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Confidence limits were estimated by a bootstrap algorithm applying 1,000 iterations (29 ).
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The residuals are then centered at zero by subtracting their mean and these effects are resampled using a bootstrap algorithm.
A bootstrap algorithm based on the theoretical framework of Zhang et al. was constructed [21].
To distinguish between these equivalent models, we validated them with a bootstrap algorithm (n=100, sample ratio=1.0).
In addition, we generated 95% confidence intervals for each value of PABAK using a bootstrap algorithm with 10,000 bootstrap samples [ 37].
A bootstrapping algorithm is used to iteratively build a target classifier from the two domains.
A bootstrapping algorithm was used to estimate parameters uncertainties.
Efron et al. [ 37] defined another type of non-Bayesian confidence level (which can be estimated by a two-level bootstrap algorithm), such that and converge at rate, as the sequence length n increases.
We selected the GTR + gamma model for each partition and the rapid Bootstrapping algorithm (Stamatakis A, Hoover P, Rougemont J: A Rapid Bootstrap Algorithm for the RAxML, Web-Servers, unpublished) and ran 500 bootstrap replicates.
A rapid bootstrap algorithm for the RAxML Web servers.
RAxML 7.0.4 conducted 1000 bootstrap replicates using a rapid bootstrap algorithm (Stamatakis et al. 2008) applying the PROTGAMMAWAG approximation.
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