\@format A data frame with 2600 observations of 6 variables:<dl>
<dt>id</dt>
<dd>(factor) Participant ID</dd><dt>ai_strategy</dt>
<dd>(factor) Strategy adopted by AI player</dd><dt>round</dt>
<dd>(numeric) Round number (between 1 and 50)</dd><dt>human_action</dt>
<dd>(factor) Action taken by human (rock, paper, or scissors)</dd><dt>ai_action</dt>
<dd>(factor) Action taken by AI (rock, paper, or scissors)</dd><dt>score</dt>
<dd>(numeric) Outcome for human player, where 1 indicates a win, -1 a loss, and 0 a tie</dd>
</dl>

datasets

Data sets and functions to support the books "Statistics: Data
analysis and modelling" by Speekenbrink, M. (2021)
<https://mspeekenbrink.github.io/sdam-book/> and "An R companion to
Statistics: data analysis and modelling" by Speekenbrink, M. (2021)
<https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets
analysed in these books are provided in this package. In addition, the
package provides functions to compute sample statistics (variance,
standard deviation, mode), create raincloud and enhanced Q-Q plots, and
expand Anova results into omnibus tests and tests of individual
contrasts.

Maarten Speekenbrink

sdamr

Statistics: Data Analysis and Modelling

rps function

An object of class <code>data.frame</code> with 2600 rows and 6 columns.

Format

\@format A data frame with 2600 observations of 6 variables:<dl>
<dt>id</dt>
<dd>(factor) Participant ID</dd>

<dt>ai_strategy</dt>
<dd>(factor) Strategy adopted by AI player</dd>

<dt>round</dt>
<dd>(numeric) Round number (between 1 and 50)</dd>

<dt>human_action</dt>
<dd>(factor) Action taken by human (rock, paper, or scissors)</dd>

<dt>ai_action</dt>
<dd>(factor) Action taken by AI (rock, paper, or scissors)</dd>

<dt>score</dt>
<dd>(numeric) Outcome for human player, where 1 indicates a win, -1 a loss, and 0 a tie</dd>


</dl>

Data from Experiment 1 in Guennouni, I., Speekenbrink, M. (2022). Transfer of learned opponent models in repeated games. Computational Brain and Behaviour, 5, 326–342 tools:::Rd_expr_doi("10.1007/s42113-022-00133-6"). Participants (n=52) each play 50 rounds of Rock-Paper-Scissors against an AI player who either adopts a "level-1" or "level-2" strategy. A level-1 strategy assumes the opponent will repeat their last action, and chooses the action that beats this. A level-2 strategy assumes the opponent adopts a level-1 strategy, and chooses the action that beats this. On 10% of rounds, the AI players pick a random action. On the remainder, they act according to their strategy. — rps

Data from Experiment 1 in Guennouni, I., Speekenbrink, M. (2022). Transfer of learned opponent models in repeated games. Computational Brain and Behaviour, 5, 326–342 tools:::Rd_expr_doi("10.1007/s42113-022-00133-6"). Participants (n=52) each play 50 rounds of Rock-Paper-Scissors against an AI player who either adopts a "level-1" or "level-2" strategy. A level-1 strategy assumes the opponent will repeat their last action, and chooses the action that beats this. A level-2 strategy assumes the opponent adopts a level-1 strategy, and chooses the action that beats this. On 10% of rounds, the AI players pick a random action. On the remainder, they act according to their strategy.

Description

Usage

Arguments

Format