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For 20 samples from a panmictic population:
library(coala)
<- coal_model(20, 2000) +
model feat_mutation(2) +
feat_recombination(1) +
sumstat_tajimas_d()
<- simulate(model, seed = 15)
stats plot(density(stats$tajimas_d, na.rm = TRUE),
main = "Neutral Distribution of Tajiam's D")
For a neutral model with two populations and migration:
<- coal_model(c(10, 10), 100) +
model2a feat_mutation(10) +
feat_recombination(5) +
feat_migration(0.5, symmetric = TRUE) +
sumstat_sfs(population = "all")
<- simulate(model2a, seed = 20)
stats barplot(stats$sfs / sum(stats$sfs),
names.arg = seq_along(stats$sfs),
col = 3)
And again, but now with a bottleneck in one population:
<- model2a +
model2b feat_size_change(0.1, population = 2, time = 0.25) +
feat_size_change(1, population = 2, time = 0.5)
<- simulate(model2b, seed = 25)
stats barplot(stats$sfs / sum(stats$sfs),
names.arg = seq_along(stats$sfs),
col = 4)
<- coal_model(10, 50) +
model3 feat_mutation(par_prior("theta", sample.int(100, 1))) +
sumstat_nucleotide_div()
<- simulate(model3, nsim = 40)
stats <- sapply(stats, function(x) mean(x$pi))
mean_pi <- sapply(stats, function(x) x$pars[["theta"]])
theta plot(theta, mean_pi, pch = 19, col = "orange")
abline(lm(mean_pi ~ theta), col = "red2", lty = 3)
If you have a nice example for using coala, feel free to extend this vignette via a pull request on GitHub!
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.