add_uncertainties_to_costmatrix: Adds uncertainties to a costmatrix

In graemetlloyd/Claddis: Measuring Morphological Diversity and Evolutionary Tempo

Adds uncertainties to a costmatrix

Description

Adds uncertainty transitions to a costmatrix.

Usage

add_uncertainties_to_costmatrix(costmatrix, message = TRUE)

Arguments

costmatrix	An object of class "costMatrix".
message	Logical indicating whether (TRUE, the default) or not (FALSE) to provide messages about the process of the function.

Details

Under a parsimony algorithm uncertainties indicate that the observed state cannot be restricted to a single value but some finite set of values. In #NEXUS format these are indicated with curly braces, , and within Claddis by a slash. For example, state 0 or 1 would be 0/1.

For most operations (calculating the length of a tree, estimating ancestral states) there is no need to formally add uncertainties to a costmatrix. Instead, the uncertainty can be established via setting up the tip states in the Swofford and Maddison (1992) approach. However, there are some usages within Claddis where it is useful to include them in the costmatrix itself, for example calculating minimum step counts for homoplasy metrics.

Here, uncertainties are added to a costmatrix in a similar way to polymorphisms. I.e., all possible uncertainty combinations are calculated and then the associated transition cost calculated and inserted. Note that here only one approach for calculating this cost is applied - the minimu rule of Hoyal Cuthill and Lloyd (in prep.).

Importantly costs of transitioning from uncertainties are assigned infinite cost. There aree two main reasons for this. Firstly, under parsimony and the minimum rule allowing uncertainties as ancestral states would lead to a tree length of zero and an ancestral state for every internal node that includes every possible state being favoured, regardless of the states at the tips. In other words, it would have no analytical value. Secondly, assigning uncertainty of ancestral states is best done by generating all (single or polymorphic) most parsimonious reconstructions and summarising these.

Note that in many practical cases an uncertainty involving all possible states - e.g., the uncertianty 0/1/2 for a three-state character - operates the same as a missing value (NA). However, it may still be useful in some circumstances. For example, to denote that the coding is definitively applicable (as opposed to inapplicable) or to properly record uncertainty rather than missing values for summary statistical purposes.

Finally, it should be noted that uncertainties are the only allowable breaking of the costmatrix rule that all off-diagonal transition costs must be positive. I.e., costs of zero from a single or polymorphic state to an uncertainty state are permitted.

Value

An object of class "costMatrix".

Author(s)

Graeme T. Lloyd [e-mail]

References

Hoyal Cuthill and Lloyd, in prep.

Swofford, D. L. and Maddison, W. P., 1992. Parsimony, character-state reconstructions, and evolutionary inferences. In R. L. Mayden (ed.), Systematics, Historical Ecology, and North American Freshwater Fishes. Stanford University Press, Stanford. pp187-223.

Examples

# Generate an example three-state unordered character costmatrix:
# Generate an example three-state unordered character costmatrix:
unordered_costmatrix <- make_costmatrix(
  min_state = 0,
  max_state = 2,
  character_type = "unordered"
)

# Generate an example three-state ordered character costmatrix:
ordered_costmatrix <- make_costmatrix(
  min_state = 0,
  max_state = 2,
  character_type = "ordered"
)

# Generate an example three-state ordered character costmatrix with polymorphism included:
unordered_polymorphism_costmatrix <- list(
  size = 7,
  n_states = 3,
  single_states = c("0", "1", "2"),
  type = "unordered",
  costmatrix = matrix(
    data = c(
      0, 2, 2, 1, 1, 3, 2,
      2, 0, 2, 1, 3, 1, 2,
      2, 2, 0, 3, 1, 1, 2,
      1, 1, 3, 0, 2, 2, 1,
      1, 3, 1, 2, 0, 2, 1,
      3, 1, 1, 2, 2, 0, 1,
      2, 2, 2, 1, 1, 1, 0
    ),
    nrow = 7,
    byrow = TRUE,
    dimnames = list(
      c(as.character(x = 0:2), "0&1", "0&2", "1&2", "0&1&2"),
      c(as.character(x = 0:2), "0&1", "0&2", "1&2", "0&1&2")
    )
  ),
  symmetry = "Symmetric",
  includes_polymorphisms = TRUE,
  polymorphism_costs = "geometric",
  polymorphism_geometry = "hypercube",
  polymorphism_distance = "manhattan",
  includes_uncertainties = FALSE,
  pruned = FALSE,
  dollo_penalty = 999,
  base_age = 1,
  weight = 0.5
)
class(unordered_polymorphism_costmatrix) <- "costMatrix"

# Add uncertainties to unordered costmatrix:
unordered_costmatrix_uncertainties <- add_uncertainties_to_costmatrix(
  costmatrix = unordered_costmatrix
)

# Show unordered costmatrix with uncertainties included:
unordered_costmatrix_uncertainties$costmatrix

# Add uncertainties to ordered costmatrix:
ordered_costmatrix_uncertainties <- add_uncertainties_to_costmatrix(
  costmatrix = ordered_costmatrix
)

# Show ordered costmatrix with uncertainties included:
ordered_costmatrix_uncertainties$costmatrix

# Add uncertainties to unordered costmatrix with polymorphisms:
unordered_polymorphism_costmatrix_uncertainties <- add_uncertainties_to_costmatrix(
  costmatrix = unordered_polymorphism_costmatrix
)

# Show unordered polymorphism costmatrix with uncertainties included:
unordered_polymorphism_costmatrix_uncertainties$costmatrix

graemetlloyd/Claddis documentation built on May 9, 2024, 8:07 a.m.