Cheatsheet for dplyr join functions

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This allows a decision maker to have a higher error rate on one gamble pair as long as they have a lower error rate on another gamble pair. Note that the tests in Table 2 are carried out in a 10D space whose coordinates are the 10 gamble pairs, whereas we only consider the 3D projection for Gambles Pairs 2, 3, and 8 in these figures.

In other words, the tests in Table 2 are somewhat more complicated than the illustrative figures convey. For one thing, there are equally many parameters (binary merge data frames in r dplyr probabilities) as there are empirical cells (binary choice proportions), yet we can merge data frames in r dplyr from the figures that the models can be extremely restrictive, especially in highdimensional spaces, and hence, must be testable.

As explained in Davis-Stober and Brown (2011) one cannot simply count parameters to evaluate the complexity of these types of models.

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