# Comparing simulations against theoretical expectations

`gl.diagnostics.sim.Rd`

Comparing simulations against theoretical expectations

## Usage

```
gl.diagnostics.sim(
x,
Ne,
iteration = 1,
pop_he = 1,
pops_fst = c(1, 2),
plot_theme = theme_dartR(),
save2tmp = FALSE,
verbose = NULL
)
```

## Arguments

- x
Output from function

`gl.sim.WF.run`

[required].- Ne
Effective population size to use as input to compare theoretical expectations [required].

- iteration
Iteration number to analyse [default 1].

- pop_he
Population name in which the rate of loss of heterozygosity is going to be compared against theoretical expectations [default 1].

- pops_fst
Pair of populations in which FST is going to be compared against theoretical expectations [default c(1,2)].

- plot_theme
User specified theme [default theme_dartR()].

- save2tmp
If TRUE, saves any ggplots and listings to the session temporary directory (tempdir) [default FALSE].

- verbose
Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log ; 3, progress and results summary; 5, full report [default NULL, unless specified using gl.set.verbosity].

## Details

Two plots are presented comparing the simulations against theoretical expectations:

Expected heterozygosity under neutrality (Crow & Kimura, 1970, p. 329) is calculated as:

Het = He0(1-(1/2Ne))^t,

where Ne is effective population size, He0 is heterozygosity at generation 0 and t is the number of generations.

Expected FST under neutrality (Takahata, 1983) is calculated as:

FST=1/(4Nem(n/(n-1))^2+1),

where Ne is effective populations size of each individual subpopulation, m is dispersal rate and n the number of subpopulations (always 2).

## References

Crow JF, Kimura M. An introduction to population genetics theory. An introduction to population genetics theory. 1970.

Takahata N. Gene identity and genetic differentiation of populations in the finite island model. Genetics. 1983;104(3):497-512.

## Author

Custodian: Luis Mijangos -- Post to https://groups.google.com/d/forum/dartr