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Thibaut,<br>
Thanks for your response to my query about the missing values and
Global R test.<br>
<br>
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.<br>
<br>
<br>
<br>
Thanks<br>
<br>
Nevil Amos<br>
<br>
<img src="cid:part1.00000102.00060307@sci.monash.edu.au" alt=""><br>
<blockquote
cite="mid:1EC92C6D037ADB42A55C4F99B7F96C8814F118D1@ICEXM4.ic.ac.uk"
type="cite">
<pre wrap="">
> 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")
</pre>
</blockquote>
<br>
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