Description Usage Arguments Details Value Author(s) References See Also Examples
Uses parametric or nonparametric bootstrap resampling in order to simulate uncertainty in the parameters of the distribution fitted to noncensored data.
1 2 3 4 5 6 7 8 9  bootdist(f, bootmethod = "param", niter = 1001, silent = TRUE,
parallel = c("no", "snow", "multicore"), ncpus)
## S3 method for class 'bootdist'
print(x, ...)
## S3 method for class 'bootdist'
plot(x, main = "Bootstrapped values of parameters", enhance = FALSE,
trueval = NULL, rampcol = NULL, nbgrid = 100, nbcol = 100, ...)
## S3 method for class 'bootdist'
summary(object, ...)

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. 
parallel 
The type of parallel operation to be used, 
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 
object 
An object of class 
main 
an overall title for the plot: see 
enhance 
a logical to get an enhanced plot. 
trueval 
when relevant, a numeric vector with the true value of parameters (for backfitting purposes). 
rampcol 
colors to interpolate; must be a valid argument to

nbgrid 
Number of grid points in each direction. Can be scalar or a length2 integer vector. 
nbcol 
an integer argument, the required number of colors 
... 
Further arguments to be passed to generic methods 
Samples are drawn by parametric bootstrap (resampling from the distribution fitted by
fitdist
) or nonparametric bootstrap (resampling with replacement from the
data set). On each bootstrap sample the function
mledist
(or mmedist
, qmedist
, mgedist
according to the component f$method
of the object of class "fitdist"
) 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.
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
the function converges is also printed in the summary.
By default (when enhance=FALSE
), the plot of an object of class
"bootdist"
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, the function plot
when there
are two paramters and the function pairs
in other cases.
In these last cases, it provides a representation of the joint uncertainty distribution
of the fitted parameters.
When enhance=TRUE
, a personalized plot version of pairs
is used where
upper graphs are scatterplots and lower graphs are heatmap image using image
based on a kernel based estimator for the 2D density function (using kde2d
from
MASS package).
Arguments rampcol
, nbgrid
, nbcol
can be used to customize the plots.
Defautls values are rampcol=c("green", "yellow", "orange", "red")
, nbcol=100
(see colorRampPalette()
), nbgrid=100
(see kde2d
).
In addition, when fitting parameters on simulated datasets for backtesting purposes, an
additional argument trueval
can be used to plot a cross at the true value.
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.
bootdist
returns an object of class "bootdist"
, 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 "bootdist"
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.
MarieLaure DelignetteMuller and Christophe Dutang.
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 fitdistrplus
for an overview of the package.
fitdist
, mledist
, qmedist
, mmedist
,
mgedist
,
quantile.bootdist
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68  # 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 gamma distribution to serving size data
# using default method (maximum likelihood estimation)
# followed by parametric bootstrap
#
data(groundbeef)
x1 < groundbeef$serving
f1 < fitdist(x1, "gamma")
b1 < bootdist(f1, niter=51)
print(b1)
plot(b1)
plot(b1, enhance=TRUE)
summary(b1)
quantile(b1)
CIcdfplot(b1, CI.output = "quantile")
# (2) non parametric bootstrap on the same fit
#
b1b < bootdist(f1, bootmethod="nonparam", niter=51)
summary(b1b)
quantile(b1b)
# (3) Fit of a normal distribution on acute toxicity values of endosulfan in log10 for
# nonarthropod invertebrates, using maximum likelihood estimation
# to estimate what is called a species sensitivity distribution
# (SSD) in ecotoxicology, followed by estimation of the 5 percent quantile value of
# the fitted distribution, what is called the 5 percent hazardous concentration (HC5)
# in ecotoxicology, with its twosided 95 percent confidence interval calculated by
# parametric bootstrap
#
data(endosulfan)
ATV < subset(endosulfan, group == "NonArthroInvert")$ATV
log10ATV < log10(subset(endosulfan, group == "NonArthroInvert")$ATV)
fln < fitdist(log10ATV, "norm")
bln < bootdist(fln, bootmethod = "param", niter=51)
quantile(bln, probs = c(0.05, 0.1, 0.2))
# (4) 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(groundbeef)
x1 < groundbeef$serving
f1 < fitdist(x1, "gamma")
# sequential version
ptm < proc.time()
summary(bootdist(f1, niter = niter))
proc.time()  ptm
# parallel version using snow
require(parallel)
ptm < proc.time()
summary(bootdist(f1, niter = niter, parallel = "snow", ncpus = 4))
proc.time()  ptm
# parallel version using multicore (not available on Windows)
ptm < proc.time()
summary(bootdist(f1, niter = niter, parallel = "multicore", ncpus = 4))
proc.time()  ptm
## End(Not run)

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