countCDFxt: Count-based extinction probabilities and bootstrap confidence...
In popbio: Construction and Analysis of Matrix Population Models

Description Usage Arguments Details Value Author(s) References See Also Examples

This function takes parameters derived from population counts and calculates the probability of extinction with bootstrap confidence intervals for a density-independent model, using a diffusion approximation.

1 2	countCDFxt(mu, sig2, nt, Nc, Ne, tq = nt, tmax = 50, Nboot = 500, plot = TRUE)

`mu`	estimated value of mean mu
`sig2`	estimated value of sample variance
`nt`	number of transitions in the data set
`Nc`	current population size
`Ne`	quasi-extinction threshold
`tq`	length of the census (in years), default is number of transitions
`tmax`	latest time to calculate extinction probability, default 50
`Nboot`	number of bootstrap samples for calculating confidence intervals for extinction probabilities, default 500)
`plot`	draw extinction time CDF plot with log-scale on y-axis

converted Matlab code from Box 3.4 in Morris and Doak (2002)

The function plots the cumulative probabilities of quasi-extinction through time with 95% confidence intervals. It also returns a data frame with the extinction time CDF for the best parameter estimates (Gbest), and the lower and upper bootstrap confidence limits for extinction probabilites (Glo, Gup).

Adapted to R by Patrick Nantel, 4 May 2005, from program 'extprob' of Morris \& Doak (2002: 79-86)

Dennis et al. 1991, Ecological Monographs 61: 115-143

Morris, W. F., and D. F. Doak. 2002. Quantitative conservation biology: Theory and practice of population viability analysis. Sinauer, Sunderland, Massachusetts, USA.

extCDF

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## plot like Figure 3.8 in Morris and Doak (2002).
logN <- log(grizzly$N[-1]/grizzly$N[-39])
countCDFxt(mu=mean(logN), sig2=var(logN), nt=38, tq=38, Nc=99, Ne=20)