search.efficiency {carcass}R Documentation

Estimates detection probability per person and visibility classes using a binomial model


The function estimates detection probability per visibility class and person. A binomial mixed model is used with vegetation density as fixed effect and person as random factor, when data of more than 2 persons are available. The number of found and the number of overseen items have to be provided per person and visibility class (one line per observer and visibility class). When only one visibility class is available, the variable visibility must be provided with only one entry. It does not matter what. The detection probabilities are given per person with a 95% credible interval. An average vegetation density specific detection probabilty over all persons is given in addition.


search.efficiency(dat=NA, person=NA, visibility=NA, detected=NA,
    notdetected=NA, nsim = 1000)


The search efficiancy data may be provided as a data.frame containing all data or, alternatively, as seperate vectors. If no visibility classes are available, the variable visibility should be a vector with the same length as the others containing a single value (e.g. "not_measured").


Data.frame containing the following columns:
person: names of the persons who searched
visibility: visibility class
detected: number of detected items
notdetected: number of not detected items


vector with names of the persons who searched


vector with visibility classes


numeric vector with number of detected items


numeric vector with number of not detected items


the number of simulations from the posterior distributions of the model parameters used to construct the 95 percent credible intervals


The function uses the function glmer of the package lme4 and the function sim of the package arm.


A list with two elements of class data.frame


a data.frame with the estimated detection probabilities per person and visibility class, its lower and uper limit of the 95% credible interval and its standard error


a data.frame with the estimated detection probabilities per visibility class averaged over the persons


Fraenzi Korner-Nievergelt


Gelman A, Hill J (2007) Data Analysis Using Regression and Multilevel and Hierarchical Models. Cambridge: Cambridge University Press.

Niermann I, Brinkmann R, Korner-Nievergelt F, Behr O (2011) Systematische Schlagopfersuche - Methodische Rahmenbedingungen, statistische Analyseverfahren und Ergebnisse. In: Brinkmann R, Niermann I, Behr O, editors. Entwicklung von Methoden zur Untersuchung und Reduktion des Kollisionsrisikos von Fledermaeusen an Onshore-Windenergieanlagen Goettingen: Cuvillier Verlag. pp. 40-115.



# Call to the function with data provided as data.frame:
## Not run: search.efficiency(searches)

# Alternative:
per <- searches$person
visi <- searches$visibility
det <- searches$detected
notdet <- searches$notdetected
## Not run: search.efficiency(person=per, visibility=visi, detected=det, notdetected=notdet)

[Package carcass version 1.6 Index]