amova {ade4}R Documentation

Analysis of molecular variance

Description

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.

Usage

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

Arguments

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

Value

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

Author(s)

Sandrine Pavoine pavoine@biomserv.univ-lyon1.fr

References

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.

See Also

randtest.amova

Examples

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

Worked out examples


> 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

> 
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[Package ade4 Index]