generateRandomDesign: Generates a random design for a parameter set.

Description

The following types of columns are created:

numeric(vector)

numeric

integer(vector)

integer

discrete(vector)

factor (names of values = levels)

If you want to convert these, look at convertDataFrameCols. Dependent parameters whose constraints are unsatisfied generate NA entries in their respective columns. 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.

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.)

Usage

generateRandomDesign(n = 10L, par.set, trafo = FALSE)

Arguments

[integer(1)] Number of samples in design. Default is 10.

par.set

[ParamSet] Parameter set.

trafo

[logical(1)] Transform all parameters by using theirs respective transformation functions. Default is FALSE.

Value

[data.frame]. Columns are named by the ids of the parameters. If the 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 trafo.