Bootstrap estimation of resource selection probability functions
Sandra V. Cardozo
Bryan F. J. Manly
Raydonal Ospina
Carlos T. S. Dias
Statistics Department, National University of Colombia, Bogota, Colombia
Western EcoSystems Technology Inc. Cheyenne, Wyoming, USA
Statistics Department, Federal University of Pernambuco, Recife/PE, Brazil
Statistics Department, ESALQ, University of Sao Paulo, Piracicaba/SP, Brazil
Computational Ecology and Software
ISSN 2220-721X
http://www.iaees.org/publications/journals/ces/online-version.asp
2013
3
4
91
101
International Academy of Ecology and Environmental Sciences
Hong Kong
6 September 2013
10 October 2013
1 December 2013
resource selection functions (RSFs)
resource selection probability function (RSPF)
bootstrap
logistic regression
Resource selection functions (RSFs) are used for quantify how animals are selective in the use of the habitat period or food. A Resource Selection Probability Function (RSPF) can be estimated if N, the total number of units in the population, and n1 the total number of used units in the study period are both known and small. An approximation of the RSPF can then be estimated using any standard program for logistic regression but the variances of the estimates of the parameters are too small. Three methods of bootstrap sampling, parametric, nonparametric and a modified parametric method are proposed for the estimation of variances, with a discussion about the limitations of logistic regression for estimating RSPF. The method for estimating the RSPF described here has potential applications in medicine, ecology and other areas.
DOI 10.0000/issn-2220-721x-compuecol-2013-v3-0011
http://www.iaees.org/publications/journals/ces/articles/2013-3(4)/bootstrap-estimation-of-resource-selection-probability-functions.pdf