AMOVA with single stratum data

[Martin Boys]

Good Evening All!

May someone please explain to me step by step how I  can do AMOVA with data that only has one stratum without having to stratify the data, especially when the data is too difficult to assign strata to. I read the paper by Kamvar et al on population genetics and genomics but, the demonstrations were based on stratified data only. I will really appreciate your inputs on this and I thank you wholeheartedly in advance.

Kindest regards

Martin

[Zhian Kamvar]

Hi Martin,

You can assign a single stratum to your data by using the `strata(myData) <- dataFrame` syntax. 
If you already have a population factor for your data (you can check this by running `nPop(myData) > 0` to make sure you have a population set), then you can assign the strata with that.

Here's an example using the nancycats data set. I've highlighted the line that sets the strata in bold:

library("poppr")
data(nancycats) # embedded data set of cat colonies from Nancy, FR
strata(nancycats) <- data.frame(colony = pop(nancycats)) # create a strata from the one population factor
poppr.amova(nancycats, ~colony) # run AMOVA, using the colony as the single stratum
#> 
#> Found 617 missing values.
#> 
#> 2 loci contained missing values greater than 5%
#> 
#> Removing 2 loci: fca8, fca45
#> Warning in is.euclid(xdist): Zero distance(s)
#> Distance matrix is non-euclidean.
#> Using quasieuclid correction method. See ?quasieuclid for details.
#> Warning in is.euclid(distmat): Zero distance(s)
#> $call
#> ade4::amova(samples = xtab, distances = xdist, structures = xstruct)
#> 
#> $results
#>                                Df    Sum Sq   Mean Sq
#> Between colony                 16  288.5021 18.031384
#> Between samples Within colony 220 1235.8990  5.617723
#> Within samples                237 1015.6256  4.285340
#> Total                         473 2540.0268  5.370035
#> 
#> $componentsofcovariance
#>                                               Sigma          %
#> Variations  Between colony                0.4476284   8.290705
#> Variations  Between samples Within colony 0.6661912  12.338794
#> Variations  Within samples                4.2853402  79.370501
#> Total variations                          5.3991598 100.000000
#> 
#> $statphi
#>                           Phi
#> Phi-samples-total  0.20629499
#> Phi-samples-colony 0.13454246
#> Phi-colony-total   0.08290705

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