mmd {AnthropMMD} | R Documentation |

## Compute MMD values from a table of sample sizes and relative frequencies

### Description

Compute various MMD results, typically using a table returned by the
function `binary_to_table`

with the argument
`relative = TRUE`

.

### Usage

```
mmd(data, angular = c("Anscombe", "Freeman"), correct = TRUE,
all.results = TRUE)
```

### Arguments

`data` |
A table of sample sizes and frequencies |

`angular` |
Choice of a formula for angular transformation: either Anscombe or Freeman-Tukey transformation. |

`correct` |
Boolean; whether to apply the correction for small
sample sizes (should be |

`all.results` |
Boolean; whether to compute all four matrices
described below as results. If FALSE, only the matrix |

### Value

A list with four components:

`MMDMatrix` |
Following the presentation adopted in many research articles, a matrix filled with MMD values above the diagonal, and standard deviations of MMD below the diagonal. |

`MMDSym` |
A symmetrical matrix of MMD values, where negative values are replaced by zeroes. |

`MMDSignif` |
A matrix where any pair of traits having a significant MMD value is indicated by a star, ‘*’. |

`MMDpval` |
A matrix filled with MMD values above the diagonal, and p-values below the diagonal. |

### Author(s)

Frédéric Santos, frederic.santos@u-bordeaux.fr

### References

de Souza, P. and Houghton, P. (1977). The mean measure of divergence
and the use of non-metric data in the estimation of biological
distances. *Journal of Archaeological Science*, **4**(2),
163–169. doi: 10.1016/0305-4403(77)90063-2

Harris, E. F. and Sjøvold, T. (2004) Calculation of Smith's mean
measure of divergence for intergroup comparisons using nonmetric
data. *Dental Anthropology*, **17**(3), 83–93.

Nikita, E. (2015) A critical review of the mean measure of divergence
and Mahalanobis distances using artificial data and new approaches to
the estimation of biodistances employing nonmetric
traits. *American Journal of Physical Anthropology*, **157**,
284–294. doi: 10.1002/ajpa.22708

### See Also

### Examples

```
## Load and visualize a binary dataset:
data(toyMMD)
head(toyMMD)
## Convert this dataframe into a table of sample sizes and relative
## frequencies:
tab <- binary_to_table(toyMMD, relative = TRUE)
tab
## Compute and display a symmetrical matrix of MMD values:
mmd_out <- mmd(tab, angular = "Anscombe")
mmd_out$MMDSym
## Significant MMD values are indicated by a star:
mmd_out$MMDSignif
```

*AnthropMMD*version 4.0.3 Index]