Author: Paul Staab

# Demographic Inference with Jaatha

#### Paul Staab

#### jaatha 3.2.5

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.

## Creating a Model

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:

    if(requireNamespace("coala")) {
         library(jaatha)
         jaatha_model <- create_jaatha_model(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.

## Importing Data

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`.

## Running Jaatha

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.