parboot: Parametric bootstrap method for fitted models inheriting...

In unmarked: Models for Data from Unmarked Animals

Parametric bootstrap method for fitted models inheriting class.

Description

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

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	print fit statistic every 'report' iterations during resampling
seed	set seed for reproducible bootstrap
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

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.

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.

Author(s)

Richard Chandler [e-mail] and Adam Smith

See Also

ranef

Examples

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)
}

(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)))

unmarked documentation built on Sept. 11, 2024, 8:28 p.m.