# Traditional, non-spatial models

The biggest selling point of the slendr package is that you can program spatiotemporal population genetics models in R and have them execute automatically in SLiM. However, there are several reasons why you might be interested in using slendr even for non-spatial models. First, R is a language that many scientists already know, and being able to simulate data from the comfort of an R interface significantly lowers the barrier of entry. Second, because slendr makes SLiM appear almost as if it were just another R library, running simulations (spatial and non-spatial), fitting models, exploring parameter grids, calculating statistics, and visualization of results can be all be performed without leaving the R interface.

In this vignette, we will demonstrate how to program non-spatial models in slendr. However, we should start by noting that there is almost no difference between code for non-spatial and spatial models in slendr. The only visible difference is that spatial models include a `map =` argument in the `population()` constructor function of ancestral population(s), and non-spatial models do not. That’s it, that’s the difference. Switching between spatial and non-spatial models is performed internally by the package, without any user intervention.

To make the comparison clearer, we will use the example from the slendr landing page, but we will implement it in a non-spatial context (i.e., as a traditional random mating simulation).

First, let’s define population objects, splits, and other demographic events (note the missing `map` argument, which is set to `FALSE` by default):

    library(slendr)

    init_env()

    #> The interface to all required Python modules has been activated.

    # African ancestral population
    afr <- population("AFR", time = 100000, N = 3000)

    # first migrants out of Africa
    ooa <- population("OOA", parent = afr, time = 60000, N = 500, remove = 23000) %>%
      resize(N = 2000, time = 40000, how = "step")

    # Eastern hunter-gatherers
    ehg <- population("EHG", parent = ooa, time = 28000, N = 1000, remove = 6000)

    # European population
    eur <- population("EUR", parent = ehg, time = 25000, N = 2000) %>%
      resize(N = 10000, how = "exponential", time = 5000, end = 0)

    # Anatolian farmers
    ana <- population("ANA", time = 28000, N = 3000, parent = ooa, remove = 4000)

    # Yamnaya steppe population
    yam <- population("YAM", time = 7000, N = 500, parent = ehg, remove = 2500)

We can define gene flow events in the same way as we did for the spatial model:

    gf <- list(
      gene_flow(from = ana, to = yam, proportion = 0.5, start = 6500, end = 6400),
      gene_flow(from = ana, to = eur, proportion = 0.5, start = 8000, end = 6000),
      gene_flow(from = yam, to = eur, proportion = 0.75, start = 4000, end = 3000)
    )

The compilation step is also the same. The only (internal) difference is that we skip the rasterization of vector maps that is performed for spatial models in order to control and restrict population boundaries:

    model <- compile_model(
      populations = list(afr, ooa, ehg, eur, ana, yam),
      gene_flow = gf, generation_time = 30,
      time_units = "years before present"
    )

Using the `plot_map()` function doesn’t make sense, as there are no spatial maps to plot. However, we can still plot the demographic graph, verifying that the model has been specified correctly using the function `plot_model()` as shown below.

Let’s say we also want to schedule specific sampling events at specific times (which record only specified individuals in a tree sequence). We can use `schedule_sampling()` to do just that:

    samples <- schedule_sampling(
      model,
      times = c(0, 5000, 12000, 20000, 35000, 39000, 43000),
      list(eur, 3), list(ehg, 1), list(yam, 1), list(ana, 3), list(ooa, 1), list(afr, 1)
    )

Because it’s not just the model itself that’s useful to visually verify (which is the main purpose of `plot_model()`) but also the sampling scheme, slendr makes it possible to overlay the numbers of individuals scheduled for tree-sequence recording from each lineage at each timepoint:

    plot_model(model, samples = samples)

Even the final step—execution of the model in SLiM—is the same, using the built-in `slim()` function:

    ts_slim <- slim(model, sequence_length = 100000, recombination_rate = 0)

Even for non-spatial models, this function still uses the same SLiM back end script used for spatial models. The only difference is that all spatial features are switched off, making the model run as a simple random-mating simulation.

Given that we are running a non-spatial simulation, you might wonder if it wouldn’t be more efficient to use a coalescent simulator. Indeed, slendr also provides an alternative msprime back end just for this purpose. We could run the exact same simulation with msprime like this:

    ts_msprime <- msprime(model, sequence_length = 100000, recombination_rate = 0)

In fact, because both SLiM and msprime back ends save outputs in a tree sequence format, we can analyse them using the same tools. See this vignette for more information about tree sequence analysis with slendr, and for more discussion on alternative simulation back ends and more extensive examples of data analysis with tree sequences you can read this tutorial.

## Extracting parameters from a model or tree sequences

In some situations (such as when model parameters are drawn from random distributions and we need to know which parameters had been used after the simulations), function `extract_parameters()` can be used. This function peeks into a slendr tree sequence object and extract parameters of the original slendr model:

    extract_parameters(ts_msprime)

    #> $splits
    #>   pop parent    N   time remove
    #> 1 AFR   <NA> 3000 100000     NA
    #> 2 OOA    AFR  500  60000  23000
    #> 3 EHG    OOA 1000  28000   6000
    #> 5 ANA    OOA 3000  28000   4000
    #> 4 EUR    EHG 2000  25000     NA
    #> 6 YAM    EHG  500   7000   2500
    #>
    #> $gene_flows
    #>   from  to start  end proportion
    #> 1  ANA YAM  6500 6400       0.50
    #> 2  ANA EUR  8000 6000       0.50
    #> 3  YAM EUR  4000 3000       0.75
    #>
    #> $resizes
    #>   pop         how     N  time end
    #> 1 OOA        step  2000 40000  NA
    #> 2 EUR exponential 10000  5000   0

For completeness, although this isn’t relevant in this example either, the function can also get parameters from any compiled model. For instance, we can check which parameters were used to compile the built-in slendr introgression model by running:

    introgression_model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))

    extract_parameters(introgression_model)

    #> $splits
    #>   pop parent    N    time remove
    #> 1  CH   <NA>   10 6500000     NA
    #> 2 AFR     CH   10 6000000     NA
    #> 3 NEA    AFR   10  600000  40000
    #> 4 EUR    AFR 5000   70000     NA
    #>
    #> $gene_flows
    #>   from  to start   end proportion
    #> 1  NEA EUR 55000 45000       0.03

As we can see, `extract_parameters()` returns a list of data frames, one data frame for each aspect of a demographic model (where applicable).

## Named samples

As an addendum, it is worth mentioning that in addition to automatically naming recorded samples according to the format of `"<population>_<number>"`, slendr supports uniquely named samples. For instance, imagine that we want to record an ancient individual representing a 45.000 years old hunter gatherer known as Ust’-Ishim (Fu, _et al., Nature, 2014). We could include this sample among our other, generically named, samples like this:

    schedule_amh <- schedule_sampling(
      model,
      times = c(0, 5000, 12000, 20000, 35000, 39000, 43000),
      list(eur, 3), list(ehg, 1), list(yam, 1), list(ana, 3), list(ooa, 1), list(afr, 1)
    )

    schedule_ui <- schedule_sampling(model, times = 45000, list(ooa, 1, "Ust_Ishim"))

    # bind the two tables together into a single schedule
    schedule <- rbind(schedule_amh, schedule_ui)

Then we would simulate just like before:

    ts <- msprime(model, sequence_length = 100000, recombination_rate = 0, samples = schedule)

When we inspect the table of all recorded individuals, we can see that the Ust’-Ishim individual is stored under its proper symbolic name:

    ts_samples(ts)

    #>         name  time pop
    #> 1  Ust_Ishim 45000 OOA
    #> 2      AFR_1 43000 AFR
    #> 3      OOA_1 43000 OOA
    #> 4      AFR_2 39000 AFR
    #> 5      OOA_2 39000 OOA
    #> 6      AFR_3 35000 AFR
    #> 7      OOA_3 35000 OOA
    #> 8      AFR_4 20000 AFR
    #> 9      ANA_1 20000 ANA
    #> 10     ANA_2 20000 ANA
    #> 11     ANA_3 20000 ANA
    #> 12     EHG_1 20000 EHG
    #> 13     EUR_1 20000 EUR
    #> 14     EUR_2 20000 EUR
    #> 15     EUR_3 20000 EUR
    #> 16     AFR_5 12000 AFR
    #> 17     ANA_4 12000 ANA
    #> 18     ANA_5 12000 ANA
    #> 19     ANA_6 12000 ANA
    #> 20     EHG_2 12000 EHG
    #> 21     EUR_4 12000 EUR
    #> 22     EUR_5 12000 EUR
    #> 23     EUR_6 12000 EUR
    #> 24     AFR_6  5000 AFR
    #> 25     ANA_7  5000 ANA
    #> 26     ANA_8  5000 ANA
    #> 27     ANA_9  5000 ANA
    #> 28     EUR_7  5000 EUR
    #> 29     EUR_8  5000 EUR
    #> 30     EUR_9  5000 EUR
    #> 31     YAM_1  5000 YAM
    #> 32     AFR_7     0 AFR
    #> 33    EUR_10     0 EUR
    #> 34    EUR_11     0 EUR
    #> 35    EUR_12     0 EUR

When we later compute summary statistics on tree sequences, this makes referring to specific individuals even more convenient:

    ts_f3(ts, A = "AFR_1", B = "EUR_1", C = "Ust_Ishim", mode = "branch")

    #> # A tibble: 1 × 4
    #>   A     B     C            f3
    #>   <chr> <chr> <chr>     <dbl>
    #> 1 AFR_1 EUR_1 Ust_Ishim 4367.