iterative modeling with MLHO
mlho.it(
dbmart,
labels = labeldt,
dems = NULL,
test.sample = 30,
MSMR.binarize = FALSE,
MSMR.sparsity = 0.005,
MSMR.jmi = TRUE,
MSMR.topn = 200,
mlearn.save.model = FALSE,
mlearn.note = "mlho_phewas run",
mlearn.aoi = "demo",
mlearn.cv = "cv",
mlearn.nfold = 5,
multicore = FALSE,
preProc = TRUE,
iterations = 5
)
dbmart table
should be the labeldt table
table containing the demographic variables
put 20 if you want to use 20 percent for testing and 80 percent for training
MSMR.lite parameter
MSMR.lite parameter
MSMR.lite parameter
MSMR.lite parameter
mlearn parameter
mlearn parameter
mlearn parameter
mlearn parameter
mlearn parameter
if you want to parallelize the process
preprocessig on the train data or not
number of iterations you want. recommended at least 5. needs to be numeric