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First, we performed bootstrap analysis using the Seoul dataset to obtain unbiased estimates of the developed model.
We performed bootstrap analysis using the rms package in R. 34 All other analyses were carried out in IBM SPSS (v21).
Should the data be skewed then non-parametric bootstrap analysis using the percentile method confidence interval [ 26] will be undertaken and incremental net benefits estimated.
Support for branches on the ML tree was evaluated using bootstrap analysis, using the frequency-based criteria (FC) parameter to determine the number of iterations.
In order to verify the topology of the Bayesian phylogenetic trees supported by the posterior probability, we performed bootstrap analysis using the Maximum Likelihood method implemented in RAxML software [ 72].
Furthermore, to evaluate the detection performance differences between methods statistically, we conducted the bootstrap analysis using the script provided in a report of an earlier study (Green and Brenner, 2002).
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Each bootstrap analysis used the same substitution models as the searches for best trees.
For each locus, we performed a 1000 pseudoreplicate bootstrap analysis using these chosen models.
To confirm the existence of this cluster, we performed bootstrap analysis using pvclust, with the results showing an identical cluster with an AU probability of 99%.
Subsequently, for each defined subfamily, maximum likelihood (ML) phylogenetic trees were built by using PhyML [ 77], and the reliability of the inferred relationships the trees was tested by bootstrap analysis using 100 resamplings of the data set.
The statistical robustness within each phylogenetic tree was confirmed with a bootstrap analysis using 1000 replicates for the Neighbor-Joining (NJ) tree.
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