Table 1

Assignment accuracy using linear discriminant analysis on quartets
Mean Variance Maximum Combined
S 0.36 0.32 0.36
Rmin 0.43 0.45 0.38
rmmg 0.43 0.43 0.45
nHaps 0.60 0.59 0.33
HapDiv 0.59 0.57 0.33
Wall’s B 0.41 0.47 0.34
Wall’s Q 0.40 0.37 0.33
Hudson’s C 0.43 0.42 0.33
ZnS 0.48 0.34 0.48
All unscaled 0.66 0.65 0.52 0.68
S x Rmin 0.36 0.36 0.34
S x rmmg 0.43 0.41 0.45
S x nHaps 0.32 0.36 0.36
S x HapDiv 0.32 0.36 0.36
S x Wall’s B 0.37 0.33 0.31
S x Wall’s Q 0.37 0.34 0.32
S x Hudson’s C 0.42 0.37 0.37
S x ZnS 0.42 0.34 0.43
All scaled 0.64 0.59 0.53 0.67
All combined 0.71

Proportions of datasets assigned correctly to constant, linearly increasing and linearly decreasing recombination models using jackknife cross-validation. Values of one-third indicate assignments no better than chance; values approaching one indicate improving assignment rates. 104 datasets consisting of 10-kb of sequence for 100 individuals were generated under each model. Assignments were made using the mean, variance and maximum value of summary statistics for 103 quartets for each dataset.

Cox et al.

Cox et al. BMC Genetics 2013 14:11   doi:10.1186/1471-2156-14-11

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