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After inclusion of hemato-oncological disease as a predictor, the model accounted for 6.4% of overall variation in resource use (R2 = 0.064, P < 0.0001).
The mathematical model is not a perfect predictor: the model of a drug block is very simplistic and could be improved by measurement of dose response curve hill coefficients.
To allow interpretation of the estimated effect of each predictor, the model will be summarised using plots of the shape of the effect of each predictor, as well as Wald χ statistics, penalised for degrees of freedom.
In the model that included only maternal PCB-153 concentration as a predictor, the R value was 0.08; however when breastfeeding duration was added as an additional predictor, the model R was 0.66, strongly suggesting that most of the variance in postnatal PCB concentrations is explained by breastfeeding duration rather than maternal PCB concentrations.
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Using these more complex predictors the model was not able to make substantially better predictions.
Given a data on response and predictors, the model is fitted using maximum likelihood estimates (MLE) of the unknown regression coefficients.
Through a combination of childhood predictors, the model correctly classified 82%2727 of 33) of the participants who eventually developed a psychosis-spectrum outcome in adulthood.
With only 16 predictors, the model improved naturalistic performance over the HRC by 68%, and including fewer than half of the predictors improved it by the full 92%.
Using the chosen predictors, the model was able to correctly classify 70.2% of the adolescents as engaging in binge drinking or not.
Despite the low number of predictors, the model shows good discriminating power, also achieving a satisfactory compromise between discrimination and generalization.
All examined characteristics, except language, were significant predictors in the regression model and SRH was the strongest predictor; however, the model explained only 7% of the total variance.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com