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For comparison of vertical, social transmission to other trait acquisition methods, we ran simulations in which sponging was either a genetic trait or acquired solely by innovation.
*Graph 2 Methods: We ran a single-question survey to a 1000 respondents via Google Surveys.
First, to confirm the positive effect of high-pass filtering for ICA methods, we ran one model with the fixed factors 'method' (extended Infomax, TDSEP and FastICA) and 'high-pass' (yes or no) and their interaction.
In order to compare the different detection methods we ran comparisons on randomly selected 1 Mb genomic regions with the following setting: raw score >0.7 and p<0.05 (NUBIscan), score >6.6, p<10−13 (Patser), default parameter (NHR-scan).
To compare the GA to the other two classification methods, we ran the GA 200 times for each of the three folds.
For the application of ML and Bayesian methods, we ran ProtTest v2.4 (Abascal et al. 2005) to determine the appropriate model of amino acid replacement.
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To evaluate the performance of our methods, we run the 10 fold cross-validation procedure.
To test the performance of the proposed edge-based adaptive method, we ran some experiments using two sets of video sequences.
To determine the reliability of each method, we ran the data of geomagnetic field measurements from the period with known storms through a test storm detector and iterated through a range of parameters unique to each method.
To evaluate normalization method, we ran the test on the tri-gram model with the normal Dice coefficient (Dice) and the improved Dice coefficient (fDice) to measure the similarity of the two sentences.
Given the random-walk nature of the method, we ran 1000 replicate runs, with a convergence limit of 0.01.
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CEO of Professional Science Editing for Scientists @ prosciediting.com