persistence.prob {carcass} R Documentation

## 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.

`coxph` `survreg`
```data(persistence)