Description Usage Arguments Value See Also Examples

Combines a list of (population) permutations and a list of graph topologies
to a big list of graphs, then fits those graphs to given data using parallel
computation. This function needs `doParallel`

, `foreach`

and
`parallel`

installed.

1 | ```
fit_permutations_and_graphs(data, permutations, graphs, cores)
``` |

`data` |
The data table. |

`permutations` |
List of population permutations. |

`graphs` |
List of functions for producing graphs. |

`cores` |
Number of cores used. |

A list of `fast_fit`

results.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
# Let's experiment by fitting all the graphs with five leaves and at most one admixture
# event to a five population subset of the bear data. Note that with three data rows only
# we do wisely by not concluding too much about the actual bear family tree; this is to
# illustrate the function usage only!
data(bears)
data <- bears[16:18, ]
print(data)
permutations <- make_permutations(c("PB", "BLK", "Sweden", "Denali", "Kenai"))
graphs <- five_leaves_graphs
# We go with one core only as I don't know what kind of machine you are using.
fitted_graphs <- fit_permutations_and_graphs(data, permutations, graphs, 1)
# Now sort the fitted objects by best_error and see how the best graph looks like.
errors <- sapply(fitted_graphs, function(x) x$best_error)
best_graphs <- fitted_graphs[order(errors)]
plot(best_graphs[[1]]$graph, color = "goldenrod", title = best_graphs[[1]]$best_error)
# The same value for best_error actually occurs in the list 152 times because of our
# unsufficient data.
``` |

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