persistence.prob {carcass} | R Documentation |

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.

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

`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 |

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.

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

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

Fraenzi Korner-Nievergelt

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.

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

[Package *carcass* version 1.6 Index]