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If k-means can be implemented for the data, random swap is only a small extension.
To account for the clustered nature of the data, random effects modeling was used.
Model performance (relative deviance reduction) was evaluated using cross-validation; that is, model parameters were fitted from 80% of the data, and the model was tested with the remaining 20% of the data (random subsampling).
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Here, the data for random networks is the average value of 50 random generated networks.
Random forests generates an ensemble of trees by treating the tree parameters as fixed but the data as random — data and predictors are sampled.
However, since the data have random behavior, it is difficult to apply statistical approaches with apriori and deterministic parameters.
The data contain random and substantial systematic errors, the latter largely arising from crystal-to-crystal variation.
After the coding parameter recognition, an additional testing program checks whether the data is random.
We expected that these effects would be decreased by averaging the data across random, uniformly distributed sequences of stimuli.
The reviewers then abstracted the data, and random effects models were constructed to pool the data for meta-analysis.
To account for the clustered nature of the data sample, random terms for school were included in all models.
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