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Therefore, we examined whether the inclusion of polynomial terms better fits our data, using the Akaike information criterion (AIC) [(21), see Materials and Methods].
We then performed likelihood ratio test (LRT) and Bayes Factor test (BF), using calculated marginal likelihood scores and discovered that a relaxed log normal clock model better fits our data (LTR: p < 0.001; logBF = 27.1).
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The model with residual ancestral migration rate (m0∼10−10) and full replacement (δ = 1) clearly better fitted our data than any other model (Figure 3A, highest ψ1, the ψ1 of this model is significantly higher after correction for multiple testing when compared with the other ψ1 values, P<0.01).
The model of a sex-linked locus for average sensillar response ratios fits our data better than the single autosomal codominant gene model found by earlier studies [ 11].
Based on AIC values and log-likelihoods, the λ-model fitted our data better and therefore was chosen for the interpretation of our results.
Overall, we did not find compelling evidence to suggest that the mixed models fit our data better than the fixed-effects model.
3 However, multicomponent models of this sophistication did not fit our data better than the models we derived.
A simpler model such as polytomous regression or predictive mean matching seems to fit our data better.
We found that soluble ICAM-1 levels correlated significantly with both plasma ANG-2 and hemoglobin, and that a model including only plasma ANG-2 and hemoglobin fit our data better than one including all three predictors.
Model M1a (nearly neutral), which hypothesizes a variable selective pressure among sites but not positive selection and M2a, which considers positive selection, fit our data better than the M0 model, with an ℓ = -6689.544 in both cases (Table 2).
Conversely, treating time as categorical better fits the data when there are fewer missing data and fewer time points.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com