Description Usage Arguments Details Value Bootstrapping for acoustic surveys Monte Carlo error References Examples
Carries out a parametric bootstrap, based on a model fitted using admbsecr.
1 2 | boot.admbsecr(fit, N, prog = TRUE, n.cores = 1, M = 10000,
infotypes = NULL)
|
fit |
A fitted |
N |
The number of bootstrap resamples. |
prog |
Logical, if |
n.cores |
A positive integer representing the number of cores to use for parallel processing. |
M |
The number of bootstrap resamples for the secondary bootstrap used to calculate Monte Carlo error. See 'Details' below. If M = 0, then this is skipped. |
infotypes |
A list, where each component contains information types for subsequent bootstrap procedures. See 'Details'. |
For each bootstrap resample, a new population of individuals is
simulated within the mask area. Detections of these individuals are
simulated using the estimated detection function. For detected
individuals, additional informatin is simulated from the estimated
distribution of measurement error. The original model is then
re-fitted to these simulated data, and parameter estimates for each
iteration saved in the component boot
of the returned list.
Note that generic functions stdEr and vcov with an
object of class admbsecr.boot
as the main argument will
return standard errors and the variance-covariance matrix for
estimated parameters based on the bootstrap procedure (via
the stdEr.admbsecr.boot and vcov.admbsecr.boot
methods). For standard errors and the variance-covariance matrix
based on maximum likelihood asymptotic theory, the methods
stdEr.admbsecr and vcov.admbsecr must be called
directly.
If infotypes
is provided it should take the form of a list,
where each component is a subset of information types (i.e.,
fit$infotypes
) used to fit the original model. A NULL
component is associated with no additional information. The
bootstrap procedure is then repeated for each component, only
utilising the appropriate additional information. In practice this
is only useful if the user is looking to investigate the benefits
of including particular information types. The results from these
extra bootstrap procedures can be found in the
boot$extra.boots
component of the returned object.
the bootstrap procedure will be
repeated for each subset; usually this is only useful to
investigate the impact on the density estimator associated with
various combinations of information types. A NULL
component
will bootstrap without any additional information types.
A list of class "admbsecr.boot"
. Components contain
information such as estimated parameters and standard errors. The
best way to access such information, however, is through the
variety of helper functions provided by the admbsecr package. S3
methods stdEr.admbsecr.boot and vcov.admbsecr.boot
can be used to return standard errors and the variance-covariance
matrix of estimated parameters based on the bootstrap procedure.
For fits based on acoustic surveys where the argument
call.freqs
is provided to the admbsecr
function, the
simulated data allocates multiple calls to the same location based
on an estimated distribution of the call frequencies. Using a
parametric bootstrap is currently the only way parameter
uncertainty can be estimated for such models (see Stevenson et al.,
in prep., for details).
There will be some error in esimates based on the parametric bootstrap (e.g., standard errors and estimates of bias) because the number of bootstrap simulations is not infinite. By default, this function calculates Monte Carlo error using a bootstrap of the estimates obtained from the initial bootstrap procedure; see Equation (9) in Koehler, Brown and Haneuse (2009). Note that this secondary bootstrap does not require the fitting of any further models, and so the increased processing time due to this procedure is negligible.
Monte Carlo error for standard errors and estimates of bias can be extracted using the function get.mce.
Koehler, E., Brown, E., and Haneuse, S. J.-P. A. (2009) On the assessment of Monte Carlo error in sumulation-based statistical analyses. The American Statistician, 63: 155–162.
Stevenson, B. C., Borchers, D. L., Altwegg, R., Measey, G. J., Swift, R. J., and Gillespie, D. M. (in prep.) A general framework for animal density estimation from acoustic detection data.
1 2 3 4 5 | ## Not run:
## In practice, N should be >> 100, but this leads to long computation time for a simple example.
boot.fit <- boot.admbsecr(fit = example$fits$simple.hn, N = 100)
## End(Not run)
|
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