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An efficient multivariate approach for estimating preference when individual observations are dependent
Author(s) -
Engen Steinar,
Grøtan Vidar,
Halley Duncan,
Nygård Torgeir
Publication year - 2008
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/j.1365-2656.2008.01427.x
Subject(s) - selection (genetic algorithm) , preference , multivariate statistics , population , component (thermodynamics) , statistics , set (abstract data type) , econometrics , variance (accounting) , resource (disambiguation) , mathematics , computer science , artificial intelligence , economics , demography , computer network , physics , accounting , sociology , thermodynamics , programming language
Summary1 We discuss aspects of resource selection based on observing a given vector of resource variables for different individuals at discrete time steps. A new technique for estimating preference of habitat characteristics, applicable when there are multiple individual observations, is proposed. 2 We first show how to estimate preference on the population and individual level when only a single site‐ or resource component is observed. A variance component model based on normal scores in used to estimate mean preference for the population as well as the heterogeneity among individuals defined by the intra‐class correlation. 3 Next, a general technique is proposed for time series of observations of a vector with several components, correcting for the effect of correlations between these. The preference of each single component is analyzed under the assumption of arbitrarily complex selection of the other components. This approach is based on the theory for conditional distributions in the multi‐normal model. 4 The method is demonstrated using a data set of radio‐tagged dispersing juvenile goshawks and their site characteristics, and can be used as a general tool in resource or habitat selection analysis.