persistence.prob: Estimates carcass persistence probability based on carcass...

In carcass: Estimation of the Number of Fatalities from Carcass Searches

Estimates carcass persistence probability based on carcass removal experiment data

Description

This function either uses a Cox proportional hazard model or an exponential model (if persistence probability can assumed to be constant over time) to estimate daily persistence probabilities of carcasses.

Usage

persistence.prob(turbineID, perstime, status, pers.const = FALSE, R = 10000)

Arguments

turbineID	factor, character or numeric vector with name of the turbines or sites for which the carcass persistence probability should be estimated separatedly
perstime	numeric vector with the persistence times for each object (e.g. in days)
status	indicator variable of observed removal (1= removal has been observed, 0 = object was still there at the end of the observation period)
pers.const	logical value indicating whether a constant persistenc probability over time can be assumed. default is FALSE. If FALSE, a Cox proportional hazard model is used and for each turbine/site the estimated proportion of remaining objects is given for each day. If TRUE, an exponential model is fitted and the estimated daily persistence probability is give for each turbine/site.
R	number of Monte Carlo simulations used to obtain the 95 percent confidence intervals of the estimated persistence probabilities from the exponential model

Details

Note that there is increasing evidence in the literature that carcass persistence probability increases with the age of a carcass. Thus you are saver to use non-constant persistence probabilities unless you have tested, how seriously an assumption of constant persistence probability influences your results.

Value

If you do not assume constant persistence probability, the function returns a list:

persistence.prob	matrix with estimated proportion of remaining carcasses after each time indicated on the rows for each turbine/site indicated on the column
estpers.lwr	the lower limits of the 95 percent confidence intervals
estpers.upr	the upper limits of the 95 percent confidence intervals

If you assume constant persistence probability, the function returns a data frame with the following variables:
turbineID: name of the turbine/site
persistence.prob: estimated daily persistence probability
lower: lower limit of the 95 percent confidence interval of the estimated persistence probability
upper: upper limit of the 95 percent confidence interval of the estimated persistence probability
mean.persistence.time: estimated mean persistence time

Note

Whether the models used in this function fits to your data is not in the responsibility of the author!

Author(s)

Fraenzi Korner-Nievergelt

References

Cox, D. R. 1972. Regression models and life-tables (with discussion). Journal of the Royal Statistical Society B 34:187-220.
Klein, J. P. and M. L. Moeschberber. 2003. Survival Analysis, Techiques for Censored and Truncated Data. Springer, New York.

See Also

You will finde more information in the documentation of the packages coxph and survreg.

Examples

data(persistence)
persistence.prob(persistence$turbineID, persistence$perstime, persistence$status)
persistence.prob(persistence$turbineID, persistence$perstime, persistence$status, 
    pers.const=TRUE)

carcass documentation built on Aug. 22, 2025, 9:08 a.m.