strum(myStrumModel,
myStrumData,
step1OptimControl = list(maxit=5500, fnscale=-10),
startValueControl = list(initPopulation=NULL,
nChildren=NULL,
nGenerations=NULL,
selection1=NULL,
selection2=NULL),
step2OptimControl = list(maxit=5000, reltol=.Machine$double.eps),
ibdMarkers=NULL)strumModel.strumData.optim function in step1.optim function in step2.a (see Details).step1OptimControl argument is given as a list of several control parameters
that is passed directly to the function optim in the step 1 of model fitting.
The step2OptimControl argument is given as a list of several control parameters
that is passed directly to the function optim in the step 2 of model fitting.
The startValueControl argument is given as a list of 5 parameters for the function
to generate the starting values (using genetic algorithm) of model fitting in step 2. Five different parameters in the list are:
initPopulationThe size of initial population. The default value is the number of model parameters * 120.nChildrenThe number of children. The default value is the number of model parameters * 2.nGenerationsThe number of generation. The default value is 20.selection1The size of the initial selection. The default value is 15.selection2The size of the final selection. The default value is 15.a
for the random effects, the value of iMarkers is used to determine the name of
marker(s) for a from the imported ibd file. If no values are specified for
ibdMarkers, then, by default, all imported ibd markers that exist in the
myStrumData object are used, analyzing one by one. Therefore, the analysis result
includes a list of strumFittedModel.createStrumModel, createStrumData# Run strum analysis.
# - myStrumModel is a strumModel object.
# - myStrumData is a strumData object.
#---------------------------------------
fitResult = strum(myStrumModel, myStrumData, ibdMarkers=c("marker1","marker2"))Run the code above in your browser using DataLab