Implements tools for building and visualising admixture graphs and for extracting equations from them. These equations can be compared to f- statistics obtained from data to test the consistency of a graph against data -- for example by comparing the sign of f_4-statistics with the signs predicted by the graph -- and graph parameters (edge lengths and admixture proportions) can be fitted to observed statistics.
|Author||Thomas Mailund [cre, aut], Kalle Leppala [aut], Svend Nielsen [aut]|
|Date of publication||2016-12-13 15:33:28|
|Maintainer||Thomas Mailund <firstname.lastname@example.org>|
add_a_leaf: Adds a new leaf to a graph.
add_an_admixture: Adds a new admixture event to a graph.
add_an_admixture2: Adds a new admixture event to a graph.
add_graph_f4: Evalutes the f_4 statistics for all rows in a data frame and...
add_graph_f4_sign: Extend a data frame with f_4 statistics predicted by a graph.
admix_props: Specify the proportions in an admixture event.
admixture_edge: Create an admixture edge from a child to two parents.
admixturegraph-package: admixturegraph: Visualising and analysing admixture graphs.
admixture_proportions: Create the list of admixture proportions for an admixture...
agraph: Create an admixture graph object.
agraph_children: Build the child incidene matrix from an parent edge list.
agraph_parents: Build the parent incidence matrix from an edge list.
agraph_weights: Build the matrix of admixture proportions from an edge list.
all_graphs: All graphs.
all_path_overlaps: Get the list of overlaps of all paths.
all_paths: Compute all paths from one leaf to another.
bears: Statistics for populations of bears
build_edge_optimisation_matrix: Build a matrix coding the linear system of edges once the...
burn_in: Removes the first k rows from a trace.
calculate_concentration: Building a proxy concentration matrix.
canonise_expression: Used to recognize similar expressions and to possibly...
canonise_graph: Canonise graph.
coef.agraph_fit: Parameters for the fitted graph.
cost_function: The cost function fed to Nelder-Mead.
edge: Create an edge from a child to a parent.
edge_optimisation_function: More detailed edge fitting than mere cost_function.
eight_leaves_trees: Eight leaves trees.
evaluate_f4: Evaluates an f_4 statistics in a given environment.
examine_edge_optimisation_matrix: Examine the edge optimisation matrix to detect unfitted admix...
extract_admixture_proportion_parameters: Extract the admixture proportion parameter from edge...
extract_graph_parameters: Extract all the parameters a graph contains.
extract_trees: Extract trees
f2: Calculate the f_2(A, B) statistics.
f3: Calculate the f_3(A; B, C) statistics.
f4: Calculate the f_4(W, X; Y, Z) statistics.
f4stats: Make a data frame an f_4 statistics object.
fast_fit: A fast version of graph fitting.
fast_plot: Fast version of graph plotting.
filter_on_leaves: Filter data so all W, X, Y and Z are leaves in the graph.
fit_graph: Fit the graph parameters to a data set.
fit_graph_list: Fit lots of graphs to data.
fit_permutations_and_graphs: Fit lots of graphs to data.
fitted.agraph_fit: Predicted f statistics for the fitted graph.
five_leaves_graphs: Five leaves graphs.
format_path: Create a path data frame from a list of nodes.
four_leaves_graphs: Four leaves graphs.
get_graph_f4_sign: Extracts the sign for the f_4 statistics predicted by the...
graph_environment: Build an environment in which f statistics can be evaluated.
graphs_2_0: Admixture graphs of 2 leaves and 0 admixture events...
graphs_3_0: Admixture graphs of 3 leaves and 0 admixture events...
graphs_3_1: Admixture graphs of 3 leaves and 1 admixture event compressed...
graphs_4_0: Admixture graphs of 4 leaves and 0 admixture events...
graphs_4_1: Admixture graphs of 4 leaves and 1 admixture event compressed...
graphs_4_2: Admixture graphs of 4 leaves and 2 admixture events...
graphs_5_0: Admixture graphs of 5 leaves and 0 admixture events...
graphs_5_1: Admixture graphs of 5 leaves and 1 admixture event compressed...
graphs_5_2: Admixture graphs of 5 leaves and 2 admixture events...
graphs_6_0: Admixture graphs of 6 leaves and 0 admixture events...
graphs_6_1: Admixture graphs of 6 leaves and 1 admixture event compressed...
graphs_6_2: Admixture graphs of 6 leaves and 2 admixture events...
graphs_7_0: Admixture graphs of 7 leaves and 0 admixture events...
graphs_7_1: Admixture graphs of 7 leaves and 1 admixture event compressed...
graphs_8_0: Admixture graphs of 8 leaves and 0 admixture events...
graph_to_vector: Graph to vector.
is_descendant_of: Is descendant of.
is_negative: All overlaps are either empty or have a negative weight.
is_positive: All overlaps are either empty or have a positive weight.
is_unknown: Overlapping edges have both positive and negative...
is_zero: All overlaps are empty.
log_likelihood: Calculate (essentially) the log likelihood of a graph with...
log_sum_of_logs: Computes the log of a sum of numbers all given in log-space.
make_an_outgroup: Make an outgroup.
make_mcmc_model: Collect the information about a graph and a data set needed...
make_permutations: List of permutations.
model_bayes_factor_n: Computes the Bayes factor between two models from samples...
model_likelihood: Computes the likelihood of a model from samples from its...
model_likelihood_n: Computes the likelihood of a model from samples from its...
mynonneg: Non negative least square solution.
no_admixture_events: Get the number of admixture events in a graph.
no_admixture_events.agraph: Get the number of admixture events in a graph.
no_admixture_events.agraph_fit: Get the number of admixture events in a fitted graph.
no_admixture_events.agraph_fit_list: Get the number of admixture events in a list of fitted graph.
no_poor_fits: Get the number of tests in the fit where the predictions fall...
no_poor_fits.agraph_fit: Get the number of tests in the fit where the predictions fall...
no_poor_fits.agraph_fit_list: Get the number of tests in the fit where the predictions fall...
overlaps_sign: Get the sign of overlapping paths.
parent_edges: Create the list of edges for an admixture graph.
path_overlap: Collect the postive and negative overlap between two paths.
plot.agraph: Plot an admixture graph.
plot.agraph_fit: Plot the fit of a graph to data.
plot.f4stats: Plot the fit of a graph to data.
plot_fit_1: A plot of the cost function or number of fitted statistics.
plot_fit_2: A contour plot of the cost function.
poor_fits: Get the tests in the fit where the predictions fall outside...
poor_fits.agraph_fit: Get the tests in the fit where the predictions fall outside...
poor_fits.agraph_fit_list: Get the tests in the fit where the predictions fall outside...
print.agraph_fit: Print function for the fitted graph.
project_to_population: Map sample names to population names.
remove_duplicates: Remove duplicate graphs from a list.
rename_nodes: Rename nodes.
residuals.agraph_fit: Errors of prediction in the fitted graph
run_metropolis_hasting: Run a Metropolis-Hasting MCMC to sample graph parameters.
seven_leaves_graphs: Seven leaves graphs.
seven_leaves_trees: Seven leaves trees.
sf2: Calculate the f_2(A, B) statistics.
sf3: Calculate the f_3(A; B, C) statistics.
sf4: Calculate the f_4(W, X; Y, Z) statistics.
six_leaves_graphs: Six leaves graphs.
split_population: Reverse a projection of samples to populations.
split_population.agraph_fit: Reverse a projection of samples to populations.
split_population.data.frame: Reverse a projection of samples to populations.
summary.agraph_fit: Summary for the fitted graph.
sum_of_squared_errors: Get the sum of squared errors for a fitted graph.
sum_of_squared_errors.agraph_fit: Get the sum of squared errors for a fitted graph.
sum_of_squared_errors.agraph_fit_list: Get the sum of squared errors for a list of fitted graph.
thinning: Thins out an MCMC trace.
vector_to_graph: Vector to graph.