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The results of RSM revealed that regression model fitted significantly at p≤0.01.
Although this model fitted significantly better than the original model according to the scaled χ2-difference test, comparative fit indexes were still clearly unacceptable.
The bifactor model fitted significantly better than the one-factor model (χ2 (10) = 358.08; p < 0.001).
The Mendelian model was accepted when (1) the general model did not show a significantly better fit to the data than the Mendelian model and (2) the general model fitted significantly better than the sporadic model.
Similar to the positive trials we found that although a linear growth curve model, excluding the Quadratic slope factor, described the data adequately (CFI = .99, SRMR = .03), the linear model fitted significantly worse than the non-linear model (χ(5) = 41.75, P < .001), and the non-linear model was therefore used in the analyses of the family data.
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The results show that the proposed logistic fault-detection model fit significantly better than other existing NHPP models based on all three goodness-of-fit criteria.
Moreover, the likelihood-ratio statistic of 79.38, which has an χ 2 distribution, reveals that the full model fits significantly better than an empty model.
The significant values of the VLMR test show that the estimated model fits significantly better than the model with one class less (Nylund et al. 2007).
The results show that the proposed logistic fault-detection model fits significantly better than all the existing NHPP models studied in this paper based on all MSE, PRR and PP criteria.
Of the two models without time variation in parameters, the interaction model fits significantly better than the baseline model without heterogeneous mixing between species.
The model fits significantly better than the model with constant relative risk (P=0.03).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com