Description Usage Arguments Details Value Examples
Run end-to-end MDFS
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data |
input data where columns are variables and rows are observations (all numeric) |
decision |
decision variable as a boolean vector of length equal to number of observations |
n.contrast |
number of constrast variables (defaults to max of 1/10 of variables number and 30) |
dimensions |
number of dimensions (a positive integer; on CUDA limited to 2–5 range) |
divisions |
number of divisions (from 1 to 15; |
discretizations |
number of discretizations |
range |
discretization range (from 0.0 to 1.0; |
pc.xi |
parameter xi used to compute pseudocounts (the default is recommended not to be changed) |
p.adjust.method |
method as accepted by |
level |
statistical significance level |
seed |
seed for PRNG used during discretizations ( |
use.CUDA |
whether to use CUDA acceleration (must be compiled with CUDA) |
In case of FDR control it is recommended to use Benjamini-Hochberg-Yekutieli p-value adjustment
method ("BY"
in p.adjust
) due to unknown dependencies between tests.
A list
with the following fields:
contrast.indices
– indices of variables chosen to build contrast variables
contrast.variables
– built contrast variables
MIG.Result
– result of ComputeMaxInfoGains
MDFS
– result of ComputePValue (the MDFS object)
statistic
– vector of statistic's values (IGs) for corresponding variables
p.value
– vector of p-values for corresponding variables
adjusted.p.value
– vector of adjusted p-values for corresponding variables
relevant.variables
– vector of relevant variables indices
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