Creates a site frequency spectrum based on a dartR or genlight object

Usage

gl.sfs(
  x,
  minbinsize = 0,
  folded = TRUE,
  singlepop = FALSE,
  plot.out = TRUE,
  verbose = NULL
)

Arguments

x: dartR/genlight object
minbinsize: remove bins from the left of the sfs. For example to remove singletons (alleles only occurring once among all individuals) set minbinsize to 2. If set to zero, also monomorphic (d0) loci are returned.
folded: if set to TRUE (default) a folded sfs (minor allele frequency sfs) is returned. If set to FALSE then an unfolded (derived allele frequency sfs) is returned. It is assumed that 0 is homozygote for the reference and 2 is homozygote for the derived allele. So you need to make sure your coding is correct.
singlepop: switch to force to create a one-dimensional sfs, even though the genlight/dartR object contains more than one population
plot.out: Specify if plot is to be produced [default TRUE].
verbose: Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log ; 3, progress and results summary; 5, full report [default 2, unless specified using gl.set.verbosity].

Value

returns a site frequency spectrum, either a one dimensional vector (only a single population in the dartR/genlight object or singlepop=TRUE) or an n-dimensional array (n is the number of populations in the genlight/dartR object). If the dartR/genlight object consists of several populations the multidimensional site frequency spectrum for each population is returned [=a multidimensional site frequency spectrum]. Be aware the multidimensional spectrum works only for a limited number of population and individuals [if too high the table command used internally will through an error as the number of populations and individuals (and therefore dimensions) are too large]. To get a single sfs for a genlight/dartR object with multiple populations, you need to set singlepop to TRUE. The returned sfs can be used to analyse demographics, e.g. using fastsimcoal2.

References

Excoffier L., Dupanloup I., Huerta-Sánchez E., Sousa V. C. and Foll M. (2013) Robust demographic inference from genomic and SNP data. PLoS genetics 9(10)

Author

Custodian: Bernd Gruber & Carlo Pacioni (Post to https://groups.google.com/d/forum/dartr)

Examples

gl.sfs(bandicoot.gl, singlepop=TRUE)
#> Starting gl.sfs 
#>   Processing genlight object with SNP data
#> Your data contains missing data, better to use gl.impute to fill those gaps meaningful!
#> 

#> Completed: gl.sfs 
#> 
#>  d0  d1  d2  d3  d4  d5  d6  d7  d8  d9 d10 d11 d12 d13 d14 d15 d16 d17 d18 d19 
#>   8   4   0   1   6   8   6   8   8   5   8   6   9  10   9   8   6  12   8  14 
#> d20 d21 d22 d23 d24 d25 d26 d27 d28 d29 d30 d31 d32 d33 d34 d35 d36 d37 d38 d39 
#>  12  20  17  14  11  18   8  12  15  13  12  13  10  17  11  18  14  14  19   7 
#> d40 d41 d42 d43 d44 d45 d46 d47 d48 d49 d50 d51 d52 d53 d54 d55 d56 d57 d58 d59 
#>  15   8   7  12  21  15  14  16  10  13   8  11  10  18   9  13   8  12  11   7 
#> d60 d61 d62 d63 d64 d65 d66 d67 d68 d69 d70 d71 d72 d73 d74 d75 d76 d77 d78 d79 
#>  11  14  15  10  12   6  17  12  14   6  11  10  12  13  12   9   3   8   6  11 
#> d80 d81 d82 d83 d84 d85 d86 d87 d88 d89 d90 d91 d92 d93 d94 d95 d96 
#>   4   7   9   6   7   8   6  14  10   8   4  10  13   6   5  10   4 
gl.sfs(possums.gl[c(1:5,31:33),], minbinsize=1)
#> Starting gl.sfs 
#>   Processing genlight object with SNP data
#> The sfs has more than 2 dimensions, therefore no plot is returned
#> Completed: gl.sfs 
#> 
#>      B
#> A     d1 d2 d3 d4 d5 d6
#>   d1  12  4  6  0  0  0
#>   d2   2 14  5  0  0  0
#>   d3  13 18 11  0  0  0
#>   d4  11 15  8  0  0  0
#>   d5   5  8  5  0  0  0
#>   d6   0  0  0  0  0  0
#>   d7   0  0  0  0  0  0
#>   d8   0  0  0  0  0  0
#>   d9   0  0  0  0  0  0
#>   d10  0  0  0  0  0  0