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The model choice did not affect the results.
> -wrap-foot> We find that, despite differences in fit (see above), model choice did not affect the opsin phylogeny (compare fig. 1 b and c with supplementary fig. S2 a and b, Supplementary Material online, and fig. 2 with supplementary fig. S3, Supplementary Material online).
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In all reviewed studies, little or no rationale was given for the choice of model, and, while model choices did appear appropriate for purpose, it is advisable to include a justification or rationale for the choice of methods [24, 25].
Variations in these apportionment modeling choices do not prevent the consistent discernment of variations in the relative strengths of source-specific PM2.5 mortality associations.
We conducted several sensitivity analyses to verify that model or specification choice did not drive our findings.
However, the latency between the first attack and the second when either of the models was the first choice did not differ significantly (W = 602, P = 0.7955).
We opted for a local linear level and slope model with intervention in state space formulation (Commandeur and Koopman 2007) instead of an Arima model, because our choice does not require user intervention, as Arima does.
However, rural placement and/or a low choice of placement (that is, second through tenth choice as compared to first choice) did not seem to influence public sector intentions in multivariate models.
What choice did they have?
What choice did one have?
The pre-publication history for this paper can be accessed here: Notes: Outcome of each model was 'enough PCP choice' (Do you feel like you had enough choice when you selected (PCP)?
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