hsp_naive: Naive hidden state prediction.

In castor: Efficient Phylogenetics on Large Trees

Naive hidden state prediction.

Description

Reconstruct ancestral discrete states of nodes and predict unknown (hidden) states of tips on a tree based on empirical state proportions across tips on the entire tree. This method completely ignores any phylogenetic relationships, and is not designed for making reliable state predictions. It is only meant for testing and debugging purposes, e.g. as a "null" model!

Usage

hsp_naive(tree, 
          tip_states, 
          Nstates     = NULL,
          check_input = TRUE)

Arguments

tree	A rooted tree of class "phylo". Note that the tree structure is not actually used internally.
tip_states	An integer vector of size Ntips, specifying the state of each tip in the tree as an integer from 1 to Nstates, where Nstates is the possible number of states (see below). tip_states can include NA to indicate an unknown tip state that is to be predicted.
Nstates	Either NULL, or an integer specifying the number of possible states of the trait. If NULL, then it will be computed based on the maximum non-NA value encountered in tip_states
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

For this function, the trait's states must be represented by integers within 1,..,Nstates, where Nstates is the total number of possible states. If the states are originally in some other format (e.g. characters or factors), you should map them to a set of integers 1,..,Nstates. You can easily map any set of discrete states to integers using the function map_to_state_space. Any NA entries in tip_states are interpreted as unknown states.

The function calculates the "global" empirical proportions of known states, i.e., across the entire tree, and then sets the state likelihoods of tips with unknown state and nodes to this distribution. States are "predicted" for each tip with unknown state and each node by randomly drawing states according to the same global empirical distribution. This function has asymptotic time complexity O(Ntips x Nstates).

Tips must be represented in tip_states in the same order as in tree$tip.label. The vector tip_states need not include 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, according to the "null" model where phylogenetic relationships are ignored but empirical frequencies of known states are still accounted for.

Value

A list with the following elements:

success	Logical, indicating whether HSP was successful. If FALSE, some return values may be NULL.
likelihoods	A 2D numeric matrix, listing the probability of each tip and node being in each state. This matrix will have (Ntips+Nnodes) rows and Nstates columns, where Nstates was either explicitly provided as an argument or set to the maximum value found in tip_states (if Nstates was passed as NULL). The rows in this matrix will be in the order in which tips and nodes are indexed in the tree, i.e. the rows 1,..,Ntips store the probabilities for tips, while rows (Ntips+1),..,(Ntips+Nnodes) store the probabilities for nodes. Each row in this matrix will sum up to 1. Note that the return value is named this way for compatibility with other HSP functions.
states	Integer vector of length Ntips+Nnodes, with values in {1,..,Nstates}, specifying the "predicted" state for each tip & node. For nodes and tips with a priori unknown state, the predicted state is simply a random draw from the empirical distribution.

Author(s)

Stilianos Louca

References

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

hsp_max_parsimony, hsp_mk_model, hsp_empirical_probabilities hsp_mk_model,

Examples

## Not run: 
# generate random tree
Ntips = 100
tree = generate_random_tree(list(birth_rate_intercept=1),max_tips=Ntips)$tree

# simulate a discrete trait
Nstates = 5
Q = get_random_mk_transition_matrix(Nstates, rate_model="ER", max_rate=0.1)
tip_states = simulate_mk_model(tree, Q)$tip_states

# print states of first 20 tips
print(tip_states[1:20])

# 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 using the naive method
HSP = hsp_naive(tree, tip_states=tip_states, Nstates=Nstates)

# print estimated states of first 20 tips
print(HSP$states[1:20])

## End(Not run)

castor documentation built on Jan. 21, 2026, 9:08 a.m.