MuMIn : multi-model inference, R package version 0.12.0

By K. Barton. 2009

3,072 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/af1086127738956d63fce81690819de979b6dfcf

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Citations in the corpus

Forest microclimate dynamics drive plant responses to warming (2020, unknown): “squaredGLMM function in the MuMIn package (55).”
A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, methodology): “The Nakagawa & Schielzeth (2013) R(2) functions have been incorporated into 667 several packages, including ‘MuMIn’ (Bartoń 2016) and ‘piecewiseSEM’ (Lefcheck 668 2015), and Johnson (2014) has developed an extension of the functions for random 669 slope models.”
Running off the road: roadside non-native plants invading mountain vegetation (2018, methodology): “Next, we used model averaging of the estimates of all models in which the AICc differed less than 2 from the best model (function model.avg, package MuMIn; Bartoń 2015), and weighted the estimates based on the support for each model, with more weight given to models with a lower AICc (Burnham and A”
Epiphytic lichen diversity along elevational gradients: biological traits reveal a complex response to water and energy (2015, methodology): “The multimodel inference analyses were performed using the MuMIn package (Barton, 2010) implemented in R (R Development Core Team, 2011).”
Species mixing reduces drought susceptibility of Scots pine (Pinus sylvestris L.) and oak (Quercus robur L., Quercus petraea (Matt.) Liebl.) – Site water supply and fertility modify the mixing effect (2020, methodology): “The selection was made with additional help by an automated AIC-based model selection procedure (function dredge from the R-package MuMIn (Barton, 2019)).”
Assessing different components of diversity across a river network using eDNA (2019, methodology): “…to test the null hypothesis that the slope is zero), models containing only one of the explanatory variables, or both variables (with and without interaction), and performed model averaging in order to calculate relative variable importance (R package “MuMIn”, Barton, 2009, version 1.43.6).”
Host diversity drives parasite diversity: meta-analytical insights into patterns and causal mechanisms (2014, methodology): “2011) using the dredge function in the MuMIn package (Bartoń 2012).”
Trait matching of flower visitors and crops predicts fruit set better than trait diversity (2015, methodology): “…compared these four a priori models with the previous best model, with the null model and with the model with the lowest AIC after evaluating the models resulting from all possible combinations of the predicting variables and their pair interactions (MuMIn package, dredge function) (Barton 2014).”
Quantifying variable importance in a multimodel inference framework (2016, methodology): “We used packages MASS (Ripley et al. 2015) to generate data sets via eigendecomposition, MuMIn (Barton 2014) to calculate SW, relaimpo (Gr€omping & Matthias 2014) to conduct hierarchical partitioning of variance, doParallel (Revolution Analytics & Weston 2014) to parallelize simulations and ggplot2 ”
Global patterns of interaction specialization in bird–flower networks (2017, methodology): “Model selection was performed using the dredge function in the “MuMIn” 1.15.6 package (Barton, 2016).”
Taste and smell words form an affectively loaded and emotionally flexible part of the English lexicon (2016, unknown): “All analyses were conducted with R (R Core Team, 2015) and the R packages “lme4” (Bates, Maechler, Bolker, & Walker, 2015), “MuMIn” (Bartoń, 2015), “effsize” (Torchiano, 2015), and “dplyr” (Wickham & Francois, 2015).”
Bee visitation rates to cultivated sunflowers increase with the amount and accessibility of nectar sugars (2017, methodology): “Model selection, including calculation of AICc values and model weights, was conducted using the MuMIn package in R version 3.1.2 (function ‘dredge’) (Barton 2016).”
Contrasting growth and water use strategies in four co-occurring Mediterranean tree species revealed by concurrent measurements of sap flow and stem diameter variations (2015, methodology): “We calculated pseudo-R 2 values for linear mixed 275 models, based on a likelihood ratio test between the target model and a null (intercept276 only) model, as implemented in the R-package MuMIn (Bartoń 2014).”
A review of searcher efficiency and carcass persistence in infrastructure-driven mortality assessment studies (2018, methodology): “All analyses were performed within the R environment (R Core Team, 2017), using the R packages ‘lme4’ (Bates et al., 2015) and ‘MuMIn’ (Barton, 2014).”
Semi‐natural habitats mitigate the effects of temperature rise on wild bees (2017, methodology): “The GAMM was built with the package mgcv version 1.8-3 (Wood 2011), while the packages glmmADMB version 0.8.0 (Skaug et al. 2014) and MuMIn version 1.12.1 (Barton 2014) were used for the GLMM and the multimodel inference, respectively.”
Model averaging, missing data and multiple imputation: a case study for behavioural ecology (2010, methodology): “…(Δ), Akaike weight (w) and parameters based on model averaging (for model averaging details, see Burnham and Anderson 2002; Anderson 2008; Richards et al. 2010; Symonds and Moussalli 2010), we used an R package ‘MuMIn’ (Barton 2009), which has a range of functions to automate an AIC-IT approach.”
Competition for light and water play contrasting roles in driving diversity–productivity relationships in Iberian forests (2014, methodology): “2013), MuMIn (Barton 2013), ordinal (Christensen 2012) and smatr (Warton et al.”