ojaMedian.ojaMedianControl(sigmaInit = 0, sigmaAda = 20, adaFactor = 0.5,
iter = 1e+06, useAllSubsets = FALSE,
nSubsetsUsed = 1000, sigmaLog10Dec = 10,
storeSubDet = TRUE, eps = 0.1, chi2 = 0.95,
samples = 20, maxlines = 1000, S1 = cov,
S2 = cov4, S1args = list(), S2args = list())evo algorithm): Set the initial variance of the mutation vector in the first run.evo algorithm): Defines after how many mutations the variance of the mutation vector is adjusted.evo algorithm): Defines the level of adjustment of the mutation vector.evo algorithm): The maximum number of iterations. If the algorithm does not converge, it stops after iter - iterations.evo algorithm): A logical flag. If it is set all datapoints and resulting simplices are taken into account for the calculation.evo algorithm): If useAllSubsets is not set, this determines how many, randomly selected, datapoints are taken into account.evo algorithm): This is an abort criterion. If the logarithmised initial variance differs more than sigmaLog10Dec from the actual, logarithmised variance, the algorithm stops.evo algorithm): A boolean flag. If it is set subdeterminants are stored. This should always been set to TRUE if $6*(dim -1)*nSubsetsUsedgrid algorithm): This is the abort criterion. If the grid becomes denser than this threshold the algorithm stops.grid algorithm): This is the test niveau of the test, if a grid point could be used as a Oja-Median or not.grid algorithm): This determines how many additional hyperplanes are taken after every run.exact algorithm): This determines how many intersection lines are investigated in addtion to the one with the steepest gradient.evo and grid algorithms): Optional arguments for S1 passed on to ics to compute the invariant coordinate system.evo and grid algorithms): Optional arguments for S2 passed on to ics to compute the invariant coordinate system.ojaMedian, also for references and examples.## Show the default settings:
str(ojaMedianControl())Run the code above in your browser using DataLab