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A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909).
In the non-academic group, the no-decline class proportion was significantly lower, with (uppi_{I, nac} = 0.47).
The behavior of several landscape pattern metrics were tested against various pattern scenarios generated by neutral landscape models, including number of classes, scale-map extent, scale-resolution, class proportion, aggregation level RULE, and aggregation level SimMap.
In contrast, the proportions of the no-decline class were smallest for the MPDM: here, the no-decline class proportion in the academic group was (uppi_{I, aca} = 0.68), which means that one-third of the students showed PD.
As displayed in Eq. 2, the distribution of decline classes was defined by the no-decline class proportion, (uppi_{Ig}), and the shape parameter, ω g : the higher ω g, the steeper the increase in decline class proportions across item positions; yet, the higher the π Ig, the smaller the cumulated probability of the decline classes and, hence, the smaller the total number of students showing PD.
A total of 1,404,470 AEs from 832 drugs were analyzed as a function of reports of suicidal ideations; other non-suicidal adverse reactions; drug class; proportion of reports from males; and average age of subject for which AE was filed.
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However, the no-decline class proportions varied across models.
When weighted by class proportions, percent correctly classified and kappa rose to 88% and 0.84, respectively.
Selection was performed maintaining a distribution of the quantitative data values and class proportions for the qualitative data.
Once the best LCA model was obtained, we then tested for the gender invariance of class proportions and probabilities.
In addition, we did not impose any constraints on the latent class proportions, (uppi_{{i_{0} g}}) and π Ig.
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