Description Arguments Details Value Author(s) Examples
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 |
... |
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 rchandler@nrc.umass.edu
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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="")
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