bootDR: Bootstrap simulation of destruction rate models

In spedygiorgio/mbbefd: Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and Destruction Rate Modelling

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

bootDR(f, bootmethod="param", niter=1001, silent=TRUE)

Arguments

f	An object of class "fitDR", output of the fitDR function.
bootmethod	A character string coding for the type of resampling : "param" for a parametric resampling and "nonparam" for a nonparametric resampling of data.
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 estimation 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 : "param" for a parametric resampling and "nonparam" for a nonparametric 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 "fitDR" on which the bootstrap procedure was applied.

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 and plot function for multivariate parameters.

Author(s)

Christophe Dutang

References

Cullen AC and Frey HC (1999), Probabilistic techniques in exposure assessment. Plenum Press, USA, pp. 181-241.

Delignette-Muller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1-34.

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

# 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.

spedygiorgio/mbbefd documentation built on Dec. 14, 2024, 7:25 a.m.