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Significance level was subsequently determined by bootstrap analyses under the null hypothesis that these windows did not harbor QTL.
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We also conducted bootstrap analyses to evaluate the stability of the regression parameters.
Bootstrap analyses quantified the effects of inter-individual variability in closed area usages.
The tree inferred with PhyML under the best model from the PCMA alignment is shown in Fig. 2. The majority-rule consensus including compatible groupings of the likelihood trees obtained from each of the three complete alignments, respectively, is shown in Fig. 3 together with bootstrap values obtained by separate likelihood bootstrap analyses of the alignments.
These bootstrap analyses confirmed the effects already described by the post hoc tests.
The bootstrap analyses were performed in the R programming language.
All bootstrap analyses were conducted using the program Statistics101.
Bootstrap analyses were performed with the rapid bootstrap algorithm [ 53], using bootstopping criteria ([ 57], command line option: −# autoMRE -B 0.01).
Bootstrap analyses were performed to estimate the uncertainty around the cost and outcome results.
Bootstrap analyses were performed with 100 replicates under the maximum likelihood criterion and with 1 000 replicates for maximum parsimony analysis.
RAxML bootstrap analyses on 1,000 replicates have been performed under the PROTMIX algorithm with the WAG amino acid substitution model [ 70].
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