The following types of columns are created:
If you want to convert these, look at
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
Parameters are trafoed (potentially, depending on the setting of argument
trafo); 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
requires setting) 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)
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
“trafo”, which is set to the value of argument
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.