R: Bootstrap Confidence Intervals

mle.vonmises.bootstrap.ci {circular} | R Documentation

## 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 class 'mle.vonmises.bootstrap.ci'
    print(x, ...)

### Arguments

`x` | vector of angular measurements as a `circular` object.
`mu` | 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`.

### 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.

### 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.
`mu` | estimate of mu as a `circular` object.
`kappa` | estimate of kappa.

### Author(s)

Claudio Agostinelli and Ulric Lund

### See Also

`mle.vonmises`

### Examples

    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))

[Package circular version 0.4-93 Index]