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carcass (version 1.7)

CIetterson: Confidence interval for the functions ettersonEq14, ettersonEq14v1 and ettersonEq14v2

Description

Calculates the Monte Carlo confidence intervals for the estimated carcass detection probability when persistence probability and searcher efficiency are uncertain.

Usage

CIetterson(s, s.lwr, s.upr, f, f.lwr, f.upr, J, s.time.variance = "carcass age", 
f.time.variance = "number of searches", nsim = 1000, ci = 0.95)

Value

a list

p.lower

lower limit of the confidence interval

p.upper

upper limit of the confidence interval

Arguments

s

point estiate for persistence probability (see help file for functions etterson14, ettersonEq14v1 or ettersonEq14v2)

s.lwr

lower limit of the 95% confidence interval of persistence probability

s.upr

upper limit of the 95% confidence interval of persistence probability

f

point estimate for the searcher efficiency (see help file for functions etterson14, ettersonEq14v1 or ettersonEq14v2

f.lwr

lower limit of the 95% confidence interval of searcher efficiency

f.upr

upper limit of the 95% confidence interval of searcher efficiency

J

vector of search intervals

s.time.variance

character, one of "date" or "carcass age"

f.time.variance

character, one of "date" or "number of searches"

nsim

number of Monte Carlo simulations

ci

size of the confidence interval, default is 0.95

Author

F. Korner

Details

The time variance in s and f is either both with date or both with carcass age and number of searches, respectively. In case of constant s and f, the function uses ettersonEq14 independent of the arguments s.time.variance or f.time.variance, when only one value is given for both parameters.

Examples

Run this code
J <- c(2,3,2,4,3,5,3,2,3,4)
s <- plogis(seq(0.2, 2, length=sum(J)))
f <- plogis(seq(1.5, 0.9, length=length(J)))

s.lwr<- plogis(seq(0.2, 2, length=sum(J))-0.5)
f.lwr <- plogis(seq(1.5, 0.9, length=length(J))-0.3)

s.upr <- plogis(seq(0.2, 2, length=sum(J))+0.5)
f.upr <- plogis(seq(1.5, 0.9, length=length(J))+0.3)

CIetterson(s=s, s.lwr=s.lwr, s.upr=s.upr, f=f, f.lwr=f.lwr, f.upr=f.upr, J=J, nsim=100)
 # nsim is too low, please, increase!

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