The following example is using NetLogo Fur model (Wilensky 2003) to show explicit parameter values definition.

There are 5 parameters in the NetLogo Fur model:

• ratio
• outer-radius-y
• inner-radius-y
• outer-radius-x
• inner-radius-x

But considering constraints and model symmetry we can reduce the parameters to:

• ratio (the inhibitor concentration parameter)
• rdiff (the difference between x and y radius) and
• gap (distance between inner and outer ellipse)

Example below still uses factorial design but on transformed parameter space ratio * rdiff while keeping the gap between the circles constant.

#All combinations of ratio x rdiff 
param_sets <- expand.grid(
  gap = 3,
  rdiff = seq(0, 2, by = 0.5),
  ratio = seq(0.30, 0.65, by = 0.05)
)
# Add NetLogo variables
param_sets <- transform(param_sets,
  inner_radius_x = 3,                         
  outer_radius_x = 3 + gap,
  inner_radius_y = 3 + rdiff,
  outer_radius_y = 3 + rdiff + gap
)

Instead of list with parameter values as in previous examples, we can use this data frame with explicit parameter sets to define param_values:

experiment <- nl_experiment(
  model_file = "models/Sample Models/Biology/Fur.nlogo",
  iterations = 20,
  
  param_values = param_sets,      # here param_sets is a data frame
  
  mapping = c(
    gap = "",
    rdiff = "",
    inner_radius_x = "inner-radius-x",
    outer_radius_x = "outer-radius-x",
    inner_radius_y = "inner-radius-y",
    outer_radius_y = "outer-radius-y"
  ),
  patches_after = list(
    patches = patch_set(
      vars = c("pxcor", "pycor", "pcolor"),
      patches = "patches"
    )
  ),
  random_seed = 3
)

result <- nl_run(experiment, parallel = TRUE, max_cores = 3) 

library(ggplot2)
nl_show_patches(result, x_param = "ratio", y_param = "rdiff") +
  scale_fill_manual(values = c("black","white")) +
  coord_fixed() +
  labs(y=expression(radius[y] - radius[x]), title = "Fur patterns (gap = 3)")