amova {ade4} | R Documentation |

The analysis of molecular variance tests the differences among population and/or groups of populations in a way similar to ANOVA. It includes evolutionary distances among alleles.

amova(samples, distances, structures) ## S3 method for class 'amova': print(x, full = FALSE, ...)

`samples` |
a data frame with haplotypes (or genotypes) as rows, populations as columns and abundance as entries |

`distances` |
an object of class `dist` computed from Euclidean distance.
If `distances` is null, equidistances are used. |

`structures` |
a data frame containing, in the jth row and the kth column, the name of the group of level k to which the jth population belongs |

`x` |
an object of class `amova` |

`full` |
a logical value indicating whether the original data ('distances', 'samples', 'structures') should be printed |

`...` |
further arguments passed to or from other methods |

Returns a list of class `amova`

`call` |
call |

`results` |
a data frame with the degrees of freedom, the sums of squares, and the mean squares. Rows represent levels of variability. |

`componentsofcovariance` |
a data frame containing the components of covariance and their contribution to the total covariance |

`statphi` |
a data frame containing the phi-statistics |

Sandrine Pavoine pavoine@biomserv.univ-lyon1.fr

Excoffier, L., Smouse, P.E. and Quattro, J.M. (1992) Analysis of molecular variance inferred
from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction
data. *Genetics*, **131**, 479–491.

data(humDNAm) amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) amovahum

> library(ade4) > ### Name: amova > ### Title: Analysis of molecular variance > ### Aliases: amova print.amova > ### Keywords: multivariate > > ### ** Examples > > data(humDNAm) > amovahum <- amova(humDNAm$samples, sqrt(humDNAm$distances), humDNAm$structures) > amovahum $call amova(samples = humDNAm$samples, distances = sqrt(humDNAm$distances), structures = humDNAm$structures) $results Df Sum Sq Mean Sq Between regions 4 78.238115 19.5595288 Between samples Within regions 5 9.284744 1.8569488 Within samples 662 316.197379 0.4776395 Total 671 403.720238 0.6016695 $componentsofcovariance Sigma % Variations Between regions 0.13380659 21.119144 Variations Between samples Within regions 0.02213345 3.493396 Variations Within samples 0.47763955 75.387459 Total variations 0.63357958 100.000000 $statphi Phi Phi-samples-total 0.2461254 Phi-samples-regions 0.0442870 Phi-regions-total 0.2111914 > > > >

[Package *ade4* Index]