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Because RF++ is constructed to accommodate clustered data, it summarizes classification both at the replicate and subject level.
Statistical analyses were therefore done on measures obtained at the replicate level.
The basic data for studying life-history trait covariation between strains were the estimates at the replicate level of 14 parameters (Table 2).
Because replicates of a given strain were not linked between experiments, we used a permutation at the replicate level procedure to correlate parameters from different experiments.
From the raw cell measurements, we estimated three major variables at the replicate level: initial cell shape, dispersal rate and cell elongation (Table 2).
From the raw cell measurements at the replicate level, we estimated five major variables describing cell population growth: carrying capacity K and growth rate r, and final (i.e. at carrying capacity) cell density, size and shape (Table 2).
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First, RF++ grows each tree on a bootstrap sample (a random sample selected with replacement) at the subject-level rather than at the replicate-level of the training data.
Most cancer research and therapy design to date has focused on chemotherapy directed at killing the replicating tumor cells.
The precision of the TGI temperature measurements was better than ±0.12°C, and for a 0 40°C gradient, the temperature at the six replicate sample wells varied less than ±0.04°C.
The experimental data are presented as mean ± SD of the replicate experiment at *p ≤ 0.05.
At each site, soil from the replicate cores was mixed to constitute a composite sample.
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