The 窶廢vent窶 popup menu is used to choose the event type. A DynRisk event supports the following event types:
窶｢ Set number
窶｢ Get number
Set number
The 窶彜et number窶 event is used to set attributes of a node during a simulation. Which attribute to set, is controlled by the 窶弃roperty窶 popup menu. The following attributes are available:
窶｢ Local factor
窶｢ Param (a)
窶｢ Param (b)
窶｢ Param (c)
Note, however, that you are not allowed to set the attributes 窶弃aram (a)窶, 窶弃aram (b)窶, or 窶弃aram (c)窶 on a node which is currently being simulated. You can, however, set these parameters on a node in a separate model, and then include another DynRisk event in the current model that initiates a single run simulation on the node after it has been modified.
Get number
The 窶廨et number窶 event can be used to get attributes or simulation results from a node during a simulation. Which attribute or result to get, is controlled by the 窶弃roperty窶 popup menu. The following attributes are available:
窶｢ Local factor
窶｢ Param (a)
窶｢ Param (b)
窶｢ Param (c)
A 窶廨et number窶 event can also initiate single run simulations on a node in a separate model. This works just as if the event did a 窶弉uicktest窶 on this node. You can choose between deterministic and random results, just as in the 窶弉uicktest窶 dialog. Moreover, you can choose between input, local, and output values. This yields the following list of options:
窶｢ Determ. input
窶｢ Determ. local
窶｢ Determ. output
窶｢ Random input
窶｢ Random local
窶｢ Random output
Finally, a 窶廨et number窶 event can be used to resample from the simulation values of a data node. The following resample options are available:
窶｢ Determ. output
窶｢ Random output
窶｢ Ranked output
The 窶廛eterm. output窶 simply gives you the 窶廝ase value窶 attribute of the data node.
The 窶彝andom output窶 gives you a random sample drawn from the set of simulation values for the data node.
Note that if you, in your model, resample from several data nodes located in the same result file, it is important to preserve the correct dependence between these data nodes. DynRisk does this by making sure that within a simulation run on you current model, you always get resampled values from the data nodes which occurred in the same run in the original simulation, i.e., the simulation that produced the data nodes.
Note also that if you, in your model, resample from several data nodes located in different result files, DynRisk has no way of preserving any possible dependence between the data nodes. As a result, such data nodes would be treated as if they were completely independent. Thus, unless you think that this is a realistic assumption, you should avoid mixing together data nodes from different files.
The 窶彝anked output窶 option is only of interest if you resample more than one data node in your model. Each event will then obtain a randomly sampled value from its corresponding data node. However, DynRisk will carry out the resampling on a ranked data set instead of the original set. Thus, if a large value is sampled for one of the data nodes, then the sampled values from the other data nodes will be large as well. Similarly, if a small value is sampled for one of the data nodes, then the sampled values from the other data nodes will be small as well.
This sampling method creates a very extreme kind of dependence, and should be used with caution. It is probably most useful in a sensitivity analysis where you want to find an upper bound on the effect of dependence.
Note that the 窶彝anked output窶 option works for data nodes in the same result file as well as data nodes located in different files.
