ordinate_cladistic_matrix {Claddis}  R Documentation 
Performs Principal Coordinates Analysis (PCoA) on a cladistic matrix.
ordinate_cladistic_matrix( cladistic_matrix, distance_metric = "mord", ged_type = "wills", distance_transformation = "arcsine_sqrt", distance_polymorphism_behaviour = "min_difference", distance_uncertainty_behaviour = "min_difference", distance_inapplicable_behaviour = "missing", character_dependencies = NULL, alpha = 0.5, correction = "cailliez", time_tree = NULL, estimate_all_nodes = FALSE, estimate_tip_values = FALSE, inapplicables_as_missing = FALSE, ancestral_polymorphism_behaviour = "equalp", ancestral_uncertainty_behaviour = "equalp", threshold = 0.01, all_missing_allowed = FALSE )
cladistic_matrix 
A charactertaxon matrix in the format imported by read_nexus_matrix. 
distance_metric 

ged_type 

distance_transformation 

distance_polymorphism_behaviour 

distance_uncertainty_behaviour 

distance_inapplicable_behaviour 

character_dependencies 

alpha 

correction 
The negative eigenvalue correction to use (one of "lingoes", "none", or "cailliez"  the default). See pcoa for more details. 
time_tree 
If a phylmorphospace is desired then a tree with root age and branchlengths must be included. 
estimate_all_nodes 

estimate_tip_values 

inapplicables_as_missing 

ancestral_polymorphism_behaviour 

ancestral_uncertainty_behaviour 

threshold 

all_missing_allowed 
Takes a cladistic matrix in the format imported by read_nexus_matrix and performs Principal Coordinates (Gower 1966) analysis on it.
This function is effectively a wrapper for the pipeline:
estimate_ancestral_states > calculate_morphological_distances > pcoa
With the first part being optional (if wanting a phylomorphospace) and the latter coming from the ape package (the user is referred there for some of the options, e.g., using the Caillez 1983 approach to avoiding negative eigenvalues). (See Lloyd 2016 for more on disparity pipelines.)
If providing a tree and inferring ancestral states then options to also infer missing or uncertain tips and whether to infer values for all characters at all internal nodes are provided by the estimate_ancestral_states part.
Other options within the function concern the distance metric to use and the transformation to be used if selecting a propotional distance (see calculate_morphological_distances).
IMPORTANT: The function can remove taxa (or if including a tree, nodes as well) if they lead to an incomplete distance matrix (see trim_matrix for more details).
time_tree 
The tree (if supplied). Note this may be pruned from the input tree by trim_matrix. 
distance_matrix 
The distance matrix. Note this may be pruned by trim_matrix and thus not include all taxa. 
removed_taxa 
A vector of taxa and/or nodes removed by trim_matrix. Returns NULL if none were removed. 
note 
See pcoa. 
values 
See pcoa. 
vectors 
See pcoa. Note: this will be the same as 
trace 
See pcoa. Note: this will be the same as 
Graeme T. Lloyd graemetlloyd@gmail.com
Cailliez, F., 1983. The analytical solution of the additive constant problem. Psychometrika, 48, 305308.
Gower, J. C., 1966. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325338.
assign_taxa_to_bins, plot_chronophylomorphospace, plot_morphospace_stack, plot_morphospace, plot_multi_morphospace
# Run on Michaux (1989) data set with default settings: x < ordinate_cladistic_matrix(cladistic_matrix = michaux_1989) # Show entire output: x # Generate a (made up) tree: time_tree < ape::rtree(n = length(x = rownames(x = michaux_1989$matrix_1$matrix))) # Add taxon names to it: time_tree$tip.label < rownames(x = michaux_1989$matrix_1$matrix) # Set root time by making youngest taxon extant: time_tree$root.time < max(diag(x = ape::vcv(phy = time_tree))) # Run with tree: y < ordinate_cladistic_matrix(cladistic_matrix = michaux_1989, time_tree = time_tree) # Show new output: y