Description Usage Arguments Details Value Author(s) References Examples

Calculates an AMOVA based on the genetic distance matrix from stamppNeisD() using the amova() function from the package PEGAS for exploring within and between population variation.

1 | ```
stamppAmova(dist.mat, geno, perm = 100)
``` |

`dist.mat` |
the matrix of genetic distances between individuals generated from stamppNeisD() |

`geno` |
a data frame containing allele frequency data generated from stamppConvert, or a genlight object containing genotype data, individual IDs, population IDs and ploidy levels. |

`perm` |
the number of permutations for the tests of hypotheses. |

Uses the formula distance ~ populations, to calculate an AMOVA for population differentiation and within & between population variation.

This function uses the amova function from the PEGAS package.

An object of class "amova" which is a list containing a table of sum of square deviations (SSD), mean square deviations (MSD) and the number of degrees of freedom as well as the variance components

LW Pembleton

Paradis E (2010) pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419-420. <doi:10.1093/bioinformatics/btp696>

1 2 3 4 5 6 7 8 9 | ```
# import genotype data and convert to allele frequecies
data(potato.mini, package="StAMPP")
potato.freq <- stamppConvert(potato.mini, "r")
# Calculate genetic distance between individuals
potato.D.ind <- stamppNeisD(potato.freq, FALSE)
# Calculate AMOVA
stamppAmova(potato.D.ind, potato.freq, 100)
``` |

```
Loading required package: pegas
Loading required package: ape
Loading required package: adegenet
Loading required package: ade4
/// adegenet 2.1.1 is loaded ////////////
> overview: '?adegenet'
> tutorials/doc/questions: 'adegenetWeb()'
> bug reports/feature requests: adegenetIssues()
Attaching package: 'pegas'
The following object is masked from 'package:ade4':
amova
The following object is masked from 'package:ape':
mst
Analysis of Molecular Variance
Call: amova(formula = dist ~ pop.names, nperm = perm)
SSD MSD df
pop.names 0.10701827 0.053509133 2
Error 0.01204481 0.004014937 3
Total 0.11906308 0.023812616 5
Variance components:
sigma2 P.value
pop.names 0.0247471 0.0792
Error 0.0040149
Phi-statistics:
pop.names.in.GLOBAL
0.8604084
Variance coefficients:
a
2
```

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