parboot {unmarked} | R Documentation

## 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.
`...` | 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 rbchan@uga.edu 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) {
        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)))

[Package unmarked version 0.12-3 Index]