The results show that, for each of the datasets, the samples of different class can be correctly identified.
The results are plotted in Figure 2B, with t= the period of data collection for each of the datasets.
These measurements were repeated following application of the appropriate SUVref filter to each of the datasets prior to measurement of the maximum voxel intensity in each hot sphere.
In addition to exporting data, the database handler also generates a summary file containing metadata for each of the datasets, as well as their respective variable sets.
For each of the datasets, we record the processing time for 10 000 times and calculate the average as the result.
The most appropriate model for each of the datasets was chosen by employing the Akaike information criterion (AIC).
For each of the datasets in this study, 150 independent partitions were made for calculations of accuracy and reproducibility.
We test the normalized STD as a function of the correlation threshold for each of the datasets.
For each of the datasets we constructed k-, r-, and a-LoCoH UDs over a range of parameter values.
We built the trees on each of the datasets by using the four methods and recorded the time that each method took.
Table S8 gives the number of individuals and SNPs in each of the datasets, and Table S9 indicates the dataset used in each analysis.
