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Now we are ready to employ retrospective and bootstrap analysis on the data sample.
In order to increase confidence in the identified bicluster we undertook bootstrap analysis on the experimental data to generate 1000 pseudo-datasets.
To compare trees from simulated vs empirical data, we performed an MP bootstrap analysis on the empirical data (31-taxon, 5974 bp), and compared these trees to results from 6000 bp of simulated data (trees not shown).
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We performed a bootstrapping analysis on the costs per patient year and two quality of care parameters, to show the degree of uncertainty regarding the results.
Bootstrap analysis on 1000 replicates was performed with Seqboot from the same software package.
Finally, to appreciate the effect of sample size on the spread of estimates for future surveys, we performed bootstrap analysis on our existing data.
The results of the final model were further confirmed in a bootstrap analysis on 10,000 replications.
Branch support values were obtained by nonparametric bootstrap analysis on 1000 pseudo-replicate data sets [ 34].
The overall model uncertainty is investigated in a bootstrap analysis based on the model simulation results, with a 95% confidence interval calculated by the percentile method [ 24].
The new datasets are subsequently refitted to the original model, yielding mean values and standard errors for every model parameter.A bootstrap analysis provides information on the stability of the model and its dependence on specific individuals in the learning dataset.
Assembly, blast searches, and bootstrap analysis were computed on the computer cluster of the Department of Ecology & Evolution at the University of Chicago (http://biocomputing.uchicago.edu).edu
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