Description Usage Arguments Details Value Note Author(s) References See Also Examples
Calculate the probability of genotypes based on the product of allele frequencies over all loci.
1 
gid 
a genind or genclone object. 
pop 
either a formula to set the population factor from the

by_pop 
When this is 
log 
a 
freq 
a vector or matrix of allele frequencies. This defaults to

... 
options from correcting rare alleles. The default is to correct allele frequencies to 1/n 
Pgen is the probability of a given genotype occuring in a population assuming HWE. Thus, the value for diploids is
pgen = prod(p_i)*(2^h)
where p_i are the allele frequencies and h is the count of the number of heterozygous sites in the sample (ArnaudHaond et al. 2007; Parks and Werth, 1993). The allele frequencies, by default, are calculated using a roundrobin approach where allele frequencies at a particular locus are calculated on the clonecensored genotypes without that locus.
To avoid issues with numerical precision of small numbers, this function calculates pgen per locus by adding up logtransformed values of allele frequencies. These can easily be transformed to return the true value (see examples).
A vector containing Pgen values per locus for each genotype in the object.
For haploids, Pgen at a particular locus is the allele frequency. This
function cannot handle polyploids. Additionally, when the argument
pop
is not NULL
, by_pop
is automatically TRUE
.
Zhian N. Kamvar, Jonah Brooks, Stacy A. KruegerHadfield, Erik Sotka
ArnaudHaond, S., Duarte, C. M., Alberto, F., & Serrão, E. A. 2007. Standardizing methods to address clonality in population studies. Molecular Ecology, 16(24), 51155139.
Parks, J. C., & Werth, C. R. 1993. A study of spatial features of clones in a population of bracken fern, Pteridium aquilinum (Dennstaedtiaceae). American Journal of Botany, 537544.
psex
, rraf
, rrmlg
,
rare_allele_correction
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  data(Pram)
head(pgen(Pram, log = FALSE))
## Not run:
# You can also supply the observed allele frequencies
pramfreq < Pram %>% genind2genpop() %>% tab(freq = TRUE)
head(pgen(Pram, log = FALSE, freq = pramfreq))
# You can get the Pgen values over all loci by summing over the logged results:
pgen(Pram, log = TRUE) %>% # calculate pgen matrix
rowSums(na.rm = TRUE) %>% # take the sum of each row
exp() # take the exponent of the results
# You can also take the product of the nonlogged results:
apply(pgen(Pram, log = FALSE), 1, prod, na.rm = TRUE)
## Rare Allele Correction 
##
# If you don't supply a table of frequencies, they are calculated with rraf
# with correction = TRUE. This is normally benign when analyzing large
# populations, but it can have a great effect on small populations. To help
# control this, you can supply arguments described in
# help("rare_allele_correction").
# Default is to correct by 1/n per population. Since the calculation is
# performed on a smaller sample size due to round robin clone correction, it
# would be more appropriate to correct by 1/rrmlg at each locus. This is
# acheived by setting d = "rrmlg". Since this is a diploid, we would want to
# account for the number of chromosomes, and so we set mul = 1/2
head(pgen(Pram, log = FALSE, d = "rrmlg", mul = 1/2)) # compare with the output above
# If you wanted to treat all alleles as equally rare, then you would set a
# specific value (let's say the rare alleles are 1/100):
head(pgen(Pram, log = FALSE, e = 1/100))
## End(Not run)

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