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2009 ; 65
(2
): 554-63
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Presence-only data and the em algorithm
#MMPMID18759851
Ward G
; Hastie T
; Barry S
; Elith J
; Leathwick JR
Biometrics
2009[Jun]; 65
(2
): 554-63
PMID18759851
show ga
In ecological modeling of the habitat of a species, it can be prohibitively
expensive to determine species absence. Presence-only data consist of a sample of
locations with observed presences and a separate group of locations sampled from
the full landscape, with unknown presences. We propose an
expectation-maximization algorithm to estimate the underlying presence-absence
logistic model for presence-only data. This algorithm can be used with any
off-the-shelf logistic model. For models with stepwise fitting procedures, such
as boosted trees, the fitting process can be accelerated by interleaving
expectation steps within the procedure. Preliminary analyses based on sampling
from presence-absence records of fish in New Zealand rivers illustrate that this
new procedure can reduce both deviance and the shrinkage of marginal effect
estimates that occur in the naive model often used in practice. Finally, it is
shown that the population prevalence of a species is only identifiable when there
is some unrealistic constraint on the structure of the logistic model. In
practice, it is strongly recommended that an estimate of population prevalence be
provided.
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