fitdistrplus (version 1.0-8)

bootdistcens: Bootstrap simulation of uncertainty for censored data

Description

Uses nonparametric bootstrap resampling in order to simulate uncertainty in the parameters of the distribution fitted to censored data.

Usage

bootdistcens(f, niter = 1001, silent = TRUE,  parallel = c("no", "snow", "multicore"), ncpus)
"print"(x, ...)
"plot"(x, ...)
"summary"(object, ...)

Arguments

f

An object of class "fitdistcens", output of the fitdistcens function.

niter

The number of samples drawn by bootstrap.

silent

A logical to remove or show warnings and errors when bootstraping.

parallel

The type of parallel operation to be used, "snow" or "multicore" (the second one not being available on Windows), or "no" if no parallel operation.

ncpus

Number of processes to be used in parallel operation : typically one would fix it to the number of available CPUs.

x

An object of class "bootdistcens".

object

An object of class "bootdistcens".

...

Further arguments to be passed to generic methods.

Value

bootdistcens returns an object of class "bootdistcens", a list with 6 components,Generic functions:

Details

Samples are drawn by nonparametric bootstrap (resampling with replacement from the data set). On each bootstrap sample the function mledist is used to estimate bootstrapped values of parameters. When mledist fails to converge, NA values are returned. Medians and 2.5 and 97.5 percentiles are computed by removing NA values. The medians and the 95 percent confidence intervals of parameters (2.5 and 97.5 percentiles) are printed in the summary. If inferior to the whole number of iterations, the number of iterations for which mledist converges is also printed in the summary. The plot of an object of class "bootdistcens" consists in a scatterplot or a matrix of scatterplots of the bootstrapped values of parameters. It uses the function stripchart when the fitted distribution is characterized by only one parameter, and the function plot in other cases. In these last cases, it provides a representation of the joint uncertainty distribution of the fitted parameters. It is possible to accelerate the bootstrap using parallelization. We recommend you to use parallel = "multicore", or parallel = "snow" if you work on Windows, and to fix ncpus to the number of available processors.

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 fitdistrplus for an overview of the package. fitdistcens, mledist, quantile.bootdistcens for another generic function to calculate quantiles from the fitted distribution and its bootstrap results and CIcdfplot for adding confidence intervals on quantiles to a CDF plot of the fitted distribution.

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.

# (1) Fit of a normal distribution to fluazinam data in log10
# followed by nonparametric bootstrap and calculation of quantiles
# with 95 percent confidence intervals
#
data(fluazinam)
(d1 <-log10(fluazinam))
f1 <- fitdistcens(d1, "norm")
b1 <- bootdistcens(f1, niter = 101)
b1
summary(b1)
plot(b1)
quantile(b1)
CIcdfplot(b1, CI.output = "quantile")

# (2) Estimation of the mean of the normal distribution 
# by maximum likelihood with the standard deviation fixed at 1 
# using the argument fix.arg
# followed by nonparametric bootstrap 
# and calculation of quantiles with 95 percent confidence intervals
#
f1b <- fitdistcens(d1, "norm", start = list(mean = 1),fix.arg = list(sd = 1))
b1b <- bootdistcens(f1b, niter = 101)
summary(b1b)
plot(b1b)
quantile(b1b)

# (3) comparison of sequential and parallel versions of bootstrap
# to be tried with a greater number of iterations (1001 or more)
#
## Not run: 
# niter <- 1001
# data(fluazinam)
# d1 <-log10(fluazinam)
# f1 <- fitdistcens(d1, "norm")
# 
# # sequential version
# ptm <- proc.time()
# summary(bootdistcens(f1, niter = niter))
# proc.time() - ptm
# 
# # parallel version using snow
# require(parallel)
# ptm <- proc.time()
# summary(bootdistcens(f1, niter = niter, parallel = "snow", ncpus = 4))
# proc.time() - ptm
# 
# # parallel version using multicore (not available on Windows)
# ptm <- proc.time()
# summary(bootdistcens(f1, niter = niter, parallel = "multicore", ncpus = 4))
# proc.time() - ptm
# ## End(Not run)