hsp_squared_change_parsimony {castor}

Hidden state prediction via squared-change parsimony.

Description

Reconstruct ancestral states of a continuous (numeric) trait for nodes and predict unknown (hidden) states for tips on a tree using squared-change (or weighted squared-change) parsimony (Maddison 1991).

Usage

hsp_squared_change_parsimony(tree, tip_states, weighted=TRUE, check_input=TRUE)

Arguments

tree

A rooted tree of class "phylo". The root is assumed to be the unique node with no incoming edge.

tip_states

A numeric vector of size Ntips, specifying the state of each tip in the tree. tip_states can include NA to indicate an unknown tip state that is to be predicted.

weighted

Logical, specifying whether to weight transition costs by the inverted edge lengths during ancestral state reconstruction. This corresponds to the "weighted squared-change parsimony" reconstruction by Maddison (1991) for a Brownian motion model of trait evolution.

check_input

Logical, specifying whether to perform some basic checks on the validity of the input data. If you are certain that your input data are valid, you can set this to FALSE to reduce computation.

Details

Any NA entries in tip_states are interpreted as unknown (hidden) states to be estimated. Prior to ancestral state reconstruction, the tree is temporarily prunned, keeping only tips with known state. The function then uses Maddison's squared-change parsimony algorithm to reconstruct the globally parsimonious state at each node (Maddison 1991). The states of tips with hidden state are set to those of the most recent ancestor with reconstructed state, as described by Zaneveld and Thurber (2014). This function has asymptotic time complexity O(Nedges). If tree$edge.length is missing, each edge in the tree is assumed to have length 1. This is the same as setting weighted=FALSE. The tree may include multi-furcations (i.e. nodes with more than 2 children) as well as mono-furcations (i.e. nodes with only one child).

Tips must be represented in tip_states in the same order as in tree$tip.label. The vector tip_states need not include item names; if it does, however, they are checked for consistency (if check_input==TRUE).

This function is meant for reconstructing ancestral states in all nodes of a tree as well as predicting the states of tips with an a priory unknown state. If the state of all tips is known and only ancestral state reconstruction is needed, consider using the function asr_squared_change_parsimony for improved efficiency.

Value

A list with the following elements:

states

A numeric vector of size Ntips+Nnodes, listing the reconstructed state of each tip and node. The entries in this vector will be in the order in which tips and nodes are indexed in tree$edge.

total_sum_of_squared_changes

The total sum of squared changes, minimized by the (optionally weighted) squared-change parsimony algorithm. This is equation 7 in (Maddison, 1991).

Author(s)

Stilianos Louca

References

W. P. Maddison (1991). Squared-change parsimony reconstructions of ancestral states for continuous-valued characters on a phylogenetic tree. Systematic Zoology. 40:304-314.

J. R. Zaneveld and R. L. V. Thurber (2014). Hidden state prediction: A modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses. Frontiers in Microbiology. 5:431.

See Also

asr_squared_change_parsimony hsp_max_parsimony, hsp_mk_model, map_to_state_space

Examples

# generate random tree
Ntips = 100
tree = generate_random_tree(list(birth_rate_intercept=1),max_tips=Ntips)$tree

# simulate a continuous trait
tip_states = simulate_ou_model(tree, 
                               stationary_mean=0,
                               stationary_std=1,
                               decay_rate=0.001)$tip_states

# print tip states
print(tip_states)

# set half of the tips to unknown state
tip_states[sample.int(Ntips,size=as.integer(Ntips/2),replace=FALSE)] = NA

# reconstruct all tip states via weighted SCP
estimated_states = hsp_squared_change_parsimony(tree, tip_states, weighted=TRUE)$states

# print estimated tip states
print(estimated_states[1:Ntips])