Parametric bootstrap method for fitted models inheriting class.
Simulate datasets from a fitted model, refit the model, and generate a sampling distribution for a user-specified fit-statistic.
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 = |
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 |
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.
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. |
Richard Chandler rbchan@uga.edu and Adam Smith
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) {
observed <- getY(fm@data)
expected <- fitted(fm)
resids <- residuals(fm)
sse <- sum(resids^2)
chisq <- sum((observed - expected)^2 / expected)
freeTuke <- sum((sqrt(observed) - sqrt(expected))^2)
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)))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.