Mixed Effects Models and Extensions in Ecology with R
By A. F. Militino. 2010
706 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Ecology, Evolution, Behavior and Systematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/500ab763d2683aa6a573feb95af1dd0d239c3bd5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Linda S. L. Tan
- David J. Nott
- Dietmar Moser
- W. Dawson
- Fraser A. Januchowski‐Hartley
- S. Bacher
- Warren P. Porter
- M. Vilà
In Ecology, Evolution, Behavior and Systematics
In Statistics and Probability
- Linda S. L. Tan
- David J. Nott
- M. Vilà
- B. Mitchell
- A. Little
- D. Schoolmaster
- G. Guntenspergen
- K. Miller
Citations in the corpus
- Guidelines for a graph-theoretic implementation of structural equation modeling (2012, background): “The accommoda-tions for these complexities that have been developed in statistical modeling (e.g., Gelman and Hill 2007, Zuur et al. 2009) can typically be brought into SE models, though an additional element of care is needed to accommodate the requirements for causal interpretations.”
- Mixed Effects Models and Extensions in Ecology with R (2009, methodology): “See, for example, Chapter 4 in Zuur et al. (2007) for guidance on this.”
- Brown bear conservation and the ghost of persecution past (2011, methodology): “…densities >100 individuals/1000 km2 had access to spawning salmon (Table 1) (Spearman’s rho = 0.668, p < 0.001; variance inflation factor = 5.251, Zuur et al. (2009)), we considered whether or not a population had access to spawning salmon only to subsample populations for analysis, and not as…”
- Behavior and nutritional condition buffer a large‐bodied endotherm against direct and indirect effects of climate (2014, methodology): “We estimated RSFs by fitting generalized linear mixed models with a binomial error distribution and logit link function (Gillies et al. 2006, Bolker et al. 2009, Zuur et al. 2009) to the used (coded 1) and random (coded 0) locations for each study site.”
- Whole‐community facilitation by beaver: ecosystem engineer increases waterbird diversity (2014, methodology): “The effect of the duration of the flood was analysed with generalized linear mixed modeling (Bolker et al., 2009, Zuur et al., 2009) by using the nlme library (Pinheiro et al., 2013) in R 3.0.0 (R Development Core Team, 2013).”
- Global rise in emerging alien species results from increased accessibility of new source pools (2018, methodology): “Following recommendations (50), we analyzed diagnostic plots (not shown) of fitted values, residuals, and the fitted splines for each term of each GAMM to assess the goodness of fits.”
- Relationships between structural complexity, coral traits, and reef fish assemblages (2017, methodology): “Variance structures are variables used to model the structure of the 237 residuals, without a penalty of adding more model parameters (Zuur et al. 2009).”
- Variational Inference for Generalized Linear Mixed Models Using Partially Noncentered Parametrizations (2012, methodology): “At the recommendation of Zuur et al. (2009), we center t to reduce correlation of t with the intercept.”
- Seize the night: European Blackbirds (Turdus merula) extend their foraging activity under artificial illumination (2015, methodology): “Furthermore, we assessed standard model validation graphs to verify homogeneity, normality, and independence of residuals (Zuur et al. 2009).”
- Fertilization decreases species diversity but increases functional diversity: A three-year experiment in a Tibetan alpine meadow (2014, background): “Linear mixed-effects models have flexibility in modeling covariance structures for repeated measures data (Zuur et al., 2009).”
- Comparing impacts of alien plants and animals in Europe using a standard scoring system (2015, methodology): “…of random effects improved model fit (i.e. that taxa differ in their impact) compared to an equivalent model without random effects fitted by generalized least squares (function gls from the package nlme, version 3 1-113; Pinheiro et al. 2013) by comparing their AICc values (Zuur et al. 2009).”