| Title: | Automatic Shift Detection using a Phylogenetic EM |
|---|---|
| Description: | Implementation of the automatic shift detection method for Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on phylogenies. Some tools to handle equivalent shifts configurations are also available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and Bastide et al. (2018) <doi:10.1093/sysbio/syy005>. |
| Authors: | Paul Bastide [aut, cre], Mahendra Mariadassou [ctb] |
| Maintainer: | Paul Bastide <[email protected]> |
| License: | GPL (>= 2) | file LICENSE |
| Version: | 1.7.0 |
| Built: | 2024-08-28 04:52:18 UTC |
| Source: | https://github.com/pbastide/phylogeneticem |
allocate_regimes_from_shifts allocate a number (from 0 to the number
of shifts) to each node, corresponding to its regime : all nodes below shift
i are numbered by i.
allocate_regimes_from_shifts(phylo, shifts_edges)allocate_regimes_from_shifts(phylo, shifts_edges)
phylo |
a phylogenetic tree, class |
shifts_edges |
edges were the shifts are. |
Vector of size (ntaxa + Nnode) of the regimes of each node and tip.
allocate_shifts_from_regimes returns the position of the shifts induced
by the allocation of the regimes. Only works in an "infinite site" model.
allocate_shifts_from_regimes(phylo, regimes)allocate_shifts_from_regimes(phylo, regimes)
phylo |
a phylogenetic tree, class |
regimes |
: vector of size (ntaxa + Nnode) of the regimes of each node and tip. |
Vector of edges numbers where the shifts are.
check_parsimony take a vector of shifts edges, and check whether the
number of groups of the tips induced by this allocation is exactly the number of
shifts plus one. This is equivalent to parsimony when there is no homoplasy (i.e. no
convergent regimes).
check_parsimony(tree, edges, ...)check_parsimony(tree, edges, ...)
tree |
phylogenetic tree |
edges |
a vector of edges of the tree, where the shifts are |
... |
possibly, a list giving the descendant tips of each edge |
This function computes explicitly the clustering of the tips, using
function clusters_from_shifts.
By default, this function uses enumerate_tips_under_edges to compute
the list of tips under each edge, but a list can be provided (to avoid extra
computation, if many tests on the same tree are done).
boolean : TRUE if the allocation is parsimonious.
clusters_from_shifts take a vector of shifts edges, and gives the
clustering of the tips induced by them, in a "no homoplasy" model (i.e. no
convergence is allowed).
clusters_from_shifts(tree, edges, part.list = enumerate_tips_under_edges(tree))clusters_from_shifts(tree, edges, part.list = enumerate_tips_under_edges(tree))
tree |
phylogenetic tree |
edges |
a vector of edges of the tree, where the shifts are |
part.list |
a list giving the descendant tips of each edge |
By default, this function uses enumerate_tips_under_edges to compute
the list of tips under each edge.
list of size n+m-1, entry i is the vector of tips bellow edge i.
compute_betas_from_shifts computes the optimal values at the nodes and tips of the
tree, given the value at the root and the list of shifts occurring in the tree.
It assumes an OU model.
compute_betas_from_shifts(phylo, optimal.value, shifts)compute_betas_from_shifts(phylo, optimal.value, shifts)
phylo |
a phylogenetic tree, class |
optimal.value |
the optimal value at the root of the tree. |
shifts |
position and values of the shifts . |
Note that this is intended to be an internal function, and should not be used.
In general, use node_optimal_values to get optimal values
from a set of parameters.
Vector of size (ntaxa + Nnode) of the optimal values at the tips of the tree.
compute_dist_phy computes the phylogenetic distances d_ij between all the
tips i, j.
compute_dist_phy(phy)compute_dist_phy(phy)
phy |
a phylogenetic tree of class |
This function relies on ape function
dist.nodes.
a matrix of phylogenetic distances, ordered as the tips of the
tree. The matrix is of type symmetricMatrix-class.
compute_shifts_from_betas computes the list of shifts corresponding
to the vector of optimal values on nodes.
compute_shifts_from_betas(phylo, betas)compute_shifts_from_betas(phylo, betas)
phylo |
a phylogenetic tree, class |
betas |
vector of size (ntaxa + Nnode) of the optimal values at each node and tip. |
This function uses function fun on each row of matrix of edges.
vector of shifts.
compute_stationary_variance computes the stationary variance matrix of
an OU process.
compute_stationary_variance(variance, selection.strength)compute_stationary_variance(variance, selection.strength)
variance |
the variance (rate matrix) of the process. |
selection.strength |
the selection strength (alpha) matrix of the process. |
A positive definite Matrix of class dpoMatrix-class.
compute_times_ca computes the times t_ij between the root and the common
ancestor of two tips i, j.
compute_times_ca(phy)compute_times_ca(phy)
phy |
a phylogenetic tree of class |
This function relies on ape functions
node.depth.edgelength and mrca.
a matrix of times of shared evolution, ordered as the tips of the
tree. The matrix is of type symmetricMatrix-class.
correspondenceEdges takes edges numbers on an input tree, and gives back
their corresponding numbers on the output tree.
correspondenceEdges(edges, from, to)correspondenceEdges(edges, from, to)
edges |
vector of index of edges in the tree "from" |
from |
initial input tree (format " |
to |
aimed output tree (format " |
vector of index of edges in the tree "to"
enlight.PhyloEM takes an object of class PhyloEM,
and returns the same object, without saving the quantities that can be easily
re-computed using function imputed_traits.PhyloEM.
enlight(x) ## S3 method for class 'PhyloEM' enlight(x)enlight(x) ## S3 method for class 'PhyloEM' enlight(x)
x |
an object of class |
The resulting object can be much lighter, saving a lot of memory space, but each
call to the function imputed_traits.PhyloEM will be longer. As
function plot.PhyloEM relies on this function, this makes the
plotting also longer.
This has the same effect as setting the option "light_result=TRUE" in the
call of PhyloEM.
Same as entry, lighter.
enlight(PhyloEM): PhyloEM object
PhyloEM, imputed_traits.PhyloEM,
plot.PhyloEM
enumerate_parsimony enumerate all the equivalent allocation of the
regimes in the tree, a clustering of the tips being given. The number of such
equivalent regimes is given by parsimonyNumber (which is faster).
enumerate_parsimony(phylo, clusters = rep(1, length(phylo$tip.label)))enumerate_parsimony(phylo, clusters = rep(1, length(phylo$tip.label)))
phylo |
a phylogenetic tree, class |
clusters |
a vector representing the group of each tip. (Default to only one group with all the tips.) |
Function extract.enumerate_parsimony furnishes the result in a
human readable form (for any subtree).
Function plot.enumerate_parsimony plots all the solutions found on
the tree.
an S3 object of class "enumerate_parsimony", with:
an object of class "parsimonyCost", result
of function parsimonyCost.
a list of size Nnode + ntaxa. Each entry i of the list represents the solutions for the subtree starting at node i. It is a list with nclus entries, each entry being a matrix. A line of the kth matrix for the ith node is one possible allocation of the shifts, starting with regime k for node i.
the entry phylogenetic tree
extract.enumerate_parsimony,
plot.enumerate_parsimony, parsimonyCost,
parsimonyNumber, partitionsNumber,
equivalent_shifts
tree <- read.tree(text="(((A,B),C),D);") plot(tree) clusters <- c(0, 1, 2, 2) sols <- enumerate_parsimony(tree, clusters) plot(sols) ## Extract the parsimonious solutions from the root extract(sols) # each line is a solution, with states of each node ## Extract the number of solutions from the root extract(sols, what = "number") extract(parsimonyNumber(tree, clusters)) # same result, more efficient ## Extract the cost of the solutions from the root extract(sols, what = "cost") extract(parsimonyCost(tree, clusters)) # same result, more efficient: ## Extract for the sub-tree below node 7 extract(sols, 7) # NAs: non-existing nodes in the sub-treetree <- read.tree(text="(((A,B),C),D);") plot(tree) clusters <- c(0, 1, 2, 2) sols <- enumerate_parsimony(tree, clusters) plot(sols) ## Extract the parsimonious solutions from the root extract(sols) # each line is a solution, with states of each node ## Extract the number of solutions from the root extract(sols, what = "number") extract(parsimonyNumber(tree, clusters)) # same result, more efficient ## Extract the cost of the solutions from the root extract(sols, what = "cost") extract(parsimonyCost(tree, clusters)) # same result, more efficient: ## Extract for the sub-tree below node 7 extract(sols, 7) # NAs: non-existing nodes in the sub-tree
enumerate_tips_under_edges gives, for each edge of the tree, the labels
of the tips that have this edge as an ancestor.
enumerate_tips_under_edges(tree)enumerate_tips_under_edges(tree)
tree |
phylogenetic tree, class |
This function uses function prop.part from package ape.
list of size Nedge, entry i is the vector of tips bellow edge i.
equivalent_shifts computes the equivalent shifts positions and their
corresponding values, assuming an ultrametric tree.
equivalent_shifts( phylo, params, T_tree = incidence.matrix(phylo), part.list = enumerate_tips_under_edges(phylo), times_shared = NULL )equivalent_shifts( phylo, params, T_tree = incidence.matrix(phylo), part.list = enumerate_tips_under_edges(phylo), times_shared = NULL )
phylo |
a phylogenetic tree, of class |
params |
an object of class |
T_tree |
(optional) matrix of incidence of the tree, result of function
|
part.list |
(optional) list of partition of the tree, result of function
|
times_shared |
(optional) a matrix, result of function
|
This function is only valid for ultrametric trees, and for models: BM, OU with fixed root or stationary root. It assumes that there are no homoplasies.
object of class equivalent_shifts, with entries:
matrix of equivalent shifts
matrix of corresponding shifts values
the entry phylogenetic tree
the dimension
plot.equivalent_shifts,
extract.equivalent_shifts, params_BM,
params_OU, enumerate_parsimony
if (requireNamespace("TreeSim", quietly = TRUE)) { ## Simualte a tree set.seed(17920902) ntaxa = 20 phylo <- TreeSim::sim.bd.taxa.age(n = ntaxa, numbsim = 1, lambda = 0.1, mu = 0, age = 1, mrca = TRUE)[[1]] ## Define parameters (BM, fixed root) params <- params_BM(p = 4, edges = c(6, 17, 31), values = cbind(1:4, -(1:4), rep(1, 4))) ## Find equivalent solutions and plot them eq_shifts <- equivalent_shifts(phylo, params) eq_shifts plot(eq_shifts) ## Extract the values # Shifts values for trait 2, for the three shifts (rows), and three solutions (columns) extract(eq_shifts, trait = 2, what = "shifts_values") # Root values for trait 4, for the tree solutions (columns) extract(eq_shifts, trait = 4, what = "root_values") ## Define parameters (OU, stationary root params <- params_OU(p = 4, edges = c(6, 17, 31), selection.strength = 0.1, values = cbind(1:4, -(1:4), rep(1, 4)), random = TRUE) ## Find equivalent solutions and plot them eq_shifts <- equivalent_shifts(phylo, params) eq_shifts plot(eq_shifts) ## Extract the values # Shifts values for trait 2, for the three shifts (rows), and three solutions (columns) extract(eq_shifts, trait = 2, what = "shifts_values") # Root values for trait 4, for the three solutions (columns) extract(eq_shifts, trait = 4, what = "root_values") }if (requireNamespace("TreeSim", quietly = TRUE)) { ## Simualte a tree set.seed(17920902) ntaxa = 20 phylo <- TreeSim::sim.bd.taxa.age(n = ntaxa, numbsim = 1, lambda = 0.1, mu = 0, age = 1, mrca = TRUE)[[1]] ## Define parameters (BM, fixed root) params <- params_BM(p = 4, edges = c(6, 17, 31), values = cbind(1:4, -(1:4), rep(1, 4))) ## Find equivalent solutions and plot them eq_shifts <- equivalent_shifts(phylo, params) eq_shifts plot(eq_shifts) ## Extract the values # Shifts values for trait 2, for the three shifts (rows), and three solutions (columns) extract(eq_shifts, trait = 2, what = "shifts_values") # Root values for trait 4, for the tree solutions (columns) extract(eq_shifts, trait = 4, what = "root_values") ## Define parameters (OU, stationary root params <- params_OU(p = 4, edges = c(6, 17, 31), selection.strength = 0.1, values = cbind(1:4, -(1:4), rep(1, 4)), random = TRUE) ## Find equivalent solutions and plot them eq_shifts <- equivalent_shifts(phylo, params) eq_shifts plot(eq_shifts) ## Extract the values # Shifts values for trait 2, for the three shifts (rows), and three solutions (columns) extract(eq_shifts, trait = 2, what = "shifts_values") # Root values for trait 4, for the three solutions (columns) extract(eq_shifts, trait = 4, what = "root_values") }
EstimateEM performs one EM for one given number of shifts. It is called
from function PhyloEM. Its use is mostly internal, and most user
should not need it.
estimateEM( phylo, Y_data, Y_data_imp = Y_data, process = c("BM", "OU", "scOU", "rBM"), independent = FALSE, tol_EM = list(variance = 10^(-2), value.root = 10^(-2), exp.root = 10^(-2), var.root = 10^(-2), selection.strength = 10^(-2), normalized_half_life = 10^(-2), log_likelihood = 10^(-2)), Nbr_It_Max = 500, method.variance = c("simple", "upward_downward"), method.init = c("default", "lasso"), method.init.alpha = c("default", "estimation"), method.init.alpha.estimation = c("regression", "regression.MM", "median"), nbr_of_shifts = 0, random.root = TRUE, stationary.root = TRUE, alpha_known = FALSE, eps = 10^(-3), known.selection.strength = 1, init.selection.strength = 1, max_selection.strength = 100, use_sigma_for_lasso = TRUE, max_triplet_number = 10000, min_params = list(variance = 0, value.root = -10^(5), exp.root = -10^(5), var.root = 0, selection.strength = 0), max_params = list(variance = 10^(5), value.root = 10^(5), exp.root = 10^(5), var.root = 10^(5), selection.strength = 10^(5)), var.init.root = diag(1, nrow(Y_data)), variance.init = diag(1, nrow(Y_data), nrow(Y_data)), methods.segmentation = c("lasso", "same_shifts", "best_single_move"), check.tips.names = FALSE, times_shared = NULL, distances_phylo = NULL, subtree.list = NULL, T_tree = NULL, U_tree = NULL, h_tree = NULL, F_moments = NULL, tol_half_life = TRUE, warning_several_solutions = TRUE, convergence_mode = c("relative", "absolute"), check_convergence_likelihood = TRUE, sBM_variance = FALSE, method.OUsun = c("rescale", "raw"), K_lag_init = 0, allow_negative = FALSE, trait_correlation_threshold = 0.9, ... )estimateEM( phylo, Y_data, Y_data_imp = Y_data, process = c("BM", "OU", "scOU", "rBM"), independent = FALSE, tol_EM = list(variance = 10^(-2), value.root = 10^(-2), exp.root = 10^(-2), var.root = 10^(-2), selection.strength = 10^(-2), normalized_half_life = 10^(-2), log_likelihood = 10^(-2)), Nbr_It_Max = 500, method.variance = c("simple", "upward_downward"), method.init = c("default", "lasso"), method.init.alpha = c("default", "estimation"), method.init.alpha.estimation = c("regression", "regression.MM", "median"), nbr_of_shifts = 0, random.root = TRUE, stationary.root = TRUE, alpha_known = FALSE, eps = 10^(-3), known.selection.strength = 1, init.selection.strength = 1, max_selection.strength = 100, use_sigma_for_lasso = TRUE, max_triplet_number = 10000, min_params = list(variance = 0, value.root = -10^(5), exp.root = -10^(5), var.root = 0, selection.strength = 0), max_params = list(variance = 10^(5), value.root = 10^(5), exp.root = 10^(5), var.root = 10^(5), selection.strength = 10^(5)), var.init.root = diag(1, nrow(Y_data)), variance.init = diag(1, nrow(Y_data), nrow(Y_data)), methods.segmentation = c("lasso", "same_shifts", "best_single_move"), check.tips.names = FALSE, times_shared = NULL, distances_phylo = NULL, subtree.list = NULL, T_tree = NULL, U_tree = NULL, h_tree = NULL, F_moments = NULL, tol_half_life = TRUE, warning_several_solutions = TRUE, convergence_mode = c("relative", "absolute"), check_convergence_likelihood = TRUE, sBM_variance = FALSE, method.OUsun = c("rescale", "raw"), K_lag_init = 0, allow_negative = FALSE, trait_correlation_threshold = 0.9, ... )
phylo |
A phylogenetic tree of class |
Y_data |
Matrix of data at the tips, size p x ntaxa. Each line is a trait, and each column is a tip. The column names are checked against the tip names of the tree. |
Y_data_imp |
(optional) imputed data if previously computed, same format as
|
process |
The model used for the fit. One of "BM" (for a full BM model, univariate or multivariate); "OU" (for an OU with independent traits, univariate or multivariate); or "scOU" (for a "scalar OU" model, see details). |
independent |
Are the trait assumed to be independent from one another? Default to FALSE. OU in a multivariate setting only works if TRUE. |
tol_EM |
the tolerance for the convergence of the parameters. A named list, with items:
|
Nbr_It_Max |
the maximal number of iterations of the EM allowed. Default to 500 iterations. |
method.variance |
Algorithm to be used for the moments computations at the E step. One of "simple" for the naive method; of "upward_downward" for the Upward Downward method (usually faster). Default to "upward_downward". |
method.init |
The initialization method. One of "lasso" for the LASSO base initialization method; or "default" for user-specified initialization values. Default to "lasso". |
method.init.alpha |
For OU model, initialization method for the selection
strength alpha. One of "estimation" for a cherry-based initialization, using
|
method.init.alpha.estimation |
If method.init.alpha="estimation",
choice of the estimation(s) methods to be used. Choices among "regression",
(method="M" is passed to |
nbr_of_shifts |
the number of shifts allowed. |
random.root |
whether the root is assumed to be random (TRUE) of fixed (FALSE). Default to TRUE |
stationary.root |
whether the root is assumed to be in the stationary state. Default to TRUE. |
alpha_known |
is the selection strength assumed to be known ? Default to FALSE. |
eps |
tolerance on the selection strength value before switching to a BM. Default to 10^(-3). |
known.selection.strength |
if |
init.selection.strength |
(optional) a starting point for the selection strength value. |
max_selection.strength |
the maximal value allowed of the selection strength. Default to 100. |
use_sigma_for_lasso |
whether to use the first estimation of the variance matrix in the lasso regression. Default to TRUE. |
max_triplet_number |
for the initialization of the selection strength value (when estimated), the maximal number of triplets of tips to be considered. |
min_params |
a named list containing the minimum allowed values for the parameters. If the estimation is smaller, then the EM stops, and is considered to be divergent. Default values:
|
max_params |
a named list containing the maximum allowed values for the parameters. If the estimation is larger, then the EM stops, and is considered to be divergent. Default values:
|
var.init.root |
optional initialization value for the variance of the root. |
variance.init |
optional initialization value for the variance. |
methods.segmentation |
For OU, method(s) used at the M step to find new candidate shifts positions. Choices among "lasso" for a LASSO-based algorithm; and "best_single_move" for a one-move at a time based heuristic. Default to both of them. Using only "lasso" might speed up the function a lot. |
check.tips.names |
whether to check the tips names of the tree against the column names of the data. Default to TRUE. |
times_shared |
(optional) times of shared ancestry of all nodes and tips,
result of function |
distances_phylo |
(optional) phylogenetic distances, result of function
|
subtree.list |
(optional) tips descendants of all the edges, result of
function |
T_tree |
(optional) matrix of incidence of the tree, result of function
|
U_tree |
(optional) full matrix of incidence of the tree, result of function
|
h_tree |
(optional) total height of the tree. |
F_moments |
(optional, internal) |
tol_half_life |
should the tolerance criterion be applied to the phylogenetic half life (TRUE, default) or to the raw selection strength ? |
warning_several_solutions |
whether to issue a warning if several equivalent solutions are found (default to TRUE). |
convergence_mode |
one of "relative" (the default) or "absolute". Should the tolerance be applied to the raw parameters, or to the renormalized ones ? |
check_convergence_likelihood |
should the likelihood be taken into consideration for convergence assessment ? (default to TRUE). |
sBM_variance |
Is the root of the BM supposed to be random and "stationary"? Used for BM equivalent computations. Default to FALSE. |
method.OUsun |
Method to be used in univariate OU. One of "rescale" (rescale the tree to fit a BM) or "raw" (directly use an OU, only available for univariate processes). |
K_lag_init |
Number of extra shifts to be considered at the initialization step. Increases the accuracy, but can make computations quite slow of taken too high. Default to 5. |
allow_negative |
whether to allow negative values for alpha (Early Burst).
See documentation of |
trait_correlation_threshold |
the trait correlation threshold to stop the analysis. Default to 0.9. |
... |
Further arguments to be passed to |
See documentation of PhyloEM for further details.
All the parameters monitoring the EM (like tol_EM, Nbr_It_Max, etc.)
can be called from PhyloEM.
An object of class EstimateEM.
extract the needed quantities out of an S3 object.
extract(x, ...)extract(x, ...)
x |
an S3 object. |
... |
further arguments to be passed to the specific method. |
An integer giving the number of equivalent parsimonious solutions.
extract.parsimonyNumber,
extract.parsimonyCost,
extract.enumerate_parsimony,
extract.partitionsNumber
enumerate_parsimony at a node.extract.enumerate_parsimony returns a matrix containing all the
possible regime allocations for the nodes of a given subtree.
## S3 method for class 'enumerate_parsimony' extract( x, node = attr(x$allocations, "ntaxa") + 1, what = c("solutions", "number", "cost"), ... )## S3 method for class 'enumerate_parsimony' extract( x, node = attr(x$allocations, "ntaxa") + 1, what = c("solutions", "number", "cost"), ... )
x |
an object of class " |
node |
the node where to retrieve the parsimony number. Default to the root of the tree. |
what |
the quantity to retrieve. Either "solutions" for the full solutions, "number" for the number of solutions, or "cost" for the minimal cost of a solution. Default to "solutions" |
... |
unused |
A matrix with ntaxa + Nnode columns, and as many rows as the number of possible parsimonious reconstructions.
enumerate_parsimony, plot.enumerate_parsimony
extract.equivalent_shifts takes an object of class
equivalent_shifts, result of function equivalent_shifts,
and returns the shifts of root values for a given trait.
## S3 method for class 'equivalent_shifts' extract(x, trait = 1, what = c("shifts_values", "root_values"), ...)## S3 method for class 'equivalent_shifts' extract(x, trait = 1, what = c("shifts_values", "root_values"), ...)
x |
an object of class |
trait |
the number of the trait to be extracted. Default to 1. |
what |
one of "shifts_values" or "root_values". |
... |
unused. |
A matrix with the values of the shifts (what = "shifts_values") or
the root (what = "root_values") for the trait for each equivalent
configuration. Each column is one configuration.
equivalent_shifts, plot.equivalent_shifts,
equivalent_shifts_edges
extract.parsimonyCost takes an object of class "parsimonyCost",
result of function parsimonyCost, and computes the minimum cost
at the given node.
## S3 method for class 'parsimonyCost' extract(x, node = attr(x, "ntaxa") + 1, ...)## S3 method for class 'parsimonyCost' extract(x, node = attr(x, "ntaxa") + 1, ...)
x |
an object of class " |
node |
the root node of the subtree. By default, the root of the tree. |
... |
unused |
An integer giving the minimum cost of the subtree.
extract.parsimonyNumber takes the two matrices computed by
parsimonyNumber, and compute the actual number of parsimonious
solution for any subtree starting from a given node.
## S3 method for class 'parsimonyNumber' extract( x, node = attr(x$nbrReconstructions, "ntaxa") + 1, what = c("number", "cost"), ... )## S3 method for class 'parsimonyNumber' extract( x, node = attr(x$nbrReconstructions, "ntaxa") + 1, what = c("number", "cost"), ... )
x |
an object of class " |
node |
the root node of the subtree. By default, the root of the tree. |
what |
the quantity to retrieve. Either "number" for the number of solutions, or "cost" for the minimal cost of a solution. Default to "number". |
... |
unused |
The parsimonious solutions are the one with the minimum number of shifts (that are given by matrix costReconstructions). This function sums the number of solutions (given in matrix nbrReconstructions) that have the minimum number of shifts.
An integer giving the number of equivalent parsimonious solutions.
partitionsNumber
extract.partitionsNumber extracts the number of partitions for a
given sub-tree, either marked or non-marked.
## S3 method for class 'partitionsNumber' extract( x, node = attr(x, "ntaxa") + 1, npart = attr(x, "npart"), marked = FALSE, ... )## S3 method for class 'partitionsNumber' extract( x, node = attr(x, "ntaxa") + 1, npart = attr(x, "npart"), marked = FALSE, ... )
x |
an object of class |
node |
the root node of the subtree where to get the result. Default to the root of the tree. |
npart |
the number of partitions (colors) allowed at the tips.
Default to the value used in the call of function
|
marked |
whether to extract the marked (TRUE) or un-marked (FALSE) partitions. The number of models is the number of un-marked partitions. Default to FALSE. |
... |
unused. |
the number of partitions with npart colors, on the sub-tree starting at node, marked or not.
extract.simul_process takes an object of class "simul_process",
result of function simul_process, and extracts the traits values,
expectations or optimal values at the tips or the internal nodes.
## S3 method for class 'simul_process' extract( x, where = c("tips", "nodes"), what = c("states", "expectations", "optimal.values"), ... )## S3 method for class 'simul_process' extract( x, where = c("tips", "nodes"), what = c("states", "expectations", "optimal.values"), ... )
x |
an object of class " |
where |
one of "tips" (the default) or "nodes". Where to extract the results. |
what |
one of "states" (the default), "expectation", or "optimal.values". |
... |
unused |
##
A matrix giving the selected quantities at the selected nodes or tips. If the tips or nods are labeled, then the colnames of the matrix are set accordingly.
Grid so that 2*ln(2)*quantile(d_ij)/factor_up_alpha < t_1/2 < factor_down_alpha * ln(2) * h_tree, with t_1/2 the phylogenetic half life: t_1/2 = log(2)/alpha. Ensures that for alpha_min, it is almost a BM, and for alpha_max, almost all the tips are decorrelated.
find_grid_alpha( phy, alpha = NULL, nbr_alpha = 10, factor_up_alpha = 2, factor_down_alpha = 3, quantile_low_distance = 1e-04, log_transform = TRUE, allow_negative = FALSE, ... )find_grid_alpha( phy, alpha = NULL, nbr_alpha = 10, factor_up_alpha = 2, factor_down_alpha = 3, quantile_low_distance = 1e-04, log_transform = TRUE, allow_negative = FALSE, ... )
phy |
phylogenetic tree of class " |
alpha |
fixed vector of alpha values if already known. Default to NULL. |
nbr_alpha |
the number of elements in the grid |
factor_up_alpha |
factor for up scalability |
factor_down_alpha |
factor for down scalability |
quantile_low_distance |
quantile for min distance |
log_transform |
whether to take a log scale for the spacing of alpha values. Default to TRUE. |
allow_negative |
whether to allow negative values for alpha (Early Burst).
See documentation of |
... |
not used. |
If quantile_low_distance=0, then quantile(d_ij)=min(d_ij), and, for any
two tips i,j, the correlation between i and j is bounded by exp(-factor_up_alpha/2).
Those values of alpha will be used for the re-scaling of the tree, which has an
exponential term in exp(2*alpha*h). The function makes sure that this number is
below the maximal float allowed (equals to .Machine$double.xmax).
A grid of alpha values
transform_branch_length, .Machine
find_rotation takes two fits from from PhyloEM,
and test if their datasets are equal up to a rotation.
find_rotation(res1, res2, tol = NULL)find_rotation(res1, res2, tol = NULL)
res1 |
an object of class |
res2 |
an object of class |
tol |
relative numerical tolerance. Default to |
If appropriate, the rotation matrix rot such that dat1 = rot
This function takes an object of class PhyloEM, result of function
PhyloEM, and return the values of the model selection criterion
for each value of K.
get_criterion(res, method.selection = NULL)get_criterion(res, method.selection = NULL)
res |
an object of class |
method.selection |
select the parameters to plot. One of "LINselect", "DDSE",
"Djump" or "likelihood" (for un-penalized likelihood). Default to "LINselect". See
|
A named vector with the values of the criterion for each number of shift K.
params_process.PhyloEM, plot.PhyloEM, plot_criterion
imputed_traits.PhyloEM takes an object of class PhyloEM,
and returns the imputed traits values, either at the internal nodes (ancestral
state reconstruction) or at the tips (data imputation)
imputed_traits(x, ...) ## S3 method for class 'PhyloEM' imputed_traits( x, trait = 1, save_all = FALSE, where = c("nodes", "tips"), what = c("imputed", "variances", "expectations"), params = NULL, method.selection = NULL, reconstructed_states = NULL, ... )imputed_traits(x, ...) ## S3 method for class 'PhyloEM' imputed_traits( x, trait = 1, save_all = FALSE, where = c("nodes", "tips"), what = c("imputed", "variances", "expectations"), params = NULL, method.selection = NULL, reconstructed_states = NULL, ... )
x |
an object of class |
... |
further arguments to be passed on to
|
trait |
an integer giving the trait to extract. Default to 1. |
save_all |
if TRUE, arguments |
where |
either "nodes" for ancestral state reconstruction, or "tips" for data imputation. |
what |
the quantity to retrieve. Either the imputed traits (default), their conditional variances, or the simple expectations under the selected process. |
params |
(optional) some user-specified parameters.
Must be of class |
method.selection |
(optional) the method selection to be used. One of "LINselect", "DDSE", "Djump". Default to "LINselect". |
reconstructed_states |
if the reconstructed states have already been
computed (by a previous call of the function, with |
A matrix or array with the computed quantities.
imputed_traits(PhyloEM): PhyloEM object
params_process.PhyloEM, PhyloEM
incidence.matrix computes the incidence matrix T of a tree : for a
lineage i and a branch b, T[i,b]=1 if b is in the lineage i, and 0
otherwise.
incidence.matrix(phylo)incidence.matrix(phylo)
phylo |
a phylogenetic tree, class |
Matrix of incidence, size Nedge x ntaxa.
incidence.matrix.full computes the incidence matrix U of a tree : for a
node i and a branch b, U[i,b]=1 if b is in the lineage i, and 0
otherwise.
incidence.matrix.full(phylo)incidence.matrix.full(phylo)
phylo |
a phylogenetic tree, class |
Matrix of incidence, size ntaxa + Nnode.
log_likelihood computes the log likelihood of some parameters.
log_likelihood(x, ...) ## S3 method for class 'params_process' log_likelihood(x, Y_data, phylo, ...) ## S3 method for class 'PhyloEM' log_likelihood(x, ...)log_likelihood(x, ...) ## S3 method for class 'params_process' log_likelihood(x, Y_data, phylo, ...) ## S3 method for class 'PhyloEM' log_likelihood(x, ...)
x |
an object of class |
... |
for a |
Y_data |
matrix of data at the tips, size p x ntaxa. Each line is a trait, and each column is a tip. The column names are checked against the tip names of the tree. |
phylo |
a phylogenetic tree, class |
The log likelihood of the data with the provided parameters on the tree.
log_likelihood(params_process): params_process object
log_likelihood(PhyloEM): PhyloEM object
merge_rotations takes several fits from PhyloEM, and
merge them according to the best score (maximum likelihood or least squares).
For each number of shifts,
The datasets needs to be equal up to a rotation. This is tested thanks to a QR
decomposition, see function find_rotation.
merge_rotations(..., method.selection = NULL, tol = NULL)merge_rotations(..., method.selection = NULL, tol = NULL)
... |
objects of class |
method.selection |
(optional) selection method to be applied to the merged fit.
See |
tol |
(optional) relative numerical tolerance. See |
An object of class PhyloEM, result of the merge.
## Not run: ## Load Data data(monkeys) ## Run method # Note: use more alpha values for better results. res <- PhyloEM(Y_data = monkeys$dat, ## data phylo = monkeys$phy, ## phylogeny process = "scOU", ## scalar OU random.root = TRUE, ## root is stationary stationary.root = TRUE, K_max = 10, ## maximal number of shifts nbr_alpha = 4, ## number of alpha values parallel_alpha = TRUE, ## parallelize on alpha values Ncores = 2) ## Rotate dataset rot <- matrix(c(cos(pi/4), -sin(pi/4), sin(pi/4), cos(pi/4)), nrow= 2, ncol = 2) Yrot <- t(rot) %*% monkeys$dat rownames(Yrot) <- rownames(monkeys$dat) ## Fit rotated dataset # Note: use more alpha values for better results. res_rot <- PhyloEM(Y_data = Yrot, ## rotated data phylo = monkeys$phy, process = "scOU", random.root = TRUE, stationary.root = TRUE, K_max = 10, nbr_alpha = 4, parallel_alpha = TRUE, Ncores = 2) ## Merge the two res_merge <- merge_rotations(res, res_rot) ## Plot the selected result plot(res_merge) ## Plot the model selection criterion plot_criterion(res_merge) ## End(Not run)## Not run: ## Load Data data(monkeys) ## Run method # Note: use more alpha values for better results. res <- PhyloEM(Y_data = monkeys$dat, ## data phylo = monkeys$phy, ## phylogeny process = "scOU", ## scalar OU random.root = TRUE, ## root is stationary stationary.root = TRUE, K_max = 10, ## maximal number of shifts nbr_alpha = 4, ## number of alpha values parallel_alpha = TRUE, ## parallelize on alpha values Ncores = 2) ## Rotate dataset rot <- matrix(c(cos(pi/4), -sin(pi/4), sin(pi/4), cos(pi/4)), nrow= 2, ncol = 2) Yrot <- t(rot) %*% monkeys$dat rownames(Yrot) <- rownames(monkeys$dat) ## Fit rotated dataset # Note: use more alpha values for better results. res_rot <- PhyloEM(Y_data = Yrot, ## rotated data phylo = monkeys$phy, process = "scOU", random.root = TRUE, stationary.root = TRUE, K_max = 10, nbr_alpha = 4, parallel_alpha = TRUE, Ncores = 2) ## Merge the two res_merge <- merge_rotations(res, res_rot) ## Plot the selected result plot(res_merge) ## Plot the model selection criterion plot_criterion(res_merge) ## End(Not run)
model_selection does the model selection on a fitted PhyloEM
object, and returns the same fitted object.
model_selection(x, ...) ## S3 method for class 'PhyloEM' model_selection( x, method.selection = c("LINselect", "DDSE", "Djump"), C.BM1 = 0.1, C.BM2 = 2.5, C.LINselect = 1.1, independent = FALSE, ... )model_selection(x, ...) ## S3 method for class 'PhyloEM' model_selection( x, method.selection = c("LINselect", "DDSE", "Djump"), C.BM1 = 0.1, C.BM2 = 2.5, C.LINselect = 1.1, independent = FALSE, ... )
x |
a fitted |
... |
Further arguments to be passed to |
method.selection |
Method selection to be used. Several ones can be used at the same time. One of "LINselect" for the Baraud Giraud Huet LINselect method; "DDSE" for the Slope Heuristic or "Djump" for the Jump Heuristic, last two based the Birgé Massart method. |
C.BM1 |
Multiplying constant to be used for the BigeMassart1 method. Need to be positive. Default to 0.1. |
C.BM2 |
Multiplying constant to be used for the BigeMassart2 method. Default to 2.5. |
C.LINselect |
Multiplying constant to be used for the LINselect method. Need to be greater than 1. Default to 1.1. |
independent |
Are the trait assumed to be independent from one another? Default to FALSE. OU in a multivariate setting only works if TRUE. |
The same object, but with a slot corresponding to the model selection used. See
function params_process.PhyloEM to retrieve the selected parameters.
model_selection(PhyloEM): PhyloEM object
PhyloEM, params_process.PhyloEM,
imputed_traits.PhyloEM
Morphometric dataset and phylogeny for brain shape variation of 50 species of New World monkeys (platyrrhine).
monkeysmonkeys
A list containing two objects:
The Phylogenetic tree for the platyrrhine species, pruned to match the species in the morphometric dataset
First two PC scores from a PCA of the species-averaged Procrustes coordinates
Aristide, L., dos Reis, S. F., Machado, A. C., Lima, I., Lopes, R. T. & Perez, S. I. (2016). Brain shape convergence in the adaptive radiation of New World monkeys. Proceedings of the National Academy of Sciences, 113(8), 2158–2163. http://doi.org/10.1073/pnas.1514473113
compute_betas_from_shifts computes the optimal values at the nodes and tips of the
tree, given the value at the root and the list of shifts occurring in the tree.
It assumes an OU model.
node_optimal_values(param, phylo)node_optimal_values(param, phylo)
param |
an object of class |
phylo |
a phylogenetic tree, class |
Matrix of size ntraits x (ntaxa + Nnode) of the optimal values at the node and tips of the tree. Column names correspond to the number of the node in the phylo object.
set.seed(1792) ntaxa = 10 tree <- rphylo(ntaxa, 1, 0.1) # parameters of the process par <- params_process("BM", ## Process p = 2, ## Dimension variance = diag(0.5, 2, 2) + 0.5, ## Rate matrix edges = c(4, 10, 15), ## Positions of the shifts values = cbind(c(5, 4), ## Values of the shifts c(-4, -5), c(5, -3))) plot(par, phylo = tree, traits = 1, value_in_box = TRUE, shifts_bg = "white", root_bg = "white", ancestral_as_shift = TRUE, root_adj = 5) nodelabels() node_optimal_values(par, tree)set.seed(1792) ntaxa = 10 tree <- rphylo(ntaxa, 1, 0.1) # parameters of the process par <- params_process("BM", ## Process p = 2, ## Dimension variance = diag(0.5, 2, 2) + 0.5, ## Rate matrix edges = c(4, 10, 15), ## Positions of the shifts values = cbind(c(5, 4), ## Values of the shifts c(-4, -5), c(5, -3))) plot(par, phylo = tree, traits = 1, value_in_box = TRUE, shifts_bg = "white", root_bg = "white", ancestral_as_shift = TRUE, root_adj = 5) nodelabels() node_optimal_values(par, tree)
params_process for a BMparams_BM creates a coherent object params_process from user
provided values of the parameters. Non specified parameters are set to
default values.
params_BM( p = 1, variance = diag(1, p, p), random = FALSE, value.root = rep(0, p), exp.root = rep(0, p), var.root = diag(1, p, p), edges = NULL, values = matrix(0, p, length(edges)), relativeTimes = NULL, nbr_of_shifts = length(edges), phylo = NULL, sBM_variance = FALSE, trait_names = NULL, ... )params_BM( p = 1, variance = diag(1, p, p), random = FALSE, value.root = rep(0, p), exp.root = rep(0, p), var.root = diag(1, p, p), edges = NULL, values = matrix(0, p, length(edges)), relativeTimes = NULL, nbr_of_shifts = length(edges), phylo = NULL, sBM_variance = FALSE, trait_names = NULL, ... )
p |
the dimension (number of traits) of the parameters. Default to 1. |
variance |
the variance (rate matrix) of the BM. Default to
|
random |
whether the root of the BM is random (TRUE) or fixed (FALSE). Default to FALSE. |
value.root |
if random=FALSE, the root value. Default to 0. |
exp.root |
if random=TRUE, the root expectation. Default to 0. |
var.root |
if random=TRUE, the root variance. Default to
|
edges |
a vector of edges where the shifts occur. Default to NULL (no shift). |
values |
a matrix of shift values, with p lines and as many columns as
the number of shifts. Each column is the p values for one shift. Default to
|
relativeTimes |
(unused) the relative position of the shift on the branch, between 0 (beginning of the branch) and 1 (end of the branch). Default to 0. |
nbr_of_shifts |
the number of shifts to use (randomly drawn). Use only
if |
phylo |
a phylogenetic tree of class |
sBM_variance |
if the root is random, does it depend on the length of the root edge ? (For equivalent purposes with a rescaled OU). Default to FALSE. If TRUE, a phylogenetic tree with root edge length must be provided. |
trait_names |
vector of trait names values. Must be of length p. |
... |
unused. |
an object of class params_process.
params_process for an OUparams_OU creates a coherent object params_process from user
provided values of the parameters. Non specified parameters are set to
default values.
params_OU( p = 1, variance = diag(1, p, p), selection.strength = diag(1, p, p), optimal.value = rep(0, p), random = TRUE, stationary.root = TRUE, value.root = rep(0, p), exp.root = rep(0, p), var.root = diag(1, p, p), edges = NULL, values = matrix(0, p, length(edges)), relativeTimes = NULL, nbr_of_shifts = length(edges), phylo = NULL, trait_names = NULL, ... )params_OU( p = 1, variance = diag(1, p, p), selection.strength = diag(1, p, p), optimal.value = rep(0, p), random = TRUE, stationary.root = TRUE, value.root = rep(0, p), exp.root = rep(0, p), var.root = diag(1, p, p), edges = NULL, values = matrix(0, p, length(edges)), relativeTimes = NULL, nbr_of_shifts = length(edges), phylo = NULL, trait_names = NULL, ... )
p |
the dimension (number of traits) of the parameters. Default to 1. |
variance |
the variance (rate matrix) of the BM. Default to
|
selection.strength |
the selection strength matrix. Default to
|
optimal.value |
the vector of the optimal values at the root. Default
to |
random |
whether the root of the OU is random (TRUE) or fixed (FALSE). Default to TRUE. |
stationary.root |
whether the root of the OU is stationary (TRUE) or not. Default to TRUE. |
value.root |
if random=FALSE, the root value. Default to 0. |
exp.root |
if random=TRUE, the root expectation. Default to 0. If
stationary.root=TRUE, default to |
var.root |
if random=TRUE, the root variance. Default to
|
edges |
a vector of edges where the shifts occur. Default to NULL (no shift). |
values |
a matrix of shift values, with p lines and as many columns as
the number of shifts. Each column is the p values for one shift. Default to
|
relativeTimes |
(unused) the relative position of the shift on the branch, between 0 (beginning of the branch) and 1 (end of the branch). Default to 0. |
nbr_of_shifts |
the number of shifts to use (randomly drawn). Use only
if |
phylo |
a phylogenetic tree of class |
trait_names |
vector of trait names values. Must be of length p. |
... |
unused. |
an object of class params_process.
params_process creates or extracts a set of parameters of class
params_process.
params_process(x, ...)params_process(x, ...)
x |
an S3 object. |
... |
further arguments to be passed to the specific method. |
An S3 object of class params_process. This is essentially a list containing the following entries:
The model used. One of "BM" (for a full BM model, univariate or multivariate); "OU" (for a full OU model, univariate or multivariate); or "scOU" (for a "scalar OU" model).
Dimension of the trait.
List describing the state of the root, with:
random state (TRUE) or deterministic state (FALSE)
if deterministic, value of the character at the root
if random, expectation of the character at the root
if random, variance of the character at the root (pxp matrix)
List with position and values of the shifts:
vector of the K id of edges where the shifts are
matrix p x K of values of the shifts on the edges (one column = one shift)
vector of dimension K of relative time of the shift from the parent node of edges
Variance-covariance matrix size p x p.
Matrix of selection strength size p x p (OU).
Vector of p optimal values at the root (OU).
params_process.character,
params_process.PhyloEM,
params_BM, params_OU
simul_process.params_process
params_process
params_process creates a coherent object params_process from user
provided values of the parameters.
## S3 method for class 'character' params_process(x, ...)## S3 method for class 'character' params_process(x, ...)
x |
one of "BM" or "OU" |
... |
specified parameters, see functions |
an object of class params_process.
params takes an object of class PhyloEM, and returns the
inferred parameters of the process.
## S3 method for class 'PhyloEM' params_process( x, method.selection = NULL, K = NULL, alpha = NULL, rBM = FALSE, init = FALSE, ... )## S3 method for class 'PhyloEM' params_process( x, method.selection = NULL, K = NULL, alpha = NULL, rBM = FALSE, init = FALSE, ... )
x |
an object of class |
method.selection |
(optional) the method selection to be used. One of "LINselect", "DDSE", "Djump". Default to "LINselect". |
K |
(optional) an integer giving the number of shifts for which to retrieve
the parameters. Default to NULL (automatically selected number of shifts, see
|
alpha |
(optional) a value of alpha for which to retrieve the parameters. Can
be an (un-ambiguous) estimation of the true value. If
specified, then |
rBM |
(optional) if TRUE, and if the process is "scOU", returns the raw
parameters of the BM on the re-scaled tree. Default to FALSE, except if
the selection strength is negative (see doc of |
init |
(optional) if TRUE, gives the parameters from the initialization of
the EM. Default to FALSE. This has no effect if |
... |
unused. |
An object of class params_process.
PhyloEM, imputed_traits.PhyloEM
parsimonyCost is an implementation of the Sankoff algorithm,
when the cost of transition between two state is always one. It is used
in functions parsimonyNumber and enumerate_parsimony
to count or enumerate all the parsimonious solutions given one clustering of the
tips.
parsimonyCost(phylo, clusters = rep(1, length(phylo$tip.label)))parsimonyCost(phylo, clusters = rep(1, length(phylo$tip.label)))
phylo |
a phylogenetic tree, class |
clusters |
the vector of the clusters of the tips. (Default to all the tips in a single group). |
An S3 class "parsimonyCost" containing a
(ntaxa + Nnode) x (nclus) matrix of the total number of shifts needed to
get the clustering, if starting from a node in state k. The cost can be
extract from any subtree with function extract.parsimonyCost.
extract.parsimonyCost, parsimonyNumber,
enumerate_parsimony, partitionsNumber,
equivalent_shifts
tree <- read.tree(text="(((1,1),2),2);") plot(tree); nodelabels() clusters <- c(1, 1, 2, 2) costs <- parsimonyCost(tree, clusters) costs ## Extract the parsimony cost at the root extract(costs) ## Extract the cost for the sub-tree below node 7 extract(costs, 7)tree <- read.tree(text="(((1,1),2),2);") plot(tree); nodelabels() clusters <- c(1, 1, 2, 2) costs <- parsimonyCost(tree, clusters) costs ## Extract the parsimony cost at the root extract(costs) ## Extract the cost for the sub-tree below node 7 extract(costs, 7)
parsimonyNumber aims at finding the number of equivalent allocations of
the shifts on the tree, i.e allocations that are parsimonious and compatible
with a given clustering of the tips.
parsimonyNumber(phylo, clusters = rep(1, length(phylo$tip.label)))parsimonyNumber(phylo, clusters = rep(1, length(phylo$tip.label)))
phylo |
phylogenetic tree, class |
clusters |
the vector of the clusters of the tips. Default to all the tips in one single cluster. |
This function does a recursion up the tree.
The function extract.parsimonyNumber gives the result sought for
any subtree.
The matrix of costs of the states (number of shifts) is also required, it is
computed by function parsimonyCost.
an object of S3 class "parsimonyNumber" with:
a (ntaxa + Nnode) x (nclus) matrix of locally parsimonious solutions starting from a cluster k at a given node
an object of class "parsimonyCost",
result of function parsimonyCost.
extract.parsimonyNumber, parsimonyCost,
enumerate_parsimony, partitionsNumber,
equivalent_shifts
tree <- read.tree(text="(((0,1),2),2);") plot(tree); nodelabels() clusters <- c(0, 1, 2, 2) n_sols <- parsimonyNumber(tree, clusters) n_sols ## Extract the number of parsimonious solutions at the root extract(n_sols) ## Extract the cost of the solutions from the root extract(n_sols, what = "cost") extract(parsimonyCost(tree, clusters)) # same, more efficient ## Extract for the sub-tree below node 7 extract(n_sols, 7) # Result: 2 (the ancestral state is either "0" or "1").tree <- read.tree(text="(((0,1),2),2);") plot(tree); nodelabels() clusters <- c(0, 1, 2, 2) n_sols <- parsimonyNumber(tree, clusters) n_sols ## Extract the number of parsimonious solutions at the root extract(n_sols) ## Extract the cost of the solutions from the root extract(n_sols, what = "cost") extract(parsimonyCost(tree, clusters)) # same, more efficient ## Extract for the sub-tree below node 7 extract(n_sols, 7) # Result: 2 (the ancestral state is either "0" or "1").
partitionsNumber computes the number of different models with a given
number of shifts K. It is also the number of colorings of the tips to the
tree in npart = K + 1 colors.
partitionsNumber(phylo, npart)partitionsNumber(phylo, npart)
phylo |
a phylogenetic tree of class |
npart |
the numbers of partitions (colors) allowed at the tips. This is the number of shifts plus one (npart = K + 1). |
an object of class partitionsNumber. This is made of a matrix
with (Nnode + ntaxa) rows and (2*npart) columns. Each column contains two vectors:
for k=1:npart it contains the number of partitions with k groups compatible
with the tree and the shift process; and for k=(npart+1):2*npart, it contains
the number of "marked" partitions with (k-npart) groups compatible with the
tree and the shift process. The actual number can be extracted with function
extract.partitionsNumber (see examples below).
extract.partitionsNumber, parsimonyNumber,
equivalent_shifts
if (requireNamespace("combinat", quietly = TRUE)) { npart <- 8 # number of colors at the tips allowed tree <- read.tree(text="(A,(A,(A,A,A),A,A));") # a tree with polytomies plot(tree) parts_num <- partitionsNumber(tree, npart) parts_num ## Number of possible colorings of the tips in npart colors extract(parts_num) ## Get all the solutions for colorings with 1 to nparts colors extract(parts_num, npart = 1:npart) ## Number of possible colorings of the tips in npart colors ## For the sub-tree starting at node 17 extract(parts_num, node = 10) ## Number of possible colorings of the tips in npart colors ## with one marked color extract(parts_num, marked = TRUE) }if (requireNamespace("combinat", quietly = TRUE)) { npart <- 8 # number of colors at the tips allowed tree <- read.tree(text="(A,(A,(A,A,A),A,A));") # a tree with polytomies plot(tree) parts_num <- partitionsNumber(tree, npart) parts_num ## Number of possible colorings of the tips in npart colors extract(parts_num) ## Get all the solutions for colorings with 1 to nparts colors extract(parts_num, npart = 1:npart) ## Number of possible colorings of the tips in npart colors ## For the sub-tree starting at node 17 extract(parts_num, node = 10) ## Number of possible colorings of the tips in npart colors ## with one marked color extract(parts_num, marked = TRUE) }
PhyloEM is the main function of the package. It uses maximum likelihood
methods to fit a BM or an OU process for several traits evolving along a
phylogenetic tree, with automatic shift detection on the branches of the tree.
This function can handle missing data.
PhyloEM( phylo, Y_data, process = c("BM", "OU", "scOU", "rBM"), check_postorder = TRUE, independent = FALSE, K_max = max(floor(sqrt(length(phylo$tip.label))), 10), use_previous = FALSE, order = TRUE, method.selection = c("LINselect", "DDSE", "Djump"), C.BM1 = 0.1, C.BM2 = 2.5, C.LINselect = 1.1, method.variance = c("upward_downward", "simple"), method.init = "lasso", method.init.alpha = "estimation", method.init.alpha.estimation = c("regression", "regression.MM", "median"), methods.segmentation = c("lasso", "best_single_move"), alpha_grid = TRUE, nbr_alpha = 10, random.root = TRUE, stationary.root = random.root, alpha = NULL, check.tips.names = TRUE, progress.bar = TRUE, estimates = NULL, save_step = FALSE, rescale_OU = TRUE, parallel_alpha = FALSE, Ncores = 3, K_lag_init = 5, light_result = TRUE, tol_tree = .Machine$double.eps^0.5, allow_negative = FALSE, option_is.ultrametric = 1, trait_correlation_threshold = 0.9, ... )PhyloEM( phylo, Y_data, process = c("BM", "OU", "scOU", "rBM"), check_postorder = TRUE, independent = FALSE, K_max = max(floor(sqrt(length(phylo$tip.label))), 10), use_previous = FALSE, order = TRUE, method.selection = c("LINselect", "DDSE", "Djump"), C.BM1 = 0.1, C.BM2 = 2.5, C.LINselect = 1.1, method.variance = c("upward_downward", "simple"), method.init = "lasso", method.init.alpha = "estimation", method.init.alpha.estimation = c("regression", "regression.MM", "median"), methods.segmentation = c("lasso", "best_single_move"), alpha_grid = TRUE, nbr_alpha = 10, random.root = TRUE, stationary.root = random.root, alpha = NULL, check.tips.names = TRUE, progress.bar = TRUE, estimates = NULL, save_step = FALSE, rescale_OU = TRUE, parallel_alpha = FALSE, Ncores = 3, K_lag_init = 5, light_result = TRUE, tol_tree = .Machine$double.eps^0.5, allow_negative = FALSE, option_is.ultrametric = 1, trait_correlation_threshold = 0.9, ... )
phylo |
A phylogenetic tree of class |
Y_data |
Matrix of data at the tips, size p x ntaxa. Each line is a trait, and each column is a tip. The column names are checked against the tip names of the tree. |
process |
The model used for the fit. One of "BM" (for a full BM model, univariate or multivariate); "OU" (for an OU with independent traits, univariate or multivariate); or "scOU" (for a "scalar OU" model, see details). |
check_postorder |
Re-order the tree in post-order. If the Upward-Downward algorithm is used, the tree need to be in post-order. Default to TRUE if the upward-downward is used, otherwise automatically set to FALSE. |
independent |
Are the trait assumed to be independent from one another? Default to FALSE. OU in a multivariate setting only works if TRUE. |
K_max |
The maximum number of shifts to be considered. Default to
|
use_previous |
Should the initialization for K+1 shifts use the estimation for $K$ shifts already obtained? Default to FALSE. |
order |
Should the estimations be done for K increasing (TRUE) or K decreasing (FALSE)? If use_previous=FALSE, this has no influence, except if one initialization fails. Default to TRUE. |
method.selection |
Method selection to be used. Several ones can be used at the same time. One of "LINselect" for the Baraud Giraud Huet LINselect method; "DDSE" for the Slope Heuristic or "Djump" for the Jump Heuristic, last two based the Birgé Massart method. |
C.BM1 |
Multiplying constant to be used for the BigeMassart1 method. Need to be positive. Default to 0.1. |
C.BM2 |
Multiplying constant to be used for the BigeMassart2 method. Default to 2.5. |
C.LINselect |
Multiplying constant to be used for the LINselect method. Need to be greater than 1. Default to 1.1. |
method.variance |
Algorithm to be used for the moments computations at the E step. One of "simple" for the naive method; of "upward_downward" for the Upward Downward method (usually faster). Default to "upward_downward". |
method.init |
The initialization method. One of "lasso" for the LASSO base initialization method; or "default" for user-specified initialization values. Default to "lasso". |
method.init.alpha |
For OU model, initialization method for the selection
strength alpha. One of "estimation" for a cherry-based initialization, using
|
method.init.alpha.estimation |
If method.init.alpha="estimation",
choice of the estimation(s) methods to be used. Choices among "regression",
(method="M" is passed to |
methods.segmentation |
For OU, method(s) used at the M step to find new candidate shifts positions. Choices among "lasso" for a LASSO-based algorithm; and "best_single_move" for a one-move at a time based heuristic. Default to both of them. Using only "lasso" might speed up the function a lot. |
alpha_grid |
whether to use a grid for alpha values. Default to TRUE. This
is the only available method for scOU. This method is not available for OU with
multivariate traits. OU with univariate traits can take both TRUE or FALSE. If
TRUE, a grid based on the branch length of the tree is automatically computed,
using function |
nbr_alpha |
If |
random.root |
whether the root is assumed to be random (TRUE) of fixed (FALSE). Default to TRUE |
stationary.root |
whether the root is assumed to be in the stationary state. Default to TRUE. |
alpha |
If the estimation is done with a fixed alpha (either known, or on a grid), the possible value for alpha. Default to NULL. |
check.tips.names |
whether to check the tips names of the tree against the column names of the data. Default to TRUE. |
progress.bar |
whether to display a progress bar of the computations. Default to TRUE. |
estimates |
The result of a previous run of this same function. This function can be re-run for other model election method. Default to NULL. |
save_step |
If alpha_grid=TRUE, whether to save the intermediate results for each value of alpha (in a temporary file). Useful for long computations. Default to FALSE. |
rescale_OU |
For the Univariate OU, should the tree be re-scaled to use a BM ? This can speed up the computations a lot. However, it can make it harder for the EM to explore the space of parameters, and hence lead to a sub-optimal solution. Default to TRUE. |
parallel_alpha |
If alpha_grid=TRUE, whether to run the estimations with different values of alpha on separate cores. Default to FALSE. If TRUE, the log is written as a temporary file. |
Ncores |
If parallel_alpha=TRUE, number of cores to be used. |
K_lag_init |
Number of extra shifts to be considered at the initialization step. Increases the accuracy, but can make computations quite slow of taken too high. Default to 5. |
light_result |
if TRUE (the default), the object returned is made light, without easily computable quantities. If FALSE, the object can be very heavy, but its subsequent manipulations can be faster (especially for plotting). |
tol_tree |
tolerance to consider a branch length significantly greater than zero, or
two lineages lengths to be different, when checking for ultrametry.
(Default to .Machine$double.eps^0.5). See |
allow_negative |
whether to allow negative values for alpha (Early Burst). See details. Default to FALSE. |
option_is.ultrametric |
option for |
trait_correlation_threshold |
the trait correlation threshold to stop the analysis. Default to 0.9. |
... |
Further arguments to be passed to |
Several models can be used:
BM with fixed root, univariate or multivariate.
OU with fixed or stationary root, univariate or multivariate.
For the OU in the multivariate setting, two assumptions can be made:
Independent traits. This amounts to diagonal rate and selection matrices.
"Scalar OU" (scOU): the rate matrix can be full, but the selection strength matrix is assumed to be scalar, i.e. all the traits are supposed to go to their optimum values with the same speed.
Note that the "scalar OU" model can also be seen as a re-scaling of the tree. The selection strength parameter alpha can then be interpreted as a measure of the "phylogenetic signal":
If alpha is close to 0, then the process is similar to a BM on the original tree, and the signal is strong.
If alpha is large, then the re-scaled tree is similar to a star-tree, and the signal is weak.
When there are no shifts, and the root is taken to be constant, this model is actually equivalent to an AC model (Uyeda et al. 2015). With this interpretation in mind, one might want to explore negative values of alpha, in order to fit a DC (or Early Burst) model. With no shift and a fixed root, the same proof shows that the scOU with alpha negative is equivalent to the DC model. There are two strong caveats in doing that.
The interpretation of the OU as modeling the dynamic of a trait undergoing stabilizing selection is lost. In this case, the scOU can only be seen as a re-scaling of the tree, similar to Pagel's delta.
The values of the "optimal values", and of the shifts on them, cannot
be interpreted as such (the process is actually going away from this values,
instead of being attracted). When looking at these values, one should only use
the un-normalized values happening of the underlying BM. You can extract those
using the params_process function with rBM = TRUE.
An object of class PhyloEM. Relevant quantities can be extracted from it
using helper functions params_process.PhyloEM,
imputed_traits.PhyloEM
plot.PhyloEM, params_process.PhyloEM,
imputed_traits.PhyloEM
## Not run: ## Load Data data(monkeys) ## Run method # Note: use more alpha values for better results. res <- PhyloEM(Y_data = monkeys$dat, ## data phylo = monkeys$phy, ## phylogeny process = "scOU", ## scalar OU random.root = TRUE, ## root is stationary stationary.root = TRUE, K_max = 10, ## maximal number of shifts nbr_alpha = 4, ## number of alpha values parallel_alpha = TRUE, ## parallelize on alpha values Ncores = 2) ## Plot selected solution (LINselect) plot(res) # three shifts ## Plot selected solution (DDSE) plot(res, method.selection = "DDSE") # no shift ## Extract and solution with 5 shifts params_5 <- params_process(res, K = 5) plot(res, params = params_5) ## Show all equivalent solutions eq_sol <- equivalent_shifts(monkeys$phy, params_5) plot(eq_sol) ## End(Not run)## Not run: ## Load Data data(monkeys) ## Run method # Note: use more alpha values for better results. res <- PhyloEM(Y_data = monkeys$dat, ## data phylo = monkeys$phy, ## phylogeny process = "scOU", ## scalar OU random.root = TRUE, ## root is stationary stationary.root = TRUE, K_max = 10, ## maximal number of shifts nbr_alpha = 4, ## number of alpha values parallel_alpha = TRUE, ## parallelize on alpha values Ncores = 2) ## Plot selected solution (LINselect) plot(res) # three shifts ## Plot selected solution (DDSE) plot(res, method.selection = "DDSE") # no shift ## Extract and solution with 5 shifts params_5 <- params_process(res, K = 5) plot(res, params = params_5) ## Show all equivalent solutions eq_sol <- equivalent_shifts(monkeys$phy, params_5) plot(eq_sol) ## End(Not run)
This function takes an object of class PhyloEM, result of function
PhyloEM, and plots a model selection criterion.
plot_criterion( res, method.selection = NULL, add = FALSE, select.col = "red", ... )plot_criterion( res, method.selection = NULL, add = FALSE, select.col = "red", ... )
res |
an object of class |
method.selection |
select the parameters to plot. One of "LINselect", "DDSE",
"Djump" or "likelihood" (for un-penalized likelihood). Default to "LINselect". See
|
add |
boolean: should the points be added to a current plot (default to FALSE). |
select.col |
the color of the point selected by the criterion. Default to "red". |
... |
further argument to be passed to base |
params_process.PhyloEM, plot.PhyloEM, get_criterion
plot.enumerate_parsimony plots a representation of all the equivalent
solutions.
## S3 method for class 'enumerate_parsimony' plot(x, numbering = FALSE, nbr_col = 3, ...)## S3 method for class 'enumerate_parsimony' plot(x, numbering = FALSE, nbr_col = 3, ...)
x |
an object of class |
numbering |
whether to number the solutions. Default to FALSE. |
nbr_col |
the number of columns on which to display the plot. Default to 3. |
... |
further arguments to be passed to |
This function uses functions plot.phylo and
nodelabels for the actual plotting of the trees.
A plot of the equivalent shifts allocations.
plot.phylo, enumerate_parsimony,
plot.equivalent_shifts, nodelabels
plot.equivalent_shifts plots a representation of all the equivalent
shifts allocations, with a representation of the shifts and their values,
and a coloration of the branches in term of regimes.
## S3 method for class 'equivalent_shifts' plot( x, trait = 1, show_shifts_values = TRUE, numbering = FALSE, colors_tips = NULL, nbr_col = 3, gray_scale = FALSE, edge.width = 2, shifts_cex = 1.2, ... )## S3 method for class 'equivalent_shifts' plot( x, trait = 1, show_shifts_values = TRUE, numbering = FALSE, colors_tips = NULL, nbr_col = 3, gray_scale = FALSE, edge.width = 2, shifts_cex = 1.2, ... )
x |
an object of class |
trait |
(integer) the trait to be plotted, if multivariate. Default to 1. |
show_shifts_values |
whether to show the equivalent shifts values or not. Default to FALSE. |
numbering |
whether to number the solutions. Default to FALSE. |
colors_tips |
user-provided colors for the tips of the tree. A vector vector with as many colors as there are tips. Will be automatically computed if not provided. |
nbr_col |
the number of columns on which to display the plot. Default to 3. |
gray_scale |
if TRUE, the colors are replaced by a gray scale. Default to FALSE. |
edge.width |
width of the edge. Default to 1. |
shifts_cex |
if |
... |
further arguments to be passed to |
This function uses function plot.phylo for the actual
plotting of the trees.
A plot of the equivalent shifts allocations.
simul_process
This function takes an object of class params_process, and plots them along
with some data at the tips of the tree.
## S3 method for class 'params_process' plot( x, phylo, data = NULL, traits, automatic_colors = TRUE, color_characters = "black", color_edges = "black", plot_ancestral_states = FALSE, ancestral_states = NULL, imposed_scale, ancestral_cex = 2, ancestral_pch = 19, value_in_box = FALSE, ancestral_as_shift = FALSE, shifts_cex = 0.6, shifts_bg = "chocolate4", root_bg = "chocolate4", shifts_adj = 0, root_adj = 1, color_shifts_regimes = FALSE, regime_boxes = FALSE, alpha_border = 70, show.tip.label = FALSE, label_cex = 0.5, label_offset = 0, axis_cex = 0.7, edge.width = 1, margin_plot = NULL, gray_scale = FALSE, ... )## S3 method for class 'params_process' plot( x, phylo, data = NULL, traits, automatic_colors = TRUE, color_characters = "black", color_edges = "black", plot_ancestral_states = FALSE, ancestral_states = NULL, imposed_scale, ancestral_cex = 2, ancestral_pch = 19, value_in_box = FALSE, ancestral_as_shift = FALSE, shifts_cex = 0.6, shifts_bg = "chocolate4", root_bg = "chocolate4", shifts_adj = 0, root_adj = 1, color_shifts_regimes = FALSE, regime_boxes = FALSE, alpha_border = 70, show.tip.label = FALSE, label_cex = 0.5, label_offset = 0, axis_cex = 0.7, edge.width = 1, margin_plot = NULL, gray_scale = FALSE, ... )
x |
an object of class |
phylo |
a phylogenetic tree. |
data |
a matrix of data at the tips of the tree. Must have p rows and
ntaxa columns. If these are simulated, use the |
traits |
a vector of integers giving the numbers of the trait to be plotted. Default to 1:p (all the traits). |
automatic_colors |
whether to color the edges automatically according to
their regimes. Default to TRUE. If FALSE, colors can be manually specified through
arguments |
color_characters |
if |
color_edges |
if |
plot_ancestral_states |
whether to plot the ancestral traits inferred at the internal nodes of the tree. Only available if only one trait is plotted. Default to FALSE. |
ancestral_states |
if |
imposed_scale |
if |
ancestral_cex |
if |
ancestral_pch |
if |
value_in_box |
whether to plot the value of the shift in a box on the edges. Only available when only one trait is plotted. Can be difficult to read on big trees. The size of the text in the boxes is controlled by parameter. Default to FALSE. |
ancestral_as_shift |
whether to represent the ancestral value at the root
as an ancestral shift on the root edge. Default to FALSE.
|
shifts_cex |
if |
shifts_bg |
if |
root_bg |
if |
shifts_adj |
the adj parameter for the shifts position on the edges. Default to 0 (beginning of the edge). |
root_adj |
if |
color_shifts_regimes |
whether to color each shift according to its regime (default to the same color of the edge it's on). Default to FALSE. |
regime_boxes |
whether to draw a box showing all the tips below a given.
The transparency of the border of the box is controlled by parameter
|
alpha_border |
if |
show.tip.label |
whether to show the tip labels. Default to FALSE. |
label_cex |
if |
label_offset |
if |
axis_cex |
cex for the label values of the plot. Default to 0.7. |
edge.width |
width of the edge. Default to 1. |
margin_plot |
vector giving the margin to around the plot.
Default to |
gray_scale |
if TRUE, the colors are replaced by a gray scale. Default to FALSE. |
... |
further arguments to be passed to |
simul_process, plot.PhyloEM,
params_BM, params_OU
PhyloEM
This function takes an object of class PhyloEM, result of function
PhyloEM, and plots the result of the inference.
## S3 method for class 'PhyloEM' plot( x, traits = 1:(x$p), params = NULL, method.selection = NULL, automatic_colors = TRUE, color_characters = "black", color_edges = "black", plot_ancestral_states = FALSE, name_trait = "Trait Value", imposed_scale, ancestral_cex = 2, ancestral_pch = 19, value_in_box = FALSE, ancestral_as_shift = FALSE, shifts_cex = 0.6, shifts_bg = "chocolate4", root_bg = "chocolate4", shifts_adj = 0, root_adj = 1, color_shifts_regimes = FALSE, regime_boxes = FALSE, alpha_border = 70, show.tip.label = FALSE, label_cex = 0.5, label_font = 1, label_offset = 0, axis_cex = 0.7, axis_las = 0, show_axis_traits = TRUE, edge.width = 1, margin_plot = NULL, gray_scale = FALSE, root.edge = TRUE, ... )## S3 method for class 'PhyloEM' plot( x, traits = 1:(x$p), params = NULL, method.selection = NULL, automatic_colors = TRUE, color_characters = "black", color_edges = "black", plot_ancestral_states = FALSE, name_trait = "Trait Value", imposed_scale, ancestral_cex = 2, ancestral_pch = 19, value_in_box = FALSE, ancestral_as_shift = FALSE, shifts_cex = 0.6, shifts_bg = "chocolate4", root_bg = "chocolate4", shifts_adj = 0, root_adj = 1, color_shifts_regimes = FALSE, regime_boxes = FALSE, alpha_border = 70, show.tip.label = FALSE, label_cex = 0.5, label_font = 1, label_offset = 0, axis_cex = 0.7, axis_las = 0, show_axis_traits = TRUE, edge.width = 1, margin_plot = NULL, gray_scale = FALSE, root.edge = TRUE, ... )
x |
an object of class |
traits |
a vector of integers giving the numbers of the trait to be plotted. Default to 1:p (all the traits). |
params |
(optional) some user-specified parameters.
Must be of class |
method.selection |
select the parameters to plot. One of "LINselect", "DDSE",
"Djump". Default to "LINselect". See
|
automatic_colors |
whether to color the edges automatically according to
their regimes. Default to TRUE. If FALSE, colors can be manually specified through
arguments |
color_characters |
if |
color_edges |
if |
plot_ancestral_states |
whether to plot the ancestral traits inferred at the internal nodes of the tree. Only available if only one trait is plotted. Default to FALSE. |
name_trait |
name of the trait scale bar for the ancestral states plotting. Default to "Trait Value". |
imposed_scale |
if |
ancestral_cex |
if |
ancestral_pch |
if |
value_in_box |
whether to plot the value of the shift in a box on the edges. Only available when only one trait is plotted. Can be difficult to read on big trees. The size of the text in the boxes is controlled by parameter. Default to FALSE. |
ancestral_as_shift |
whether to represent the ancestral value at the root
as an ancestral shift on the root edge. Default to FALSE.
|
shifts_cex |
if |
shifts_bg |
if |
root_bg |
if |
shifts_adj |
the adj parameter for the shifts position on the edges. Default to 0 (beginning of the edge). |
root_adj |
if |
color_shifts_regimes |
whether to color each shift according to its regime (default to the same color of the edge it's on). Default to FALSE. |
regime_boxes |
whether to draw a box showing all the tips below a given.
The transparency of the border of the box is controlled by parameter
|
alpha_border |
if |
show.tip.label |
whether to show the tip labels. Default to FALSE. |
label_cex |
if |
label_font |
if |
label_offset |
if |
axis_cex |
cex for the label values of the plot. Default to 0.7. |
axis_las |
las for the label values of the plot. Default to 0 (see par). |
show_axis_traits |
control whether the trait values axis is plotted (default to TRUE). |
edge.width |
width of the edge. Default to 1. |
margin_plot |
vector giving the margin to around the plot.
Default to |
gray_scale |
if TRUE, the colors are replaced by a gray scale. Default to FALSE. |
root.edge |
a logical indicating whether to draw the root edge (defaults to TRUE) |
... |
further arguments to be passed to |
params_process.PhyloEM, imputed_traits.PhyloEM
residuals computes the residuals of some parameters.
## S3 method for class 'PhyloEM' residuals(object, ...)## S3 method for class 'PhyloEM' residuals(object, ...)
object |
an object of class |
... |
for a |
The log likelihood of the data with the provided parameters on the tree.
shifts_to_simmap takes a vector of edges where the shifts occur, and return a
simmap formatted tree, mapped with corresponding regimes.
shifts_to_simmap(tree, shifts_edges)shifts_to_simmap(tree, shifts_edges)
tree |
input tree in |
shifts_edges |
shifts positions on the edges |
Ancestral state is always 0, and other states are consecutive integers.
tree a simmap object
shifts.list_to_matrix takes the list description of the shifts
to give the matrix representation of the shifts : the b th element of
the lth line has the value of the shift on character l occurring on that branch b
shifts.list_to_matrix(phy, shifts, p = nrow(shifts$values))shifts.list_to_matrix(phy, shifts, p = nrow(shifts$values))
phy |
Input tree. |
shifts |
list description of the shifts : shifts$edges, shifts$values. |
p |
number of traits (optional, needed when shifts = NULL). |
Matrix p x Nedge of length nbranch.
shifts.matrix_to_list takes the vectorial description of the shifts
to create the list description of the shifts.
shifts.matrix_to_list(delta)shifts.matrix_to_list(delta)
delta |
matrix description of the shift. |
List describing shifts.
simulate simulate a stochastic process on a tree.
simul_process(x, ...) ## S3 method for class 'params_process' simul_process( x, phylo, simulate_random = TRUE, checks = TRUE, U_tree = NULL, times_shared = NULL, ... ) ## S3 method for class 'PhyloEM' simul_process( x, simulate_random = TRUE, checks = TRUE, U_tree = NULL, times_shared = NULL, ... )simul_process(x, ...) ## S3 method for class 'params_process' simul_process( x, phylo, simulate_random = TRUE, checks = TRUE, U_tree = NULL, times_shared = NULL, ... ) ## S3 method for class 'PhyloEM' simul_process( x, simulate_random = TRUE, checks = TRUE, U_tree = NULL, times_shared = NULL, ... )
x |
an object of class |
... |
for a |
phylo |
a phylogenetic tree, class |
simulate_random |
set to FALSE if only the expected values are needed (and not the random sample). Default to TRUE. |
checks |
whether to check the entry parameters for consistency. Default to TRUE. |
U_tree |
optional, full incidence matrix of the tree, result of function
|
times_shared |
optional, times of shared ancestry of all nodes and tips,
result of function |
An S3 object of class simul_process. This contains:
an array with dimensions p x Nnode x 2 (BM) or p x Nnode x 3 (OU). For each trait t, 1 <= t <= p, sim_traits[t, , ] has tree columns, containing respectively the simulated state, expected value and optimal value for all the nodes.
the phylogenetic tree used for the simulations (class phylo).
the parameters used for the simulations
(class params_proces).
simul_process(params_process): params_process object
simul_process(PhyloEM): PhyloEM object
params_process, PhyloEM, extract.simul_process
Re-scale the branch length of the tree so that a BM running on the new tree produces the same observations at the tips than an OU with parameter alpha.
transform_branch_length(phylo, alp)transform_branch_length(phylo, alp)
phylo |
A phylogenetic tree of class |
alp |
Value of the selection strength. |
phylo The same phylogenetic tree, with transformed branch lengths.