summaryTable: Create a Summary Table of the Stay Probabilities
In jdtrat/dynaq: Tools to Simulate DynaQ Reinforcement Learning Algorithms

This function creates a summary table of the mean, standard deviation, minimum, and maximum stay probabilities based on the predictions from a logistic regression. It is specific to each reward and transition structure combination.

1	summaryTable(data)

data

Data is a single dataframe that has had the manipulateData function applied to it. Should be a dataframe with multiple agents.

alpha gamma reward transition meanPredStay stdPredStay minPredStay maxPredStay

A dataframe with 8 coiumns and variable rows depending on the combinations of the learning rate, alpha, and temporal discount factor, gamma:

alpha: Numeric – learning rate, alpha.
gamma: Numeric – temporal discount factor, gamma.
reward: Factor – yes or no.
transition: Factor – common or rare transition type.
meanPredStay: Numeric – the mean predicted probability of staying.
stdPredStay: Numeric – the standard deviation of the predicted probability of staying.
minPredStay: Numeric – the minimum predicted probability of staying.
maxPredStay: Numeric – the maximum predicted probability of staying.