Bootstrap simulation of destruction rate models
Description
Uses parametric or nonparametric bootstrap resampling in order to simulate uncertainty
in the parameters of the distribution fitted to destruction rate data.
Generic methods are print
, plot
, summary
.
Usage
1 
Arguments
f 
An object of class 
bootmethod 
A character string coding for the type of resampling : 
niter 
The number of samples drawn by bootstrap. 
silent 
A logical to remove or show warnings and errors when bootstraping. 
Details
Samples are drawn by parametric bootstrap (resampling from the distribution fitted by
fitDR
) or nonparametric bootstrap (resampling with replacement from the
data set). On each bootstrap sample the estimaiton process is
used to estimate bootstrapped values of parameters. When that function fails
to converge, NA
values are returned. Medians and 2.5 and 97.5 percentiles are
computed by removing NA
values.
This method returns an object of class "bootDR"
inheriting from the
"bootdist"
class. Therefore the following generic methods are defined:
print
, plot
, summary
.
Value
bootDR
returns an object of class "bootDR"
inheriting from the "bootdist"
class.
That is a list with 6 components,
estim 
a data frame containing the bootstrapped values of parameters. 
converg 
a vector containing the codes for convergence obtained if an iterative method is used to estimate parameters on each bootstraped data set (and 0 if a closed formula is used). 
method 
A character string coding for the type of resampling : 
nbboot 
The number of samples drawn by bootstrap. 
CI 
bootstrap medians and 95 percent confidence percentile intervals of parameters. 
fitpart 
The object of class 
Generic functions:
print

The print of a
"bootDR"
object shows the bootstrap parameter estimates. If inferior to the whole number of bootstrap iterations, the number of iterations for which the estimation converges is also printed. summary

The summary provides the median and 2.5 and 97.5 percentiles of each parameter. If inferior to the whole number of bootstrap iterations, the number of iterations for which the estimation converges is also printed in the summary.
plot

The plot shows the bootstrap estimates with
stripchart
function for univariate parameters andplot
function for multivariate parameters.
Author(s)
Christophe Dutang
References
Cullen AC and Frey HC (1999), Probabilistic techniques in exposure assessment. Plenum Press, USA, pp. 181241.
DelignetteMuller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 134.
See Also
See mledist
, mmedist
,
qmedist
, mgedist
for details on parameter estimation.
See bootdist
for details on generic function.
See fitDR
for estimation procedures.
Examples
1 2 3  # We choose a low number of bootstrap replicates in order to satisfy CRAN running times
# constraint.
# For practical applications, we recommend to use at least niter=501 or niter=1001.

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