Generates a random design for a parameter set.
The following types of columns are created:
If you want to convert these, look at
For discrete vectors the levels and their order will be preserved, even if not all levels are present.
The algorithm simply calls
sampleValues and arranges the result in a data.frame.
Parameters are trafoed (potentially, depending on the setting of argument
dependent parameters whose constraints are unsatisfied are set to
generateRandomDesign will NOT work if there are dependencies over multiple levels of
parameters and the dependency is only given with respect to the “previous” parameter.
A current workaround is to state all dependencies on all parameters involved.
(We are working on it.)
Note that if you have trafos attached to your params, the complete creation of the design
(except for the detection of invalid parameters w.r.t to their
takes place on the UNTRANSFORMED scale. So this function samples from a uniform density
over the param space on the UNTRANSFORMED scale, but not necessarily the transformed scale.
generateRandomDesign(n = 10L, par.set, trafo = FALSE)
integer(1)] Number of samples in design. Default is 10.
ParamSet] Parameter set.
logical(1)] Transform all parameters by using theirs respective transformation functions. Default is
data.frame]. Columns are named by the ids of the parameters.
par.set argument contains a vector parameter, its corresponding column names
in the design are the parameter id concatenated with 1 to dimension of the vector.
The result will have an
logical(1) attribute “trafo”,
which is set to the value of argument