULL, bias = FALSE, alpha = 0.05, 
          reps = 1000, control.circular = list())
# S3 method for mle.vonmises.bootstrap.ci
print(x, …)

Arguments

vector of angular measurements as a circular object.

If NULL the value is estimated. This value is used in the bootstrap replications for kappa.

bias

logical, if TRUE, the replication estimates for kappa are computed with a bias corrected method. See mle.vonmises. Default is FALSE, i.e. no bias correction.

alpha

parameter determining level of confidence intervals. 1-alpha confidence intervals for mu and kappa are computed. By default, 95% confidence intervals are generated.

reps

number of resampled data sets to use. Default is 1000.

control.circular

the attribute of the resulting objects (mu, mu.ci).

…

arguments passed to print.default.

Value

A list is returned with the following components:

mu.ci

limits of the confidence interval for mu as a circular object.

kappa.ci

limits of the confidence interval for kappa.

estimate of mu as a circular object.

kappa

estimate of kappa.

Details

Percentile confidence intervals are computed by resampling from the original data set reps times. For each resampled data set, the MLE's of mu and kappa are computed. The bootstrap confidence intervals are the alpha/2 and 1-alpha/2 percentiles of the reps MLE's computed for each resampled data set.

Examples

Run this code

# NOT RUN {
x <- rvonmises(n=25, mu=circular(0), kappa=3)
x.bs <- mle.vonmises.bootstrap.ci(x, alpha=.10)
par(mfcol=c(1,2))
rose.diag(x.bs$mu, bins=30, main=expression(mu))
hist(x.bs$kappa, main=expression(kappa))
# }