Generate an events file for a dyad
basic_event_df.RdThis function constructs an events dataframe in the proper format for inputting to write_input_from_df. It constructs basic dataframes only. It is limited to two actors who must act in patterned ways and only perform one type of action throughout the interaction. This can be the optimal action as calculated by bayesact or interact or it can be an action from the behaviors dictionary. It is possible to simulate interaction in larger network structures and with more complex patterns of action. Dataframes created by this function may be useful as templates for creating the more complicated events files needed for this.
Usage
basic_event_df(
n,
actors,
a1_action = "bayesact_optimal",
a2_action = "bayesact_optimal",
a1_emotion = "none",
a2_emotion = "none",
noise = "none",
act_simultaneously = FALSE,
perspective_shift = TRUE,
switch_actor_after = 1
)Arguments
- n
numeric; number of turns
- actors
string vector of length 2; names of agents a1 and a2
- a1_action, a2_action
string; one of "bayesact_optimal", "interact_optimal", or an action from the dictionary. Default "bayesact_optimal".
- a1_emotion, a2_emotion
string; one of "none", "bayesact_optimal", "interact_optimal", or a modifier from the modifier dictionary. Default "none".
- noise
string vector listing all the places where noise is desired. Options are "none" (default), "a1_action", "a1_emotion", "a2_action", "a2_emotion"
- act_simultaneously
logical; whether the actors should act at the same time. Default FALSE--in this case, they will instead take turns.
- perspective_shift
logical; whether to switch which actor is the agent/client when the turn switches. If true (default), the "perspective" of bayesact is always from the agent's point of view. If false, the perspective is always from one actor's point of view.
- switch_actor_after
numeric; how many actions each actor should take before switching. Default 1. If act_simultaneously is TRUE, this is ignored.
Examples
basic_event_df(10, actors = c("Sally", "Reem"))
#> agent agent_action agent_emotion object object_action object_emotion
#> 1 Sally * Reem
#> 2 Reem * Sally
#> 3 Sally * Reem
#> 4 Reem * Sally
#> 5 Sally * Reem
#> 6 Reem * Sally
#> 7 Sally * Reem
#> 8 Reem * Sally
#> 9 Sally * Reem
#> 10 Reem * Sally
basic_event_df(2, actors = c("Jamal", "Sam"), a1_action = "greet", a2_action = "bayesact_optimal",
noise = c("a1_action", "a2_action"))
#> agent agent_action agent_emotion object object_action object_emotion
#> 1 Jamal greet+ Sam
#> 2 Sam *+ Jamal
basic_event_df(7, actors = c("Sally", "Reem"),
noise = c("a1_action", "a2_action"), perspective_shift = FALSE,
switch_actor_after = 2)
#> agent agent_action agent_emotion object object_action object_emotion
#> 1 Sally *+ Reem
#> 2 Sally *+ Reem
#> 3 Sally Reem *+
#> 4 Sally Reem *+
#> 5 Sally *+ Reem
#> 6 Sally *+ Reem
#> 7 Sally Reem *+
basic_event_df(4, actors = c("Jamal", "Sam"),
a1_action = "greet", a2_action = "bayesact_optimal",
a1_emotion = "excited", a2_emotion = "tired",
noise = c("a1_action", "a2_action", "a1_emotion", "a2_emotion"))
#> agent agent_action agent_emotion object object_action object_emotion
#> 1 Jamal greet+ excited+ Sam
#> 2 Sam *+ tired+ Jamal
#> 3 Jamal greet+ excited+ Sam
#> 4 Sam *+ tired+ Jamal