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To arbitrate between models, we calculated Bayes factors by comparing harmonic mean model likelihoods [ 106].
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To examine the substantial decrease of 24.1 log units between models, we calculate the posterior distribution of parsimony tree lengths under both models.
In order to select the final model between equally good predictor models we calculated a permutation P value associated with their prediction accuracy.
In order to select the final classifier between the equally performing kNN models, we calculated a permutation P value for the prediction accuracy.
To study differences between original patients' tumors and PDX models we calculated the number of times that each gene was called as 'expressed' in each group, or its 'expression frequency'.
In order to compare the magnitude of group differences as estimated by the four models, we calculated effect sizes between the group means of proficiency.
To verify the model, we calculated the correlation between the observed values from the experiments and the predicted values by the model using the adjusted R-squared ((bar{R}^2)).
Using a random effects model that included between study heterogeneity, we calculated summary relative risks by pooling the study specific estimates.
Confidence intervals for the differences in AUC between models were calculated by bootstrap methods.
In addition, the continuous net reclassification index (cNRI), with 5%% risk reclassification as cut-point between models, was calculated based on logistic models.
To assess the fit between the model and the data we calculated the Comparative Fit Index (CFI) and the Root Mean-Squared Error of Approximation (RMSEA).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com