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A simple location prediction scheme is used.
We also implement a simple location classification algorithm based on logistic regression on a cluster scale.
The bootstrap procedure described in Algorithm 1 assumes a simple location shift model.
For these data a simple location shift of δ = 5 is equivalent to a standardised effect size ΔNormal = 0.17 and pNoether = Pr Y > X) = 0.55.
In contrast, Figure 3 shows the estimated power curves for another dimension of the SF-36: RP, which can only take one of five discrete values (as shown in Figure 1c), for detecting a simple location shift (mean difference) δ = 5.
Therefore, to illustrate this point, we shall use the five discrete category outcome of the RP dimension of the SF-36 to show the effect of the bootstrap sample size estimator when the alternative to the null hypothesis is an odds ratio transformation rather than a simple location shift.
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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