[adegenet-forum] Testing how many vectors are significant using R test

[Nevil Amos]

Thibaut,
Thanks for your response to my query about the missing values and Global 
R test.

I have a number of datasets where more than one lambda stands out on the 
screeplot, thus there may be more than one global structure present.  
The Global R test allows me to test whether there is significant global 
structure in the data, however it is not clear to me how to determine 
the number of axes that should be interpreted, where more than on stands 
out from the rest.  A screeplot from the dataset appended to my last 
query is attached below, here it seems that both lambda 1 and 2 may 
stand out? In other cases the values are more marginal and a formal test 
of each would be useful.



Thanks

Nevil Amos


>
>
>  > myspca<-spca(EYRM.genind)
>
> Choose a connection network:
>          Delaunay triangulation (type 1)
>          Gabriel graph (type 2)
>          Relative neighbours (type 3)
>          Minimum spanning tree (type 4)
>          Neighbourhood by distance (type 5)
>          K nearest neighbours (type 6)
>          Inverse distances (type 7)
> Answer: 1
>
> Keep this graph (y/n)? y
> Select the first number of axes (>=1): 1
> Select the second number of axes (>=0): 0
> Warning message:
> In spca(EYRM.genind) :
>   NAs in data are automatically replaced (to mean allele frequency)
>  > plot(myspca)
>  > myGtest <- global.rtest(EYRM.genind$tab, myspca$lw, nperm = 999)
> Error in global.rtest(EYRM.genind$tab, myspca$lw, nperm = 999) :
>   NA entries in X
>  > save.image("d:\\nevs_docs\\Rwork\\spca\\NA_data.RData")
>   

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