simulate_simpson {bayestestR} | R Documentation |

## Simpson's paradox dataset simulation

### Description

Simpson's paradox, or the Yule-Simpson effect, is a phenomenon in probability and statistics, in which a trend appears in several different groups of data but disappears or reverses when these groups are combined.

### Usage

```
simulate_simpson(
n = 100,
r = 0.5,
groups = 3,
difference = 1,
group_prefix = "G_"
)
```

### Arguments

`n` |
The number of observations for each group to be generated (minimum 4). |

`r` |
A value or vector corresponding to the desired correlation coefficients. |

`groups` |
Number of groups (groups can be participants, clusters, anything). |

`difference` |
Difference between groups. |

`group_prefix` |
The prefix of the group name (e.g., "G_1", "G_2", "G_3", ...). |

### Value

A dataset.

### Examples

```
data <- simulate_simpson(n = 10, groups = 5, r = 0.5)
if (require("ggplot2")) {
ggplot(data, aes(x = V1, y = V2)) +
geom_point(aes(color = Group)) +
geom_smooth(aes(color = Group), method = "lm") +
geom_smooth(method = "lm")
}
```

[Package

*bayestestR*version 0.13.2 Index]