Constructor for DDModel-class — DDModel • DDModeling

Userfriendly function for the construction of a DDModel-class.

DDModel(
  model = NULL,
  task = NULL,
  conditions = NULL,
  parameter = NULL,
  dt = NULL,
  sigma = NULL,
  CDF_perc = NULL,
  CAF_perc = NULL
)

Arguments

model	`character` of the name of the Model to be used (choices are "DSTP","DMC","SSP")
task	`character` specifying a specific predefined modelstructure ("flanker","custom","RMT_LDT")
conditions	`character` vector of the names of conditions
parameter	`character` vector of the names of custom parameters
dt	`numeric` representing the integration constant of the diffusion process
sigma	`numeric` representing the diffusion constant of the diffusion process
CDF_perc	`Numeric` vector specifying the CDF percentiles (note: numbers equal to absolut percentiles!)
CAF_perc	`Numeric` vector specifying the CAF percentiles (note: numbers equal to boarders of segments!)

Value

DDModel-class.

Details

The constructor allows for the usage of handy defaults for specific tasks. Choosing task = "flanker" will configure the corresponding model in a way, that is in correspondance to present litretature. Only the type of data representation (i.e. CDF and CAF quantiles) need further specification. If however, one wants to define customized applications of the models, the task = "custom" preset can be choosen. It is important to emphazise that in this case there are several more parameters needed in the constructor (see usage or examples). Moreso after model construction one must specify the parameter domains and model matrizes manually! Be sure to double check your model befor computation. Custom models should only be used, if one knows exactly what he is doing!

Examples

M_DSTP <- DDModel(model="DSTP",task = "flanker",
          CDF_perc = c(0.1,0.3,0.5,0.7,0.9),CAF_perc = c(0.0,0.2,0.4,0.6,0.8,1.0))
M_DMC  <- DDModel(model="DMC",task = "flanker",
          CDF_perc = c(0.1,0.3,0.5,0.7,0.9),CAF_perc = c(0.0,0.2,0.4,0.6,0.8,1.0))
M_Custom <- DDModel(model="DSTP",task="custom",
          CDF_perc = c(0.1,0.3,0.5,0.7,0.9),CAF_perc = c(0.0,0.2,0.4,0.6,0.8,1.0),
          conditions="single",parameter=c("par_1","par_2"),dt=0.001,sigma=0.1)