unmarked (version 1.5.0)

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 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.

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)
}

# \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)))
# }