|Simulation Example 4|
|Methods||Main effects average∗||X100X102||X300X302||X50X250||(Variables)|
|pLPS||96 (100)||98 (100,100)||98 (98,100)||99 (100,100)||320|
|RF||NA (99)||NA (96,100)||NA (87,72)||NA (94,89)||(1268)|
|SPLR||100(100)||97 (100,100)||82 (100,100)||97 (100,100)||1017|
n = 700 and p = 200, with positive correlations among neighbors in the first 200 variables and negative correlations among neighbors in the next 200 variables. Tabulated numbers show the number of tests (out of 100) 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 100 tests) that the algorithms selected patterns (variables for RF) that are not in the true model.
∗The average of X1, X3, X10, X201, X210, X220 and X230.
Shi et al.
Shi et al. BMC Bioinformatics 2012 13:98 doi:10.1186/1471-2105-13-98