, Kristina Wicke [aut]
, Mareike Fischer
[aut] | Title: | Phylogenetic Tree Models and the Power of Tree Shape Statistics |
|---|---|
| Description: | The first goal of this package is to provide a multitude of tree models, i.e., functions that generate rooted binary trees with a given number of leaves. Second, the package allows for an easy evaluation and comparison of tree shape statistics by estimating their power to differentiate between different tree models. Please note that this R package was developed alongside the manuscript "Tree balance in phylogenetic models" by S. J. Kersting, K. Wicke, and M. Fischer (2024) <doi:10.48550/arXiv.2406.05185>, which provides further background and the respective mathematical definitions. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM – Artificial Intelligence in Farming funded by the German Federal Ministry of Education and Research (No. 03WIR4805). |
| Authors: | Sophie Kersting [aut, cre]
, Kristina Wicke [aut]
, Mareike Fischer
[aut] |
| Maintainer: | Sophie Kersting <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.0.1.1 |
| Built: | 2025-02-13 02:41:18 UTC |
| Source: | https://github.com/cran/poweRbal |
enum2cladewise - Changes the node enumeration to cladewise
enumeration, i.e., starting from the root we follow the rule:
Go to the left child; if that does not exist or was already visited go (up
again and) to the right child.
The nodes in the rooted binary tree can be nearly arbitrarily enumerated
(distinct nodes should have distinct values and the
values should be positive, i.e., >0).
enum2cladewise(phy, root = NULL)enum2cladewise(phy, root = NULL)
phy |
A rooted binary tree of class |
root |
Integer value (default = NULL) that should only be specified if the root is known precisely (not necessary, but speeds up computation). |
enum2cladewise A single tree of class phylo is
returned with cladewise node enumeration.
# Example with cladewise enumeration: phy_alreadycladew <- list(edge = matrix(c(6,7, 7,8, 8,1, 8,2, 7,9, 9,3, 9,4, 6,5), byrow = TRUE, ncol = 2), tip.label = rep(" ",5), Nnode = 4) attr(phy_alreadycladew, "class") <- "phylo" enum2cladewise(phy_alreadycladew, root = 6)$edge ape::plot.phylo(phy_alreadycladew) # Example with other node enumeration: phy_example <- list(edge = matrix(c(1,55, 55,12, 12,2, 12,10, 55,9, 9,13, 9,60, 1,3), byrow = TRUE, ncol = 2), tip.label = rep(" ",5), Nnode = 4, edge.length = rep(1, 8)) attr(phy_example, "class") <- "phylo" # The reenumeration works with and without specifying the root: enum2cladewise(phy_example, root = 1)$edge ape::plot.phylo(enum2cladewise(phy_example))# Example with cladewise enumeration: phy_alreadycladew <- list(edge = matrix(c(6,7, 7,8, 8,1, 8,2, 7,9, 9,3, 9,4, 6,5), byrow = TRUE, ncol = 2), tip.label = rep(" ",5), Nnode = 4) attr(phy_alreadycladew, "class") <- "phylo" enum2cladewise(phy_alreadycladew, root = 6)$edge ape::plot.phylo(phy_alreadycladew) # Example with other node enumeration: phy_example <- list(edge = matrix(c(1,55, 55,12, 12,2, 12,10, 55,9, 9,13, 9,60, 1,3), byrow = TRUE, ncol = 2), tip.label = rep(" ",5), Nnode = 4, edge.length = rep(1, 8)) attr(phy_example, "class") <- "phylo" # The reenumeration works with and without specifying the root: enum2cladewise(phy_example, root = 1)$edge ape::plot.phylo(enum2cladewise(phy_example))
genAldousBetaTree - Generates a rooted binary tree in
phylo format with the given number of n leaves under the
Aldous beta model.
The Aldous beta model is not a rate-based incremental evolutionary (tree)
construction and thus cannot generate edge lengths, only a topology.
Instead, the Aldous beta model works as follows: The idea is to start with
the root and the set of its descendant leaves, i.e., all n leaves.
Then, this set is partitioned into two subsets according to a density
function dependent on the parameter beta.
The two resulting subsets contain the leaves of the two maximal pending
subtrees of the root, respectively. The same procedure is then applied to the
root's children and their respective subsets, and so forth.
genAldousBetaTree(n, BETA)genAldousBetaTree(n, BETA)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
BETA |
Numeric value >=-2 which specifies how the leaf sets
are partitioned. For certain choices of
|
genAldousBetaTree A single tree of class phylo is
returned.
D. Aldous. Probability Distributions on Cladograms. In Random Discrete Structures, pages 1–18. Springer New York, 1996.
genAldousBetaTree(n = 5, BETA = 1)genAldousBetaTree(n = 5, BETA = 1)
genAltBirthDeathTree - Generates a rooted binary tree in phylo
format with the given number of n leaves under the alternative
birth-death model.
In the alternative birth-death process all species have the same speciation
BIRTHRATE and extinction rates DEATHRATE. Extinct species
remain as fossils inside the tree with zero speciation and extinction
rates.
genAltBirthDeathTree(n, BIRTHRATE = 1, DEATHRATE = 0, TRIES = 5)genAltBirthDeathTree(n, BIRTHRATE = 1, DEATHRATE = 0, TRIES = 5)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
BIRTHRATE |
Positive numeric value (default = 1) which specifies the rate at which the speciation events occur. |
DEATHRATE |
Positive numeric value (default = 0) which specifies the rate at which the extinction events occur. |
TRIES |
Integer value (default = 5) that specifies
the number of attempts to generate a tree with |
genAltBirthDeathTree A single tree of class phylo is
returned.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models. arXiv:2406.05185, 2024.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models: Supplementary material. https://tinyurl.com/278cwdh8, 2024.
genAltBirthDeathTree(n = 7, DEATHRATE = 1)genAltBirthDeathTree(n = 7, DEATHRATE = 1)
genBiSSETree - Generates a rooted binary tree in phylo
format with the given number of n leaves under the BiSSE model.
In the BiSSE model all species have a state, either A or B, and depending on
the state a speciation rate BIRTHRATES, an extinction rate
DEATHRATES as well as a transition rate to the other state
TRANSRATES.
Extinct species are removed from the tree, i.e., the generated tree contains
only species living at the present.
genBiSSETree( n, BIRTHRATES = c(1, 1), DEATHRATES = c(0, 0), TRANSRATES, TRIES = 5, TIMEperTRY = 0.1 )genBiSSETree( n, BIRTHRATES = c(1, 1), DEATHRATES = c(0, 0), TRANSRATES, TRIES = 5, TIMEperTRY = 0.1 )
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
BIRTHRATES |
Numeric vector (default = c(1,1)) which specifies the speciation rates in state A and B (vector with 2 values >=0, one value >0). |
DEATHRATES |
Numeric vector (default = c(0,0)) which specifies the extinction rates in state A and B (vector with 2 values >=0). |
TRANSRATES |
Numeric vector which specifies the transition rates from A to B and from B to A (vector with 2 values >0). |
TRIES |
Integer value (default = 5) that specifies
the number of attempts to generate a tree with |
TIMEperTRY |
Numeric value (default = 0.1) that specifies the maximum amount of time (in seconds) invested per try. |
genBiSSETree A single tree of class phylo is
returned.
This function uses the tree.bisse function of the
diversitree package
(R. G. FitzJohn. Diversitree: Comparative Phylogenetic Analyses of
Diversification in R. Methods in Ecology and
Evolution, 3(6):1084-1092, 2012).
W. P. Maddison, P. E. Midford, and S. P. Otto. Estimating a binary character’s effect on speciation and extinction. Systematic Biology, 56(5):701–710, 2007.
if (requireNamespace("diversitree", quietly = TRUE)) { genBiSSETree(n = 5, BIRTHRATES = c(1,2), DEATHRATES = c(0,0), TRANSRATES = c(0.1,0.3)) }if (requireNamespace("diversitree", quietly = TRUE)) { genBiSSETree(n = 5, BIRTHRATES = c(1,2), DEATHRATES = c(0,0), TRANSRATES = c(0.1,0.3)) }
genCombTree - Generates the rooted binary comb tree (also known as
caterpillar tree) in phylo format with the given number of n
leaves.
genCombTree(n)genCombTree(n)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
genCombTree A single tree of class phylo is returned.
D. Aldous. Probability Distributions on Cladograms. In Random Discrete Structures, pages 1–18. Springer New York, 1996.
genCombTree(n = 6)genCombTree(n = 6)
genDensityTree - Generates a rooted binary tree in phylo
format with the given number of n leaves under the
density-dependent model.
In the density-dependent tree generation process all species have the same
speciation BIRTHRATE, but the extinction rates depend on the
number of species (it increases linearly with the number of co-existing
lineages until an equilibrium number is reached at which speciation and
extinction rates are equal).
Extinct species are removed from the tree, i.e., the generated tree contains
only species living at the present.
genDensityTree(n, BIRTHRATE = 1, EQUILIB, TRIES = 5, TIMEperTRY = 0.01)genDensityTree(n, BIRTHRATE = 1, EQUILIB, TRIES = 5, TIMEperTRY = 0.01)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
BIRTHRATE |
Positive numeric value (default = 1) which specifies the rate at which the speciation events occur. |
EQUILIB |
Integer value that specifies the equilibrium number. |
TRIES |
Integer value (default = 5) that specifies
the number of attempts to generate a tree with |
TIMEperTRY |
Numeric value (default = 0.01) that specifies the maximum amount of time (in seconds) invested per try. |
genDensityTree A single tree of class phylo is
returned.
P. H. Harvey, R. M. May, and S. Nee. Phylogenies without fossils. Evolution, 48(3):523–529, 1994.
genDensityTree(n = 5, EQUILIB = 6)genDensityTree(n = 5, EQUILIB = 6)
genETMTree - Generates a rooted binary tree in phylo
format with the given number of n leaves under the
equiprobable-types-model.
Given n, all tree shapes/topologies with n leaves are
equiprobable under the ETM.
genETMTree(n)genETMTree(n)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
genETMTree A single tree of class phylo is returned.
This function uses the rtree(..., equiprob = T) function of the
ape package
(E. Paradis, K. Schliep. “ape 5.0: an environment for modern
phylogenetics and evolutionary analyses in R.”
Bioinformatics, 35, 526-528, 2019).
genETMTree(n = 5)genETMTree(n = 5)
genFordsAlphaTree - Generates a rooted binary tree in
phylo format with the given number of n leaves under Ford's
alpha model.
Ford's alpha model is not a rate-based evolutionary (tree)
construction and thus cannot generate edge lengths, only a topology.
Instead, it works as follows: The idea is to start with a cherry and
incrementally increase the size of the tree by adding a new leaf with
a leaf edge to any edge (inner or leaf edge), one at a time.
Given a tree with i leaves, then each of the i-1 inner edges (includes
an additional root edge) is chosen with probability
ALPHA/(i-ALPHA).
Each of the i leaf edges is chosen with probability
(1-ALPHA)/(i-ALPHA).
genFordsAlphaTree(n, ALPHA)genFordsAlphaTree(n, ALPHA)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
ALPHA |
Numeric value >=0 and <=1 which specifies the probabilities of
picking an inner or a leaf edge. For certain choices of
|
genFordsAlphaTree A single tree of class phylo is
returned.
D. J. Ford. Probabilities on cladograms: introduction to the alpha
model, 2005.
G. Kaur, K. P. Choi, and T. Wu. Distributions of cherries and pitchforks for the Ford model. Theoretical Population Biology, 149:27–38, 2023.
genFordsAlphaTree(n = 5, ALPHA = 0.3)genFordsAlphaTree(n = 5, ALPHA = 0.3)
genGFBTree - Generates the rooted binary greedy from the bottom tree
in phylo format with the given number of n leaves.
genGFBTree(n)genGFBTree(n)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
genGFBTree A single tree of class phylo is returned.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models. arXiv:2406.05185, 2024.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models: Supplementary material. https://tinyurl.com/278cwdh8, 2024.
genGFBTree(n = 6)genGFBTree(n = 6)
genGrowTree - Generates a rooted binary tree in phylo
format with the given number of n leaves under a specified
discrete-time tree growing model without extinction.
These tree growing models act at the leaves by varying their speciation
rates according to a parameter ZETA or variance SIGMA. They
may also depend on so-called trait values of the leaves (e.g., continuous or
discrete age, or another numeric trait that affects fitness).
You may choose an already built-in model (see use_built_in) or
specify a (new) model by defining how the rates (and optionally traits)
change in every time step (see parameters childRates and
otherRates as well as childTraits and
otherTraits; see also Table 5 of the supplementary material of
the corresponding manuscript).
genGrowTree( n, STARTING_RATE = 1, STARTING_TRAIT = 10, ZETA = 1, SIGMA = 0, childRates, otherRates, childTraits = NULL, otherTraits = NULL, use_built_in = NULL )genGrowTree( n, STARTING_RATE = 1, STARTING_TRAIT = 10, ZETA = 1, SIGMA = 0, childRates, otherRates, childTraits = NULL, otherTraits = NULL, use_built_in = NULL )
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
STARTING_RATE |
Positive numeric value (default = 1) which specifies the initial rate at which the speciation events occur (has only influence on the edge length, not on the tree topology). |
STARTING_TRAIT |
Numeric value (default = 10) which specifies the initial state of a trait. |
ZETA |
Constant non-negative numeric value (default = 1) which can
influence the speciation rates. Can also be a vector if used as such when
defining the functions |
SIGMA |
Constant positive numeric value (default = 0) which can influence
the speciation rates. Can also be a vector if used as such when defining the
functions |
childRates |
A function that generates two speciation rates for the
children emerging from a speciation event based on various factors.
|
otherRates |
A function that generates a new speciation rate for all
leaves not affected by the speciation event (all but parent and children)
based on various factors. The function is applied after the speciation event,
i.e., after
|
childTraits |
An optional function (default = NULL) that generates two
trait values for the children emerging from a speciation event based on
various factors. |
otherTraits |
An optional function (default = NULL) that generates a new
trait value for all leaves not affected by the speciation event (all but
parent and children) based on various factors. |
use_built_in |
Optional (default = NULL): Character specifying which of
the already implemented models should be used. Overwrites
|
genGrowTree A single tree of class phylo is
returned.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models. arXiv:2406.05185, 2024.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models: Supplementary material. https://tinyurl.com/278cwdh8, 2024.
M. G. B. Blum and O. Francois. On statistical tests of phylogenetic tree imbalance: the Sackin and other indices revisited. Mathematical Biosciences, 195(2):141–153, 2005.
S. B. Heard. Patterns in phylogenetic tree balance with variable and evolving speciation rates. Evolution, 50(6):2141–2148, 1996.
S. J. Kersting. Genetic programming as a means for generating improved tree balance indices (Master’s thesis, University of Greifswald), 2020.
M. Kirkpatrick and M. Slatkin. Searching for evolutionary patterns in the shape of a phylogenetic tree. Evolution, 47(4):1171–1181, 1993.
genGrowTree(n = 5, use_built_in = "IF_sym", ZETA = 2)genGrowTree(n = 5, use_built_in = "IF_sym", ZETA = 2)
genMBTree - Generates the rooted binary maximally balanced tree in
phylo format with the given number of n leaves.
genMBTree(n)genMBTree(n)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
genMBTree A single tree of class phylo is returned.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models. arXiv:2406.05185, 2024.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models: Supplementary material. https://tinyurl.com/278cwdh8, 2024.
genMBTree(n = 6)genMBTree(n = 6)
genPDATree - Generates a rooted binary tree in phylo
format with the given number of n leaves under the
proportional-to-distinguishable-arrangements model.
Given n, all phylogenies (trees with labeled leaves) with
n leaves are equiprobable under the PDA.
genPDATree(n)genPDATree(n)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
genPDATree A single tree of class phylo is returned.
This function uses the rtopology(..., rooted = T) function of
the ape package
(E. Paradis, K. Schliep. “ape 5.0: an environment for modern
phylogenetics and evolutionary analyses in R.”
Bioinformatics, 35, 526-528, 2019).
D. E. Rosen. Vicariant patterns and historical explanation in biogeography. Systematic Zoology, 27(2):159, 1978.
genPDATree(n = 5)genPDATree(n = 5)
genTrees - Is a wrapper function that generates
Ntrees-many rooted binary trees with the given number of n
leaves under any tree model tm contained in this package (more
details on the available models are given in the parameter information
for tm).
genTrees(n, Ntrees = 1L, tm)genTrees(n, Ntrees = 1L, tm)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
Ntrees |
Integer value (default = 1) that specifies the desired number of generated trees. |
tm |
Character or list specifying the tree model under which the trees
should be generated as well as their parameters. Available are:
|
genTrees If Ntrees is 1, then a single tree of
class phylo is returned.
If Ntrees is larger than 1, a list of class
multiPhylo containing the trees of class phylo is returned.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models. arXiv:2406.05185, 2024.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models: Supplementary material. https://tinyurl.com/278cwdh8, 2024.
genTrees(n = 5, Ntrees = 2, tm = list("aldous", 1)) genTrees(n = 5, tm = "pda")genTrees(n = 5, Ntrees = 2, tm = list("aldous", 1)) genTrees(n = 5, tm = "pda")
genYuleTree - Generates a rooted binary tree in phylo
format with the given number of n leaves under the Yule model.
The Yule process is a simple birth-process in which all species have the same
speciation rate.
genYuleTree(n)genYuleTree(n)
n |
Integer value that specifies the desired number of leaves, i.e.,
vertices with in-degree 1 and out-degree 0. |
genYuleTree A single tree of class phylo is returned.
This function uses the rtree function of the ape package
(E. Paradis, K. Schliep. “ape 5.0: an environment for modern
phylogenetics and evolutionary analyses in R.”
Bioinformatics, 35, 526-528, 2019).
G. U. Yule. A mathematical theory of evolution, based on the conclusions of
Dr. J. C. Willis, F. R. S. Philosophical Transactions of the Royal Society
of London. Series B, Containing Papers of a Biological
Character, 213(402-410):21–87, 1925.
E. F. Harding. The probabilities of rooted tree-shapes generated by random bifurcation. Advances in Applied Probability, 3(1):44–77, 1971.
genYuleTree(n = 5)genYuleTree(n = 5)
getAccRegion - Computes the region of acceptance based on quantiles
for a specified level of significance and method.
getAccRegion_sampled - Computes a sampling-based region of acceptance
for the given null model based on quantiles for a specified level of
significance and method.
getAccRegion_exact - Computes the exact region of acceptance for the
given null model based on quantiles for a specified level of significance
and method. Currently, this is only implemented for
null_model = "yule" or "pda", and n<=20.
computeAccRegion - Computes the bounds of the region of acceptance
given the empirical distribution function (specified by the unique values
and their probabilities under the null model) for specified cut-offs
(e.g., 0.025 on both sides for a symmetric two-tailed test).
For values strictly outside of the interval the null hypothesis is
rejected.
This function also computes the probabilities to
reject the null hypothesis if the value equals the lower or upper bound of
the region of acceptance. This probability is 0 for correction method
"none" and for "small-sample" it ensures that the probability of rejection
exactly corresponds with the specified cut-offs.
getAccRegion( tss, null_model = "yule", n, distribs = "exact_if_possible", N_null = 10000L, N_alt = 1000L, N_intervals = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) getAccRegion_sampled( tss, null_model = "yule", n, N_null, N_alt = 1000L, N_intervals = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) getAccRegion_exact( tss, null_model = "yule", n, N_alt = 1000L, N_intervals = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) computeAccRegion( unique_null_vals, unique_null_probs, correction, cutoff_left, cutoff_right )getAccRegion( tss, null_model = "yule", n, distribs = "exact_if_possible", N_null = 10000L, N_alt = 1000L, N_intervals = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) getAccRegion_sampled( tss, null_model = "yule", n, N_null, N_alt = 1000L, N_intervals = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) getAccRegion_exact( tss, null_model = "yule", n, N_alt = 1000L, N_intervals = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) computeAccRegion( unique_null_vals, unique_null_probs, correction, cutoff_left, cutoff_right )
tss |
Vector containing the names (as character) of the tree shape
statistics that should be compared. You may either use the short names
provided in |
null_model |
The null model that is to be used to determine the power
of the tree shape statistics. In general, it must be a function that
produces rooted binary trees in |
n |
Integer value that specifies the desired number of leaves, i.e., vertices with in-degree 1 and out-degree 0. |
distribs |
Determines how the distributions (and with that the
bounds of the critical region) are computed. Available are:
|
N_null |
Sample size (integer >=10) if distributions are sampled (default = 10000L). |
N_alt |
Sample size (integer >=10) for the alternative models to
estimate the power (default = 1000L). Only needed here if the
|
N_intervals |
Number (integer >=3, default = 1000L) of different
quantile/cut-off pairs investigated as potential bounds of the region of
acceptance. This parameter is only necessary if the |
test_type |
Determines the method. Available are:
|
correction |
Specifies the desired correction method.
Available are:
|
sig_lvl |
Level of significance (default=0.05, must be >0 and <1). |
unique_null_vals |
Numeric vector containing all the unique values under the null model. |
unique_null_probs |
Numeric vector containing the corresponding probabilities of the unique values under the null model. |
cutoff_left |
Numeric value (>=0, <1) specifying the cut-off of the distribution for the lower bound of the region of acceptance. The sum of the two cut-offs must be <1. |
cutoff_right |
Numeric value (>=0, <1) specifying the cut-off of the distribution for the upper bound of the region of acceptance. The sum of the two cut-offs must be <1. |
getAccRegion Numeric matrix (one row per TSS) with four
columns: The first two columns contain the interval limits of the region
of acceptance, i.e., we reject the null hypothesis for values strictly
outside of this interval. The third and fourth columns contain the
probabilities to reject the null hypothesis if values equal the lower or
upper bound, respectively.
getAccRegion_sampled Numeric matrix (one row per TSS) with
four columns - similar as getAccRegion.
getAccRegion_exact Numeric matrix (one row per TSS) with
four columns - similar as getAccRegion.
computeAccRegion Numeric vector with
four columns - similar as getAccRegion.
getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L) getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, null_model = "etm", N_null = 20L, correction = "none", distribs = "sampled") getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, N_null = 20L, test_type = "two-tailed-unbiased", N_intervals = 5L, N_alt = 10L) getAccRegion_sampled(tss = c("Sackin", "Colless", "B1I"), n = 6L, N_null = 20L, correction = "none") getAccRegion_exact(tss = c("Sackin", "Colless", "B1I"), null_model = "etm", n = 8L) computeAccRegion(unique_null_vals = c(1,2,3,4,5), unique_null_probs = c(0.1,0.4,0.1,0.2,0.2), correction = "small-sample", cutoff_left = 0.15, cutoff_right = 0.15)getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L) getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, null_model = "etm", N_null = 20L, correction = "none", distribs = "sampled") getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, N_null = 20L, test_type = "two-tailed-unbiased", N_intervals = 5L, N_alt = 10L) getAccRegion_sampled(tss = c("Sackin", "Colless", "B1I"), n = 6L, N_null = 20L, correction = "none") getAccRegion_exact(tss = c("Sackin", "Colless", "B1I"), null_model = "etm", n = 8L) computeAccRegion(unique_null_vals = c(1,2,3,4,5), unique_null_probs = c(0.1,0.4,0.1,0.2,0.2), correction = "small-sample", cutoff_left = 0.15, cutoff_right = 0.15)
getPowerMultTSS - Computes the power of one or multiple TSS by
calculating the proportion of values outside the region of acceptance for
a single alternative model.
getPowerMultTSS(accept_regions, alt_data)getPowerMultTSS(accept_regions, alt_data)
accept_regions |
Numeric matrix (one row per TSS) with two or four
columns: The first two columns contain the interval limits of the region
of acceptance, i.e., we reject the null hypothesis for values strictly
outside of this interval. The third and fourth columns contain the
probabilities to reject the null hypothesis if values equal the lower or
upper bound, respectively. If the last two columns are missing they are
interpreted as zeroes. See return value of |
alt_data |
Numeric matrix (one row per TSS) with values under the alternative model. If there is only one TSS, then it can be a simple vector of values instead (returns a single unnamed value). |
getPowerMultTSS A vector containing the power regarding the
given TSS (retains row names of accept_regions).
# Example with small data (with/without third and fourth column): getPowerMultTSS(accept_regions = c(2,3, 0,0), alt_data = c(1,2,4,5)) getPowerMultTSS(accept_regions = c(2,3, 0.5,1), alt_data = c(1,2,4,5)) # Example with multiple rows/TSS: getPowerMultTSS(accept_regions = matrix(c(2,3,0,0, 20,30,0.5,0.5), nrow = 2, byrow = TRUE, dimnames = list(c("TSS1", "TSS2"), NULL)), alt_data = matrix(c( 1,2,3,4, 10,20,30,40), nrow = 2, byrow = TRUE, dimnames = list(c("TSS1", "TSS2"), NULL))) # Example with generated TSS data: getPowerMultTSS(accept_regions = getAccRegion(tss = c("Colless","SNI"), n = 6L), alt_data = getTSSdata(tss = c("Colless", "SNI"), n = 6L, Ntrees = 20L, tm = list("aldous", -1)))# Example with small data (with/without third and fourth column): getPowerMultTSS(accept_regions = c(2,3, 0,0), alt_data = c(1,2,4,5)) getPowerMultTSS(accept_regions = c(2,3, 0.5,1), alt_data = c(1,2,4,5)) # Example with multiple rows/TSS: getPowerMultTSS(accept_regions = matrix(c(2,3,0,0, 20,30,0.5,0.5), nrow = 2, byrow = TRUE, dimnames = list(c("TSS1", "TSS2"), NULL)), alt_data = matrix(c( 1,2,3,4, 10,20,30,40), nrow = 2, byrow = TRUE, dimnames = list(c("TSS1", "TSS2"), NULL))) # Example with generated TSS data: getPowerMultTSS(accept_regions = getAccRegion(tss = c("Colless","SNI"), n = 6L), alt_data = getTSSdata(tss = c("Colless", "SNI"), n = 6L, Ntrees = 20L, tm = list("aldous", -1)))
getTSSdata - Compute the tree shape statistics of trees generated
under a tree model for each given TSS.
getTSSdata_trees - Compute the tree shape statistics for each given
TSS and all given trees.
getTSSdata(tss, n, Ntrees = 1L, tm) getTSSdata_trees(tss, treeList)getTSSdata(tss, n, Ntrees = 1L, tm) getTSSdata_trees(tss, treeList)
tss |
Vector containing the names (as character) of the tree shape
statistics that should be compared. You may either use the short names
provided in |
n |
Integer value that specifies the desired number of leaves, i.e., vertices with in-degree 1 and out-degree 0. |
Ntrees |
Integer value (default = 1) that specifies the desired number of generated trees. |
tm |
If the respective model is included in this package, then specify
the model and its parameters by using a character or list. Available are all
options listed under parameter |
treeList |
List of trees of class |
getTSSdata Numeric matrix of TSS values (one row per TSS).
getTSSdata_trees Numeric matrix of TSS values
(one row per TSS).
# Example using tree models and TSS included in this package: getTSSdata(tss = c("Colless", "Sackin"), n = 5L, Ntrees = 3L, tm = list("aldous", -1)) # Example using a "new" tree model and a "new" TSS provided by the user: my_aldous <- list(func = function(n, Ntrees){ trees <- lapply(1:Ntrees, function(x){genAldousBetaTree(n = n, BETA =5L)}) attr(trees, "class") <- "multiPhylo" return(trees)}) my_avd <- list(func = treebalance::avgVertDep, short = "My AVD") getTSSdata(tss = c("Colless", "my_avd"), n = 5L, Ntrees = 3L, tm = "my_aldous") # Example using TSS provided in tssInfo. getTSSdata_trees(tss = c("Colless", "Sackin"), treeList = genTrees(n = 5L, Ntrees = 3L, tm = "yule")) # Example using a "new" TSS provided by the user. my_avd <- list(func = treebalance::avgVertDep, short = "My AVD") getTSSdata_trees(tss = c("Colless", "my_avd"), treeList = genTrees(n = 5L, Ntrees = 3L, tm = list("IF_sym", 2)))# Example using tree models and TSS included in this package: getTSSdata(tss = c("Colless", "Sackin"), n = 5L, Ntrees = 3L, tm = list("aldous", -1)) # Example using a "new" tree model and a "new" TSS provided by the user: my_aldous <- list(func = function(n, Ntrees){ trees <- lapply(1:Ntrees, function(x){genAldousBetaTree(n = n, BETA =5L)}) attr(trees, "class") <- "multiPhylo" return(trees)}) my_avd <- list(func = treebalance::avgVertDep, short = "My AVD") getTSSdata(tss = c("Colless", "my_avd"), n = 5L, Ntrees = 3L, tm = "my_aldous") # Example using TSS provided in tssInfo. getTSSdata_trees(tss = c("Colless", "Sackin"), treeList = genTrees(n = 5L, Ntrees = 3L, tm = "yule")) # Example using a "new" TSS provided by the user. my_avd <- list(func = treebalance::avgVertDep, short = "My AVD") getTSSdata_trees(tss = c("Colless", "my_avd"), treeList = genTrees(n = 5L, Ntrees = 3L, tm = list("IF_sym", 2)))
getTSSnames - Returns the full names (character/expression) of the
TSS.
getTSSsimple - Returns the simple names (character/expression) of the
TSS.
getTSScolors - Returns the colors of the TSS.
getTSSsafe_n - Returns the ranges of n that can be safely used.
getTSStype - Returns the types of the TSS, i.e., whether they are
balance or imbalance indices, or simple tree shape statistics.
getTSSonly_bin - Returns TRUE/FALSE vector: TRUE if TSS is only for
binary trees and FALSE otherwise.
getAllTSS - Returns the short names of all TSS that are safe to
use for the specified n, have one of the specified types and
can be applied to (non-)binary trees (not_only_bin).
getTSSnames(tss_shorts) getTSSsimple(tss_shorts) getTSScolors(tss_shorts) getTSSsafe_n(tss_shorts) getTSStype(tss_shorts) getTSSonly_bin(tss_shorts) getAllTSS(n = NULL, not_only_bin = FALSE, types = c("tss", "bali", "imbali"))getTSSnames(tss_shorts) getTSSsimple(tss_shorts) getTSScolors(tss_shorts) getTSSsafe_n(tss_shorts) getTSStype(tss_shorts) getTSSonly_bin(tss_shorts) getAllTSS(n = NULL, not_only_bin = FALSE, types = c("tss", "bali", "imbali"))
tss_shorts |
Vector of short names (characters) of TSS contained in
|
n |
Integer value or vector of integer values, that
specifies the number(s) of leaves.
If NULL (default), then |
not_only_bin |
Select TRUE if you also want to analyze non-binary trees and therefore want to filter out any TSS that only work on binary trees. Otherwise, select FALSE (default) if all TSS are applicable. |
types |
Character vector, that specifies all permissible TSS types. The
vector may contain a subset of |
getTSSnames Vector of characters/expressions.
getTSSsimple Vector of characters/expressions.
getTSScolors Vector of characters (color names).
getTSSsafe_n Numeric matrix, one row per TSS and two columns
with lower and upper limit.
getTSStype Vector of characters (types as factors).
getTSSonly_bin Logical vector.
getAllTSS Character vector of short names of TSS
contained in tssInfo.
getTSSnames(tss_shorts = c("Sackin", "Colless", "B1I")) getTSSsimple(tss_shorts = c("Sackin", "Colless", "B1I")) getTSScolors(tss_shorts = c("Sackin", "Colless", "B1I")) getTSSsafe_n(tss_shorts = c("Sackin", "Colless", "B1I")) getTSStype(tss_shorts = c("Sackin", "Colless", "B1I")) getTSSonly_bin(tss_shorts = c("Sackin", "Colless", "B1I")) getAllTSS(n = c(3,30))getTSSnames(tss_shorts = c("Sackin", "Colless", "B1I")) getTSSsimple(tss_shorts = c("Sackin", "Colless", "B1I")) getTSScolors(tss_shorts = c("Sackin", "Colless", "B1I")) getTSSsafe_n(tss_shorts = c("Sackin", "Colless", "B1I")) getTSStype(tss_shorts = c("Sackin", "Colless", "B1I")) getTSSonly_bin(tss_shorts = c("Sackin", "Colless", "B1I")) getAllTSS(n = c(3,30))
This function generates a plot for an object of class poweRbal_data.
Creates a bar plot if alt_model_params and x$alt_model_params
= NULL and a line plot otherwise if this information is given.
## S3 method for class 'poweRbal_data' plot( x, tss_names = NULL, tss_colors = NULL, sig_lvl = NULL, legend_pos = "topright", alt_model_names = NULL, alt_model_params = NULL, tss_ltys = NULL, alt_model_family = NULL, ... )## S3 method for class 'poweRbal_data' plot( x, tss_names = NULL, tss_colors = NULL, sig_lvl = NULL, legend_pos = "topright", alt_model_names = NULL, alt_model_params = NULL, tss_ltys = NULL, alt_model_family = NULL, ... )
x |
An object of class
|
tss_names |
Vector of characters/expression of the TSS names (default
= NULL). If none are provided, |
tss_colors |
Vector of colors for the TSS (default = NULL). |
sig_lvl |
Level of significance (default=0.05, must be >0 and <1) depicted as a dashed horizontal line. Not depicted if set to NULL. |
legend_pos |
Character specifying where the legend is displayed (default = "topright"). No legend is displayed if set to NULL. |
alt_model_names |
Vector of characters/expression of the model names
(default = NULL). If none are provided, the column names of
|
alt_model_params |
Numeric vector containing the parameter
values of the representatives of the tree model (default = NULL). If none
are provided, |
tss_ltys |
Vector of line types for the TSS (default = NULL). |
alt_model_family |
Vector of characters/expressions of the name of the
tree model family and of the parameter (default = NULL), e.g.
|
... |
Additional arguments passed to the |
plot.poweRbal_data No return value, as the primary purpose
of this function is the side effect (plotting).
# Plotting a 'poweRbal_data' object: pc1 <- powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous",-1), "pda", "etm"), n = 8L, N_null = 40L, N_alt = 20L) plot(pc1) # Plotting a power comparison with a tree model family pc2 <- powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous", -1.5), list("aldous", -1),list("aldous", -0.5), list("aldous", 0),list("aldous", 0.5)), n=20L, N_null = 20L, N_alt = 10L, distribs = "sampled") # Create a bar plot or ... plot(pc2) # ... a line plot by specifying 'alt_model_params'. plot(pc2, alt_model_params = c(-1.5,-1,-0.5,0,0.5), tss_names = getTSSnames(c("Sackin", "Colless", "B1I")), tss_colors = getTSScolors(c("Sackin", "Colless", "B1I")), alt_model_family = c("Aldous\'", expression(beta)), ylim = c(0,1))# Plotting a 'poweRbal_data' object: pc1 <- powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous",-1), "pda", "etm"), n = 8L, N_null = 40L, N_alt = 20L) plot(pc1) # Plotting a power comparison with a tree model family pc2 <- powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous", -1.5), list("aldous", -1),list("aldous", -0.5), list("aldous", 0),list("aldous", 0.5)), n=20L, N_null = 20L, N_alt = 10L, distribs = "sampled") # Create a bar plot or ... plot(pc2) # ... a line plot by specifying 'alt_model_params'. plot(pc2, alt_model_params = c(-1.5,-1,-0.5,0,0.5), tss_names = getTSSnames(c("Sackin", "Colless", "B1I")), tss_colors = getTSScolors(c("Sackin", "Colless", "B1I")), alt_model_family = c("Aldous\'", expression(beta)), ylim = c(0,1))
powerComp - Compare the power of a set of TSS to identify trees
generated under different alternative models given a null model.
powerComp_RegAcc - Compare the power of a set of TSS to identify trees
generated under different alternative models given a the region(s) of
acceptance.
powerComp( tss, null_model = "yule", alt_models, n, distribs = "exact_if_possible", N_null = 10000L, N_alt = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) powerComp_RegAcc( tss, accept_regions, null_model, alt_models, n, distribs = "exact_if_possible", N_null = 10000L, N_alt = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 )powerComp( tss, null_model = "yule", alt_models, n, distribs = "exact_if_possible", N_null = 10000L, N_alt = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 ) powerComp_RegAcc( tss, accept_regions, null_model, alt_models, n, distribs = "exact_if_possible", N_null = 10000L, N_alt = 1000L, test_type = "two-tailed", correction = "small-sample", sig_lvl = 0.05 )
tss |
Vector containing the names (as character) of the tree shape
statistics that should be compared. You may either use the short names
provided in |
null_model |
The null model that is to be used to determine the power
of the tree shape statistics. In general, it must be a function that
produces rooted binary trees in |
alt_models |
List containing the alternative models that are to be
used to determine the power of the tree shape statistics. Functions that
produce rooted binary trees in |
n |
Integer value that specifies the desired number of leaves, i.e., vertices with in-degree 1 and out-degree 0. |
distribs |
Determines how the distributions (and with that the
bounds of the critical region) are computed. Available are:
|
N_null |
Sample size (integer >=10) if distributions are sampled (default = 10000L). |
N_alt |
Sample size (integer >=10) for the alternative models to estimate the power (default = 1000L). |
test_type |
Determines the method. Available are:
|
correction |
Specifies the desired correction method.
Available are:
|
sig_lvl |
Level of significance (default = 0.05, must be >0 and <1). |
accept_regions |
Numeric matrix (one row per TSS) with two or four
columns: The first two columns contain the interval limits of the region
of acceptance, i.e., we reject the null hypothesis for values strictly
outside of this interval. The third and fourth columns contain the
probabilities to reject the null hypothesis if values equal the lower or
upper bound, respectively. If the last two columns are missing they are
interpreted as zeroes. See return value of |
powerComp Returns an object of class 'poweRbal_data' which
is a list containing the following objects:
power: Numeric matrix containing the power values (one row per TSS and one
column per alternative model).
accept_regions: Numeric matrix containing the information on the region of
acceptance (one row per TSS and four columns).
CIradius: Numeric matrix containing the confidence interval radii (one row
per TSS and one column per alternative model).
actual_sample_sizes: Numeric vector containing the actual sample sizes
under each alternative model as some models do not always successfully
generate trees.
other input data.
powerComp_RegAcc Returns an object of class 'poweRbal_data'
similar to powerComp.
S. J. Kersting, K. Wicke, and M. Fischer. Tree balance in phylogenetic models. arXiv:2406.05185, 2024.
powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous",-1), "pda", "etm"), n = 10L, distribs = "sampled", N_null = 40L, N_alt = 20L) powerComp_RegAcc(tss = c("Sackin", "Colless", "B1I"), accept_regions = getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, null_model = "etm", N_null = 20L, distribs = "sampled"), null_model = "etm", distribs = "sampled", alt_models = list(list("aldous",-1), "pda", "yule"), n = 6L, N_null = 20L, N_alt = 20L)powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous",-1), "pda", "etm"), n = 10L, distribs = "sampled", N_null = 40L, N_alt = 20L) powerComp_RegAcc(tss = c("Sackin", "Colless", "B1I"), accept_regions = getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, null_model = "etm", N_null = 20L, distribs = "sampled"), null_model = "etm", distribs = "sampled", alt_models = list(list("aldous",-1), "pda", "yule"), n = 6L, N_null = 20L, N_alt = 20L)
This function prints the contents of an object of class poweRbal_data.
It provides a brief summary of the object structure and its contents.
This function provides a summary of an object of class poweRbal_data.
It offers a high-level overview of the contents and their structure.
## S3 method for class 'poweRbal_data' print(x, ...) ## S3 method for class 'poweRbal_data' summary(object, ...)## S3 method for class 'poweRbal_data' print(x, ...) ## S3 method for class 'poweRbal_data' summary(object, ...)
x |
An object of class
|
... |
Additional arguments passed to the |
object |
An object of class |
print.poweRbal_data No return value, as the primary purpose
of this function is the side effect (printing).
summary.poweRbal_data No return value, as the primary purpose
of this function is the side effect (printing summary).
# Printing a 'poweRbal_data' object: pc1 <- powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous",-1), "pda", "etm"), n = 8L, N_null = 40L, N_alt = 20L) pc1 # Summary of a 'poweRbal_data' object: summary(pc1)# Printing a 'poweRbal_data' object: pc1 <- powerComp(tss = c("Sackin", "Colless", "B1I"), alt_models = list(list("aldous",-1), "pda", "etm"), n = 8L, N_null = 40L, N_alt = 20L) pc1 # Summary of a 'poweRbal_data' object: summary(pc1)
showTSSdata - This function plots histograms of TSS data.
showTSSdata(tss_data, main = NULL, xlab = NULL, sig_lvl = 0.05, ...)showTSSdata(tss_data, main = NULL, xlab = NULL, sig_lvl = 0.05, ...)
tss_data |
Numeric matrix of TSS values (one row per TSS). The row names are used as names for the TSS. |
main |
Title (default = NULL). A generic title is created by default. |
xlab |
Label of x-axis (default = NULL). A generic label is created by default. |
sig_lvl |
Level of significance (default=0.05, must be >0 and <1). |
... |
Add further specifications for |
showTSSdata No return value, called for side effects
(plotting).
showTSSdata(tss_data = getTSSdata_trees(tss = c("Colless", "Sackin"), treeList = lapply(1:20L, function(x) genYuleTree(10))), breaks=15)showTSSdata(tss_data = getTSSdata_trees(tss = c("Colless", "Sackin"), treeList = lapply(1:20L, function(x) genYuleTree(10))), breaks=15)
tssInfo - List that provides information on available tree shape
statistics (TSS) from the package 'treebalance'.
Most of them are either balance or imbalance indices. The indices are grouped
by their families and otherwise sorted alphabetically by their full names.
The following information is provided:
short: Abbreviation of the name (plain characters).
simple: Simplified full name (plain characters).
name: Full name (partly expressions as some names use special symbols).
func: Function of the TSS.
type: Either "tss", "bali", or "imbali" expressing what type of tree shape
statistic it is.
only_binary: TRUE if TSS is suitable only for binary trees, FALSE if also
applicable to arbitrary rooted trees.
safe_n : Integer vector with two entries specifying the range of leaf
numbers n for which the TSS can be (safely) used, without
warnings for too few leaves or values reaching Inf for too many
leaves.
c(4,800), for example means that this TSS should only be applied
on trees with 4 to 800 leaves. 'Inf' as the second entry means
that there is no specific upper limit, but that the size of the
tree itself and the computation time are the limiting factors.
col: Color for the TSS (related TSS have similar colors).
tssInfotssInfo
An object of class list of length 29.
M. Fischer, L.Herbst, S. J. Kersting, L. Kühn, and K. Wicke, Tree Balance Indices - A Comprehensive Survey. Springer, 2023. ISBN: 978-3-031-39799-8
tssInfo$ALD$name tssInfo$ALD$func(genYuleTree(6))tssInfo$ALD$name tssInfo$ALD$func(genYuleTree(6))