When used for demographic inference, Jaatha supports using the package coala
as simulation engine. If these packages do not suit your needs, it is of course also possible to use the normal interface described in the Introduction
vignette.
Jaatha automatically creates a simulation function, parameter ranges and summary statistics from a coala
model. We can for example specify a simple isolation-with-migration model using par_range
s to mark parameters we want to estimate with Jaatha:
if(require("coala")) {
model <- coal_model(c(10, 15), 100) +
feat_mutation(par_range("theta", 1, 10)) +
feat_migration(par_range("m", 0, 3), symmetric = TRUE) +
feat_pop_merge(par_range("t_split", 0.1, 2), 2, 1) +
feat_recombination(1) +
sumstat_jsfs()
}
## Lade nötiges Paket: coala
We can now just pass this coala
model to the create_jaatha_model
function to convert it into a Jaatha model:
## A simulation takes less than a second
This uses coala
for the simulations, gets the parameter ranges specified with par_range
and uses summary statistics added to the model. Coala supports a wide range of models. Please refer to its documentation for more information.
You can use coala’s calc_sumstats_form_data
function to calculate the summary statistic for genetic data. The output of this function can be directly passed on to create_jaatha_data
.
From here on, you can estimate parameters using the jaatha
as described in the introduction vignette.
If you are using a simulator that is writing temporary files to disk (e.g. ms
, msms
and seq-gen
), please make sure that there is sufficient free space on your tempdir()
to store the output of sim
simulations per core that you use (arguments sim
and cores
in the jaatha
function). Also, please make sure that your machine does not run out of memory. Both will lead to failtures during the estimation process. Reducing the number of cores reduces both the required memory and disk space at the cost of a longer runtime.