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The window size determines how successive data values are regrouped [ 38]; it must be much smaller than the total sample size, and one can check that the resulting error estimates are insensitive to the choice of this window size.
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As a remedy, we choose to consider the impulse-response function for straight-line interpolation between successive discrete data values.
They are characterized by successive data points with the same or similar value that together produce large blunt peaks.
The blocks are a time series of successive data.
To generate test data successive values were drawn at random from this top-level distribution.
For association study a Bayesian based analysis of population structure was conducted which used an ad hoc statistics (∆K), the rate of change in log probability of data between successive K values.
The most likely value of K is determined by comparing the likelihood of the data for different values of K. To determine the number of clusters, we also calculated the rate of change in the log probability of the data between successive K values (ΔK) plotted against K following Evanno et al. [46].
We computed the rate of change in the log probability of data between successive K values (ΔK statistic; Evanno et al. 2005).
In addition, ΔK was calculated based on the rate of change in the log probability of data between successive K values [ 27].
Finally, estimated log probabilities of data Pr (X | K) for each value of K were evaluated by calculating ΔK, the rate of change in the log probability of data between successive K values [ 29].
Using the ad hoc Δ K statistic based on the rate of change in the log probability of data between successive K values, Structure can be used to identify the number of genetic clusters (Evanno et al. 2005).
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