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

In ianfiske/unmarked: Models for Data from Unmarked Animals

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
...	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]

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 two fit-stats: sum of squared errors and population size at
# sampled plots.
fitStats <- function(fit) {
    sse <- SSE(fit)
    plot.area.ha <- lengths*1000 * 40 / 10000
    N <- sum(predict(fit, type="state")$Predicted*plot.area.ha, na.rm=TRUE)
    return(c(sse, N.hat=N))
    }

(pb <- parboot(fm, fitStats, nsim=25))
plot(pb, main="")

ianfiske/unmarked documentation built on May 18, 2019, 1:28 a.m.