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Linear, polynomial and logarithmic regressions were fitted to the data, and the significant regression that best-fit the data was selected using Akaike's Information Criterion.
For the remaining five analyses the model of protein evolution that best fits the data was selected using MEGA.
The value of K that best fits our data was selected using the Δ K statistic [ 37].
The statistically significant model for the data was selected using a series of LRTs to compare models and their more parameter rich extensions.
The model that best fit the data was selected using the deviance function and was assessed by comparing the effects of each parameter in relation to the full model (age, period, and cohort).
The value of K that best fitted our data was selected using the estimated log probability of data Pr(X| K) and the derived ΔK statistic (Evanno et al. 2005).
Similar(54)
Data were selected using the same criteria as for the SIFM model (Olsen et al.
Observatory data were selected using the same criteria as for the satellite data and were not further decimated.
Level 1b magnetic data are selected using standard geomagnetic indices: Kp, Dst and the interplanetary magnetic field (IMF) By and Bz components.
The synthetic data were selected using the following criteria: K p ≤ 2 o, −20 nT ≤ Dst ≤ 20 nT, −8 nT ≤+ IMF B y ≤ 8 nT and −2 nT ≤ IMF B z ≤ 6 nT.
Models are nucleotide and amino acid data were selected using ModelGenerator.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com