visualize_c_map | R Documentation |

This function displays a visualization of the possible bias c that allows for a non-zero effect in sensitivity. This function includes the ability to add values of effect size and correlation to see how they map onto the proposed c value.

```
visualize_c_map(
dlow,
r_values,
d_values = NULL,
f_values = NULL,
f2_values = NULL,
nnt_values = NULL,
prob_values = NULL,
prop_u1_values = NULL,
prop_u2_values = NULL,
prop_u3_values = NULL,
prop_overlap_values = NULL,
lower = TRUE
)
```

`dlow` |
The lower limit of the possible effect size (required). |

`r_values` |
A vector of correlation values that are possible (required). |

`d_values` |
A vector of effect size values that are possible. |

`f_values` |
A vector of f effect size values that are possible. |

`f2_values` |
A vector of f2 effect size values that are possible. |

`nnt_values` |
A vector of number needed to treat effect size values that are possible. |

`prob_values` |
A vector of probability of superiority effect size values that are possible. |

`prop_u1_values` |
A vector of proportion of overlap u1 effect size values that are possible. |

`prop_u2_values` |
A vector of proportion of overlap u2 effect size values that are possible. |

`prop_u3_values` |
A vector of proportion of overlap u3 effect size values that are possible. |

`prop_overlap_values` |
A vector of proportion of distribution overlap effect size values that are possible. |

`lower` |
Use this to indicate if you want the lower or upper bound
of d for one sided confidence intervals. If d is positive, you generally
want |

Returns a pretty graph of the possible effect size and correlation combinations with the region of effect colored in. Note that all effect sizes are converted to d for the graph.

`graph` |
The graph of possible values for c |

```
visualize_c_map(dlow = .25,
d_values = c(.2, .3, .8),
r_values = c(.1, .4, .3),
lower = TRUE)
```

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