Table 3

Simulation Example 3
False patterns
Methods X100 X200 X300X400 X150X450X451 (Variables)
pLPS3 47 (50) 50 (50) 47 (50,50) 47 (50,49,48) 204
Logic 50 (50) 50 (50) 34 (43,44) 30 (50,44,41) 151
RF NA (50) NA (50) NA (36,40) NA (49,47,49) (279)
SPLR 50 (50) 50 (50) 45 (49,50) 50 (50,50,50) 554

n = 1000 and p = 500, with correlations among neighboring variables. Tabulated numbers show the number of tests (out of 50) in which the pattern was detected by each algorithm. The number outside the parentheses is the number of times the given pattern was selected; the numbers inside the parentheses shows how many times the variables in the pattern are detected in the model, as a main effect or in some interaction. The final column shows the total number of times (in 50 tests) that the algorithms selected patterns (variables for RF) that are not in the true model.

Shi et al.

Shi et al. BMC Bioinformatics 2012 13:98   doi:10.1186/1471-2105-13-98

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