Reweight() allows the weights of specific characters in phylogenetic
datasets to be arbitrarily adjusted.
Arguments
- dataset
A phylogenetic data matrix of phangorn class
phyDat, or as a matrix in the format produced byPhyDatToMatrix().- weights
Unnamed integer vector specifying desired weight of each character in turn; or named integer vector specifying weights of each character; unnamed entries will be assigned weight 1.
Value
Reweight() returns dataset after adjusting the weights of
the specified characters.
For a matrix, this is attained by repeating each column the weights times.
For a phyDat object, the "weight" attribute will be modified.
Details
This functionality should be employed with care. The underlying principle of parsimony is that all evolutionary steps are equivalent. Setting different weights to different characters is at odds with that principle, so analysis of a re-weighted matrix using a parsimony-based framework is arguably no longer parsimony analysis; on the most permissive view, the criteria used to determine a weighting scheme will always be arbitrary.
It can be useful to relax the criterion that all evolutionary steps are equivalent – for example, implied weighting (Goloboff 1997) typically recovers better trees than equal-weights parsimony (Smith 2019) . This said, assigning different weights to different characters tacitly imposes a model of evolution that differs from that implicit in equal-weights parsimony. Whereas probabilistic models can be evaluated by various methods (e.g. fit, marginal likelihood, posterior predictive power), there are no principled methods of comparing different models under a parsimony framework.
As such, Reweight() is likely to be useful for a narrow set of uses.
Examples may include:
informal robustness testing, to explore whether certain characters are more or less influential on the resulting tree;
Imposing constraints on a dataset, by adding each constraint as a column in a dataset whose weight exceeds the total amount of data.
References
Goloboff PA (1997).
“Self-Weighted Optimization: Tree Searches and Character State Reconstructions under Implied Transformation Costs.”
Cladistics, 13(3), 225–245.
doi:10.1111/j.1096-0031.1997.tb00317.x
.
Smith MR (2019).
“Bayesian and Parsimony Approaches Reconstruct Informative Trees from Simulated Morphological Datasets.”
Biology Letters, 15(2), 20180632.
doi:10.1098/rsbl.2018.0632
.
See also
Other phylogenetic matrix conversion functions:
Decompose(),
MatrixToPhyDat(),
StringToPhyDat()
Examples
mat <- rbind(a = c(0, 2, 0), b = c(0, 2, 0), c = c(1, 3, 0), d = c(1, 3, 0))
dat <- MatrixToPhyDat(mat)
# Set character 1 to weight 1, character 2 to weight 2; omit character 3
Reweight(mat, c(1, 2, 0))
#> _1 _1 _2
#> a 0 2 2
#> b 0 2 2
#> c 1 3 3
#> d 1 3 3
# Equivalently:
Reweight(dat, c("3" = 0, "2" = 2))
#> $a
#> [1] 1 3 1
#>
#> $b
#> [1] 1 3 1
#>
#> $c
#> [1] 2 4 1
#>
#> $d
#> [1] 2 4 1
#>
#> attr(,"weight")
#> [1] 1 2 0
#> attr(,"nr")
#> [1] 3
#> attr(,"nc")
#> [1] 4
#> attr(,"index")
#> [1] 1 2 3
#> attr(,"levels")
#> [1] "0" "1" "2" "3"
#> attr(,"allLevels")
#> [1] "0" "1" "2" "3"
#> attr(,"type")
#> [1] "USER"
#> attr(,"contrast")
#> 0 1 2 3
#> [1,] 1 0 0 0
#> [2,] 0 1 0 0
#> [3,] 0 0 1 0
#> [4,] 0 0 0 1
#> attr(,"class")
#> [1] "phyDat"