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**admixturegraph**: Admixture Graph Manipulation and Fitting**fast_plot**: Fast version of graph plotting.

# Fast version of graph plotting.

### Description

This is a fast, deterministic and stand-alone function for visualizing the
admixture graph. Has the bad habit if sometimes drawing several nodes at the
exact same coordinates; for clearer reasults try `plot.agraph`

(which, on the other hand, relies on numerical optimising of a compicated cost
function and might be unpredictable).

### Usage

1 2 |

### Arguments

`x` |
The admixture graph. |

`ordered_leaves` |
The leaf-nodes in the left to right order they should be drawn. |

`show_admixture_labels` |
A flag determining if the plot should include the names of admixture proportions. |

`show_inner_node_labels` |
A flag determining if the plot should include the names of inner nodes. |

`...` |
Additional plotting options. |

### Value

A plot.

### See Also

`plot.agraph`

### Examples

1 2 3 4 5 6 7 8 | ```
# taken from the collection of all the admixture graphs with four leaves and at
# most two admixture events:
fast_plot(four_leaves_graphs[[24]](c("A", "B", "C", "D")))
# To be fair, here is a graph that looks all right:
fast_plot(four_leaves_graphs[[25]](c("A", "B", "C", "D")))
``` |

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- add_a_leaf: Adds a new leaf to a graph.
- add_a_leaf: Adds a new leaf to a graph.
- add_an_admixture: Adds a new admixture event 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_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: 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.
- 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.
- admix_props: Specify the proportions in an admixture event.
- admixture_edge: Create an admixture edge from a child to two parents.
- admixture_edge: Create an admixture edge from a child to two parents.
- admixturegraph-package: admixturegraph: Visualising and analysing admixture graphs.
- admixturegraph-package: admixturegraph: Visualising and analysing admixture graphs.
- admixture_proportions: Create the list of admixture proportions for an admixture...
- admixture_proportions: Create the list of admixture proportions for an admixture...
- agraph: Create an admixture graph object.
- agraph: Create an admixture graph object.
- agraph_children: Build the child incidene matrix from an parent edge list.
- agraph_children: Build the child incidene matrix from an parent edge list.
- agraph_parents: Build the parent incidence matrix from an 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.
- agraph_weights: Build the matrix of admixture proportions from an edge list.
- all_path_overlaps: Get the list of overlaps of all paths.
- all_path_overlaps: Get the list of overlaps of all paths.
- all_paths: Compute all paths from one leaf to another.
- all_paths: Compute all paths from one leaf to another.
- bears: Statistics for populations of bears
- bears: Statistics for populations of bears
- build_edge_optimisation_matrix: Build a matrix coding the linear system of edges once the...
- 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.
- burn_in: Removes the first k rows from a trace.
- calculate_concentration: Building a proxy concentration matrix.
- calculate_concentration: Building a proxy concentration matrix.
- canonise_expression: Used to recognize similar expressions and to possibly...
- canonise_expression: Used to recognize similar expressions and to possibly...
- coef.agraph_fit: Parameters for the fitted graph.
- coef.agraph_fit: Parameters for the fitted graph.
- cost_function: The cost function fed to Nelder-Mead.
- cost_function: The cost function fed to Nelder-Mead.
- edge: Create an edge from a child to a parent.
- edge: Create an edge from a child to a parent.
- edge_optimisation_function: More detailed edge fitting than mere cost_function.
- edge_optimisation_function: More detailed edge fitting than mere cost_function.
- eight_leaves_trees: Eight leaves trees.
- eight_leaves_trees: Eight leaves trees.
- evaluate_f4: Evaluates an f_4 statistics in a given environment.
- evaluate_f4: Evaluates an f_4 statistics in a given environment.
- examine_edge_optimisation_matrix: Examine the edge optimisation matrix to detect unfitted admix...
- 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_admixture_proportion_parameters: Extract the admixture proportion parameter from edge...
- extract_graph_parameters: Extract all the parameters a graph contains.
- extract_graph_parameters: Extract all the parameters a graph contains.
- extract_trees: Extract trees
- extract_trees: Extract trees
- f2: Calculate the f_2(A, B) statistics.
- f2: Calculate the f_2(A, B) statistics.
- f3: Calculate the f_3(A; B, C) statistics.
- f3: Calculate the f_3(A; B, C) statistics.
- f4: Calculate the f_4(W, X; Y, Z) statistics.
- f4: Calculate the f_4(W, X; Y, Z) statistics.
- f4stats: Make a data frame an f_4 statistics object.
- f4stats: Make a data frame an f_4 statistics object.
- fast_fit: A fast version of graph fitting.
- fast_fit: A fast version of graph fitting.
- fast_plot: Fast version of graph plotting.
- fast_plot: Fast version of graph plotting.
- filter_on_leaves: Filter data so all W, X, Y and Z are leaves in the graph.
- 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: Fit the graph parameters to a data set.
- fit_permutations_and_graphs: 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.
- fitted.agraph_fit: Predicted f statistics for the fitted graph.
- five_leaves_graphs: Five leaves graphs.
- five_leaves_graphs: Five leaves graphs.
- format_path: Create a path data frame from a list of nodes.
- format_path: Create a path data frame from a list of nodes.
- four_leaves_graphs: Four leaves graphs.
- four_leaves_graphs: Four leaves graphs.
- get_graph_f4_sign: Extracts the sign for the f_4 statistics predicted by the...
- 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.
- graph_environment: Build an environment in which f statistics can be evaluated.
- is_negative: All overlaps are either empty or have a negative weight.
- 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_positive: All overlaps are either empty or have a positive weight.
- is_unknown: Overlapping edges have both positive and negative...
- is_unknown: Overlapping edges have both positive and negative...
- is_zero: All overlaps are empty.
- is_zero: All overlaps are empty.
- log_likelihood: Calculate (essentially) the log likelihood of a graph with...
- 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.
- log_sum_of_logs: Computes the log of a sum of numbers all given in log-space.
- make_an_outgroup: Make an outgroup.
- make_an_outgroup: Make an outgroup.
- make_mcmc_model: Collect the information about a graph and a data set needed...
- make_mcmc_model: Collect the information about a graph and a data set needed...
- make_permutations: List of permutations.
- make_permutations: List of permutations.
- model_bayes_factor_n: Computes the Bayes factor between two models from samples...
- 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: Computes the likelihood of a model from samples from its...
- model_likelihood_n: 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.
- mynonneg: Non negative least square solution.
- no_admixture_events: Get the number of admixture events in a graph.
- 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: 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: 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_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: 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: 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...
- 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.
- overlaps_sign: Get the sign of overlapping paths.
- parent_edges: Create the list of edges for an admixture graph.
- parent_edges: Create the list of edges for an admixture graph.
- path_overlap: Collect the postive and negative overlap between two paths.
- path_overlap: Collect the postive and negative overlap between two paths.
- plot.agraph: Plot an admixture graph.
- plot.agraph: Plot an admixture graph.
- plot.agraph_fit: Plot the fit of a graph to data.
- plot.agraph_fit: Plot the fit of a graph to data.
- plot.f4stats: 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_1: A plot of the cost function or number of fitted statistics.
- plot_fit_2: A contour plot of the cost function.
- 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: 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...
- 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.
- print.agraph_fit: Print function for the fitted graph.
- project_to_population: Map sample names to population names.
- project_to_population: Map sample names to population names.
- residuals.agraph_fit: Errors of prediction in the fitted graph
- residuals.agraph_fit: Errors of prediction in the fitted graph
- run_metropolis_hasting: Run a Metropolis-Hasting MCMC to sample graph parameters.
- run_metropolis_hasting: Run a Metropolis-Hasting MCMC to sample graph parameters.
- seven_leaves_trees: Seven leaves trees.
- seven_leaves_trees: Seven leaves trees.
- sf2: Calculate the f_2(A, B) statistics.
- sf2: Calculate the f_2(A, B) statistics.
- sf3: Calculate the f_3(A; B, C) statistics.
- sf3: Calculate the f_3(A; B, C) statistics.
- sf4: Calculate the f_4(W, X; Y, Z) statistics.
- sf4: Calculate the f_4(W, X; Y, Z) statistics.
- six_leaves_graphs: Six leaves graphs.
- six_leaves_graphs: Six leaves graphs.
- split_population: Reverse a projection of samples to populations.
- split_population: Reverse a projection of samples to populations.
- split_population.agraph_fit: 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.
- 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: 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: 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.
- 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.
- thinning: Thins out an MCMC trace.