Closed akang1414 closed 5 years ago
I am puzzled, if I run it on a toy example:
x<-matrix(sample(9,size=1000,replace=TRUE),nrow=100)
pop<-rep(1:10,each=10)
pairwise.neifst(cbind(pop,x),diploid=FALSE)
it works:
1 2 3 4 5 6 7 8 9 10
1 NA -0.0023 -0.0101 0.0066 0.0062 0.0119 -0.0054 -0.0066 0.0009 -0.0102
2 -0.0023 NA -0.0014 0.0040 0.0169 0.0126 -0.0080 -0.0126 -0.0118 0.0007
3 -0.0101 -0.0014 NA 0.0140 0.0278 -0.0075 0.0056 -0.0057 -0.0128 -0.0025
4 0.0066 0.0040 0.0140 NA -0.0010 -0.0066 -0.0093 -0.0219 -0.0176 -0.0084
5 0.0062 0.0169 0.0278 -0.0010 NA 0.0221 -0.0214 -0.0030 -0.0056 -0.0043
6 0.0119 0.0126 -0.0075 -0.0066 0.0221 NA 0.0118 -0.0212 -0.0261 0.0170
7 -0.0054 -0.0080 0.0056 -0.0093 -0.0214 0.0118 NA -0.0169 -0.0230 0.0010
8 -0.0066 -0.0126 -0.0057 -0.0219 -0.0030 -0.0212 -0.0169 NA -0.0415 -0.0288
9 0.0009 -0.0118 -0.0128 -0.0176 -0.0056 -0.0261 -0.0230 -0.0415 NA -0.0129
10 -0.0102 0.0007 -0.0025 -0.0084 -0.0043 0.0170 0.0010 -0.0288 -0.0129 NA
Could you post a toy example with your problem?
> x<-matrix(sample(9,size=1000,replace=TRUE),nrow=100)
> pop<-rep(1:10,each=10)
> pairwise.neifst(cbind(pop,x),diploid=FALSE)
Doesn't work
1 2 3 4 5 6 7 8 9 10
1 NA NA NA NA NA NA NA NA NA NA
2 NA NA NA NA NA NA NA NA NA NA
3 NA NA NA NA NA NA NA NA NA NA
4 NA NA NA NA NA NA NA NA NA NA
5 NA NA NA NA NA NA NA NA NA NA
6 NA NA NA NA NA NA NA NA NA NA
7 NA NA NA NA NA NA NA NA NA NA
8 NA NA NA NA NA NA NA NA NA NA
9 NA NA NA NA NA NA NA NA NA NA
10 NA NA NA NA NA NA NA NA NA NA
Which version of hierfstat are you running? ( SessionInfo() )
hierfstat_0.04-22
Install the latest version (0.04-29):
library(devtools)
install_github("jgx65/hierfstat")
library("hierfstat")
and retry please
Works fine with the new version! For some combinations I see very high FST values (0.91) with both Nei and WC methods. Can I say two populations are very different?
I don't know the biological underpinning but indeed, these values are high. Regards
I am calculating pairwise.neifst for a haploid organism like below
pairwise.neifst(data, diploid=FALSE)
It returns NA for all the comparisons. But if i remove diploid then it does give out some values.
pairwise.neifst(data)
How do I fix this? Thanks!