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Thus, these four environmental factors served as possible predictors in our model selection analyses.
For the model selection analyses, all variables were standardized to a mean of zero and a standard deviation of one.
To select the best-fitting evolutionary model for Bayesian MCMC inference, we performed an initial series of model selection analyses using BEAST v1.5.4.
The results presented in this paper raise the question of whether we should be performing full protein model selection analyses prior to any amino acid phylogeny estimation.
We did the same for precipitation metrics (Bio12 Bio19) and genetic (HO, FIS, AR) metrics and used the first and second PC axes from each data set (PC1-Precipitation and PC2-Precipitation and PC1-Genetics and PC2-Genetics, hereafter) in downstream model selection analyses.
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Model-selection analyses were implemented in R using the MuMIn package (Barton, 2015).
Here, we employ macroevolutionary phylogenetic model-selection analyses to address these two predictions about evolutionary diversification using one of the most exceptionally species-rich and ecologically diverse lineages of living vertebrates, the South American lizard genus Liolaemus.
For model selection we analysed the likelihood for varying numbered of topics in the range (T in {5, 10, 15,ldots, 100, 150, 200,ldots, 500}).
Results from structural equation models and selection analyses revealed that physical dominance, but not sexual attractiveness, predicted mating success.
Finally, in order to examine the effect of VOI size (assuming that smaller VOIs result in TACs with higher noise levels) on the model selection criteria, similar analyses were performed for VOIs with sizes smaller and larger than 5 mL separately.
Model selection for these analyses was performed using ProtTest [ 70].
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