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This alternative model fit the data almost identically to our final model (CFI = 0.934; TLI = 0.918; RMSEA = 0.045; SRMR = 0.047).
The alternative model fit was evaluated with the Pregibon link test [ 16] and the Bayesian information criterion (BIC).
An alternative model fit to assess if weight gain could explain some of the observed racial/ethnic variation in diabetes incidence revealed an ∼3% increase in subsequent risk for each 1-kg/m increment in BMI (1.03 [1.03–1.03]).
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Alternative model fitting strategies led to selection of the same two explanatory factors.
If the p-value of the test statistic exceeds that critical value (i.e. if the alternative model fits the data significantly better than the null model), then the null model can be rejected.
Although this alternative model fitted the CFSE data almost as well as our full (unconstrained) model, it was clearly in poor agreement with the estimated cell counts, and it was unable to capture the continued expansion of the cell population over time (see bottom right panel in Figure 6).
However, two alternative models fit the data equally well.
Other research groups examining family history have found that alternative models fit their data better.
Each of these alternative models fit the data better than the heat index only model (Table 2).
The likelihood ratio tests assess whether the alternative models fits the data better than the null model and is known to be conservative in simulation tests.
Reviewer #3: Understanding how specific sequences of behavior are executed remains challenging, with a number of alternative models fitting data in different contexts.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com