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Load the Utility (*.exe) and the example inputs
‘SAD_Observation_Sample.txt and a ‘i-SAD_*.txt.
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Lounch the utility ‘SAD_Convergence_Tool.exe’
Press ‘Load initial distribution’ button and load an initial distribution
Press ‘Load observation’ button and load the observed distribution
Press ‘create outputs’ button and create the Output files ‘Output 1.txt’ and
‘Output 2.txt’
Set parameters of computation (see the paper); Slopes min and max are
constraints for autocorrelation (  min and  max ); ‘sample size’ refers to the N
of the sample that is used for the numerical splicing, the smaller is the
number, the faster is the computation, but the higher are the fluctuations that
bias the resulting convergence; ‘limit to save’ refers to the ‘Output 3’ (‘save
moments’ button), which is not required to complete the computation; the logmoments are used for the computation of average moments from this value on
(those average moments are assumed to be reached by the convergence).
Press ‘run’ button (Please, note that the computation may be slow if setting
many steps of iteration, and the window can switch off visualising in the case;
to see a quick convergence, set the small number of steps; however, large
fluctuations will occur in this case.)
See computed figs by switching between panels
Press ‘End’ button
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Outputs
Output 1.txt
Step
-1
-1
-1
0
0
0
1
1
1
Rank Simulation
Observation
1
8.69366774975277E-0004
0.5
8.69366774975277E-0004
0
8.69366774975277E-0004
1
8.69366774975277E-0004
0.5
8.69366774975277E-0004
0
8.69366774975277E-0004
1
8.69366774975277E-0004
0.5
8.69366774975277E-0004
0
8.69366774975277E-0004
Step=-1 labels the observation
Steps>-1 label particular simulations
Values of the ‘Simulation’ and ‘Observation’ are standardized abundances (i.e. mean
abundance = 1).
Output 2.txt
-1 labels the observation, again
Jaccard
see the paper
Slope Min Set
see the paper for  min ; the value that was set to the computation
Slope Max Set
see the paper for  max ; the value that was set to the computation
Slope Min Sim
see above; because the slope is changed during computation in
order to keep marginal distributions (see methods and guide in on line supporting
information), this number show the actual value that was used for the computation
Slope Max Sim
see above
Pearson Correlation Coefficient
the Pearson’s correlation coefficient between
abundances of two adjacent subsamples to be merged; note that the value of correlation
used in the figs in the paper is computed from the ‘Slope min Sim’ and ‘Slope max Sim’
(see above).
Step
step of splicing
Kolmogorov-Smirnov statistics
see the paper
Four statistical moments of the distributions of the log-transformed abundances follow
Ln-Mean Simulated; Ln-Mean observed; ln-StDev Simulated; Ln-StDev observed; LnSkewness Simulated; Ln-Skewness Observed; Ln-Kurtosis Simulated; Ln-Kurtosis
observed
Output 3.txt
Ln-Mean; Ln-StDev; Ln-Skewness; Ln-Kurtosis
The mean moments of the consecutive simulated distributions of log-transformed
abundances across all steps from the ‘limit to save’ on (i.e. those that are assumed to be
reached by the convergence).