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First, the fraction of significant combinations increases with the number of breaks in the variable species.
Together they identified the breaks in the variable and then offered the substantive interpretation of the levels of proficiency.
At the stringent parameter combinations, often there are very few breaks in the variable species to reliably estimate significance of joint breaks.
For Rank differential = 2, the table shows the number of breaks in the variable species, the number of joint breaks, the average number of joint breaks in 1000 random shuffles and the p-value of the joint breaks, for FBP.
In contrast to the 5 species case, both for FBP and FBR, the number of breaks in the variable species is too small relative to the number of blocks to achieve a significance in correlated breaks (data not shown).
The above model – FBP – misses the cases in which breaks in the variable species are not bounded by a single pair of markers, but are nevertheless within a short distance of one another.
Similar(53)
Recall that based on our parsimony assumption, breaks in the two variable species must have occurred independently.
In each of the s blocks, we then identify whether a break occurred in the variable species using the criteria defined above.
The i th block has a joint break in the two variable species if BT [i,1] = BT [i,2] = 1.
The entry in i th row and say 1st column – BT [i,1] – is 1 if the i th block is broken in the first variable species and 0 otherwise (likewise for 2nd column).
The grouping of regions by claim rate represented in the maps was derived using a software algorithm that computes the natural breaks in the distribution of the variable.
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