parboot: Parametric bootstrap for `unmarked` models

Description

Simulate datasets from a fitted model, refit the model, and generate a sampling distribution for a user-specified fit-statistic.

Usage

# S4 method for unmarkedFit
parboot(object, statistic = SSE, nsim = 10, 
                                report, parallel = FALSE, ncores, ...)

Value

An object of class parboot with three slots:

call: parboot call
t0: Numeric vector of statistics for original fitted model.
t.star: nsim by length(t0) matrix of statistics for each simulation fit.

Arguments

object: a fitted model inheriting class "unmarkedFit"
statistic: a function returning a vector of fit-statistics. First argument must be the fitted model. Default is sum of squared residuals.
nsim: number of bootstrap replicates
report: Non-functional; will be removed soon.
parallel: logical (default = TRUE) indicating whether to compute bootstrap on multiple cores, if present. If TRUE, suppresses reporting of bootstrapped statistics. Defaults to serial calculation when nsim < 100. Parallel computation is likely to be slower for simple models when nsim < ~500, but should speed up the bootstrap of more complicated models.
ncores: integer (default = one less than number of available cores) number of cores to use when bootstrapping in parallel.
...: Additional arguments to be passed to statistic

Author

Richard Chandler rbchan@uga.edu and Adam Smith

Details

This function simulates datasets based upon a fitted model, refits the model, and evaluates a user-specified fit-statistic for each simulation. Comparing this sampling distribution to the observed statistic provides a means of evaluating goodness-of-fit or assessing uncertainty in a quantity of interest.

Examples

Run this code


data(linetran)
(dbreaksLine <- c(0, 5, 10, 15, 20))
lengths <- linetran$Length

ltUMF <- with(linetran, {
	unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
	siteCovs = data.frame(Length, area, habitat), dist.breaks = dbreaksLine,
	tlength = lengths*1000, survey = "line", unitsIn = "m")
    })

# Fit a model
(fm <- distsamp(~area ~habitat, ltUMF))

# Function returning three fit-statistics.
fitstats <- function(fm, na.rm=TRUE) {
    observed <- getY(fm@data)
    expected <- fitted(fm)
    resids <- residuals(fm)
    sse <- sum(resids^2, na.rm=na.rm)
    chisq <- sum((observed - expected)^2 / expected, na.rm=na.rm)
    freeTuke <- sum((sqrt(observed) - sqrt(expected))^2, na.rm=na.rm)
    out <- c(SSE=sse, Chisq=chisq, freemanTukey=freeTuke)
    return(out)
}

# \donttest{
(pb <- parboot(fm, fitstats, nsim=25, report=1))
plot(pb, main="")


# Finite-sample inference for a derived parameter.
# Population size in sampled area

Nhat <- function(fm) {
    sum(bup(ranef(fm, K=50)))
    }

set.seed(345)
(pb.N <- parboot(fm, Nhat, nsim=25, report=5))

# Compare to empirical Bayes confidence intervals
colSums(confint(ranef(fm, K=50)))
# }

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