PBoot: Parametric bootstrap

In benRenard/disTRIMbution: Estimation of Trimmed (Either Truncated or Rectified) Distributions

Parametric bootstrap

Description

Applies parametric bootstrap to an estimated distribution

Usage

PBoot(
  d,
  n,
  nsim = 1000,
  trim = c(-Inf, Inf),
  doTrunc = FALSE,
  par.low = rep(-Inf, nPar(d)),
  par.high = rep(Inf, nPar(d)),
  method = "Nelder-Mead",
  control = list(fnscale = -1, maxit = 10000)
)

Arguments

d	distributions3 object, estimated distribution. Typically, the output of estim_ML.
n	Integer, size of each resampled dataset. Typically length(y), where y are calibration data.
nsim	Integer, number of resampled datasets.
trim	Numeric vector of size 2, trimming bounds.
doTrunc	Logical, do truncation? default FALSE, i.e. rectification is used.
par.low	Numeric vector of size nPar(d), lower bounds for parameters.
par.high	Numeric vector of size nPar(d), higher bounds for parameters.
method	Character string, optimization method, see ?optim.
control	List, controls for optimization algorithm, see ?optim.

Value

a matrix of size nsim * nPar(d) containing the estimated parameters

Examples

# EXAMPLE 1
# Define Normal distribution
norm <- Normal(mu=1,sigma=2)
# generate data and reset all negative values to zero
y <- random(norm,200); y[y<0] <- 0
# Estimate rectified Gaussian distribution.
fit <- estim_ML(d=norm,y=y,trim=c(0,Inf),par.low=c(-Inf,0))
# Do parametric bootstrap.
boot <- PBoot(fit,length(y),trim=c(0,Inf),par.low=c(-Inf,0),nsim=100)
plot(boot)

# EXAMPLE 2
# Define Normal distribution
norm <- Normal(mu=0.75,sigma=0.25)
# generate data and remove all values outside [0;1]
z <- random(norm,200); y <- z[z>0 & z<1]
# Estimate truncated Gaussian distribution.
fit <- estim_ML(d=norm,y=y,trim=c(0,1),doTrunc=TRUE,par.low=c(-Inf,0))
# Do parametric bootstrap.
boot <- PBoot(fit,length(y),trim=c(0,1),doTrunc=TRUE,par.low=c(-Inf,0),nsim=100)
plot(boot)

benRenard/disTRIMbution documentation built on July 1, 2023, 4:24 a.m.