Description Usage Arguments Value See Also Examples
Given a table of observed f statistics and a graph, uses Nelder-Mead algorithm to
find the graph parameters (edge lengths and admixture proportions) that minimize the value
of cost_function
, i. e. maximizes the likelihood of a graph with
parameters given the observed data.
Like fast_fit
but outputs a more detailed analysis on the results.
1 2 3 4 5 6 | fit_graph(data, graph, point = list(rep(1e-05,
length(extract_graph_parameters(graph)$admix_prop)), rep(1 - 1e-05,
length(extract_graph_parameters(graph)$admix_prop))), Z.value = TRUE,
concentration = calculate_concentration(data, Z.value),
optimisation_options = NULL, parameters = extract_graph_parameters(graph),
iteration_multiplier = 3, qr_tol = 1e-08)
|
data |
The data table, must contain columns |
graph |
The admixture graph (an |
point |
If the user wants to restrict the admixture proportions somehow, like to fix some of them. A list of two vectors: the lower and the upper bounds. As a default the bounds are just it little bit more than zero and less than one; this is because sometimes the infimum of the values of cost function is at a point of non-continuity, and zero and one have reasons to be problematic values in this respect. |
Z.value |
Whether we calculate the default concentration from Z scores
(the default option |
concentration |
The Cholesky decomposition of the inverted covariance matrix.
Default matrix determined by the parameter |
optimisation_options |
Options to the Nelder-Mead algorithm. |
parameters |
In case one wants to tweak something in the graph. |
iteration_multiplier |
Given to |
qr_tol |
Given to |
A class agraph_fit
list containing a lot of information about the fit:
data
is the input data,
graph
is the input graph,
matrix
is the output of build_edge_optimisation_matrix
,
containing the full
matrix, the column_reduced
matrix without zero
columns, and graph parameters
,
complaint
coding wchich subsets of admixture proportions are trurly fitted,
best_fit
is the optimal admixture proportions (might not be unique if they
are not trurly fitted),
best_edge_fit
is an example of optimal edge lengths,
homogeneous
is the reduced row echelon form of the matrix describing when
a vector of edge lengths have no effect on the prediced statistics F,
free_edges
is one way to choose a subset of edge lengths in such a vector as
free variables,
bounded_edges
is how we calculate the reamining edge lengths from the free ones,
best_error
is the minimum value of the cost_function
,
approximation
is the predicted statistics F with the optimal graph parameters,
parameters
is jsut a shortcut for the graph parameters.
See summary.agraph_fit
for the interpretation of some of these results.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # For example, let's fit the following two admixture graph to an example data on bears:
data(bears)
print(bears)
leaves <- c("BLK", "PB", "Bar", "Chi1", "Chi2", "Adm1", "Adm2", "Denali", "Kenai", "Sweden")
inner_nodes <- c("R", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "M", "N")
edges <- parent_edges(c(edge("BLK", "R"),
edge("PB", "v"),
edge("Bar", "x"),
edge("Chi1", "y"),
edge("Chi2", "y"),
edge("Adm1", "z"),
edge("Adm2", "z"),
edge("Denali", "t"),
edge("Kenai", "s"),
edge("Sweden", "r"),
edge("q", "R"),
edge("r", "q"),
edge("s", "r"),
edge("t", "s"),
edge("u", "q"),
edge("v", "u"),
edge("w", "M"),
edge("x", "N"),
edge("y", "x"),
edge("z", "w"),
admixture_edge("M", "u", "t"),
admixture_edge("N", "v", "w")))
admixtures <- admixture_proportions(c(admix_props("M", "u", "t", "a"),
admix_props("N", "v", "w", "b")))
bears_graph <- agraph(leaves, inner_nodes, edges, admixtures)
plot(bears_graph, show_admixture_labels = TRUE)
fit <- fit_graph(bears, bears_graph)
summary(fit)
# It turned out the values of admixture proportions had no effect on the cost function. This is not
# too surprising because the huge graph contains a lot of edge variables compared to the tiny
# amount of data we used! Note however that the mere existence of the admixture event with non-
# trivial (not zero or one) admixture proportion might still decrease the cost function.
|
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