Description Usage Arguments Details Value Methods (by class) Examples
A framework for arc length estimation.
1 2 3 4 5 6 7 8 9 10 11 12 | alE(x, q1, q2, dc, type, distribution = 1L)
alEfitdist(x, q1, q2, dc, type, bootstraps, distribution = 1L)
alEfit(X, q1, q2, dc, type, bootstraps, distribution = 1, ...)
## Default S3 method:
alEfit(X, q1, q2, dc, type, bootstraps, distribution = 1,
...)
## S3 method for class 'alEfit'
print(x, ...)
|
x |
An alEfit object. |
q1, q2 |
Vectors specifying the quantiles over which arc length segments are to be computed. |
dc |
TRUE/FALSE: Should the discrete or continuous sample statistic be used. |
type |
The type of bandwidth estimator for the underlying KDE; see |
distribution |
The distribution to be fitted, 1=normal (default), 2=generalised Pareto. |
bootstraps |
An integer specifying the size of the parametric bootstrap. |
X |
A vector of sample values. |
... |
Additional arguments passed to |
Estimate distributional parameters using the method of arc lengths.
Simulate bootstrap distributions for parameter estimates, resulting from sample arc length statistics.
This method is currently only implemented for the normal and generalised Pareto distributions. The underlying C code for the Nelder-Mead method of the optim function is used for optimising the objective function. The tolerance level is set at 1e-15, and a maximum number of 1000 iterations is allowed. The maximum likelihood estimates are used as initial values for the Nelder-Mead algorithm.
alE: A list with the following components (see optim
):
par: The estimated parameters.
abstol: The absolute tolerance level (default 1e-15).
fail: An integer code indicating convergence.
fncount: Number of function evaluations.
alEfitdist: A matrix of parameter estimates resulting from the estimated arc lengths over the specified interval(s), i.e. the bootstrap distribution for the estimated parameters resulting from the chosen sample arc length statistic.
alEfit: A generic S3 object with class alEfit.
alEfit.default: A list with all components from alE
, as well as :
dc: TRUE/FALSE Was the discrete or continuous sample arc length statistic used?
q1, q2: The segments over which the arc length(s) were calculated.
bw: The bandwidth for the kernel density estimator.
distribution: The distribution fitted to the data.
dist: A numeric matrix whose columns represent a bootstrap distribution for the corresponding parameter estimate.
se: A numeric vector with standard errors, obtained by a parametric bootstrap.
bootstraps: Number of bootstrap samples.
default
: default method for alEfit.
alEfit
: print method for alEfit.
1 2 3 4 5 6 7 8 9 10 11 12 13 | x <- rnorm(1000)
alE(x,0.025, 0.975, TRUE, -1)
alE(x,c(0.025, 0.5), c(0.5, 0.975), TRUE, -1)
alE(x,0.025, 0.975, FALSE, -1)
alE(x,c(0.025, 0.5), c(0.5, 0.975), FALSE, -1)
## Not run:
alEfitdist(x, 0.025, 0.975, TRUE, -1, 100)
alEfitdist(x, 0.025, 0.975, FALSE, -1, 100)
## End(Not run)
alEfit(x, q1=0.025, q2=0.975, dc=TRUE, type=-1, bootstraps=50)
alEfit(x, q1=0.025, q2=0.975, dc=FALSE, type=-1, bootstraps=50)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.