We implemented a Bootstrap (n = 9,999) approach to generate a confidence envelope.
A bootstrapped (n = 1000) neighbor-joining tree on basis of the MSA was constructed and orthologous groups, so-called subfamilies, were identified automatically by LOFT.
The significance of the angle was determined by a bootstrapping procedure (N = 400) similar to that applied for comparisons of allometric vectors (see above).
Differences between flower positions in the mean absolute coefficients of family-mean and phenotypic correlations were estimated by a bootstrapping method (n = 10 000 permutations, using Data Pilot ver.1.03, [ 54]), because the values used in calculating correlation coefficients were not independent.
Significance of overlap was calculated by applying a bootstrap method (n = 100,000), in which two subset of genes were randomly sampled from all annotated genes (19,990) and the number of shared genes between them was counted.
Each tree is grown by taking a bootstrap sample of N objects chosen at random with replacement from a training set containing N objects, so that the same object may appear more than once in the sample.
Evaluation of model robustness was based on relative standard errors (RSEs, %) of the model parameter estimates determined by nonparametric bootstrapping (n = 200).
For each bootstrap repetition b = 1, 2, …, B (say B = 1,000), we construct a bootstrap sample ( y n b, x n b ) n = 1 N by resampling (with replacement) from the original sample ( y n, x n ) n = 1 N with probability 1/N for each observation.
Error bars are S.E.M. and were generated by bootstrapping (N = 58 1897 trajectories; 3 4 keratocytes).
Error bars are S.E.M. and were generated by bootstrapping (N ≥ 202 trajectories; ≥3 experiments).
