| Type: | Package |
| Title: | Diversity-Dependent Diversification |
| Version: | 5.2.2 |
| Date: | 2023-07-10 |
| Depends: | R (≥ 3.5.0) |
| Imports: | deSolve, ape, phytools, subplex, DEoptim, Matrix, expm, SparseM, Rcpp (≥ 1.0.10) |
| LinkingTo: | Rcpp (≥ 1.0.10), RcppEigen, BH (≥ 1.81.0-1) |
| Suggests: | testthat (≥ 3.0.0), testit, igraph |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Description: | Implements maximum likelihood and bootstrap methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent, under various models including various types of key innovations. See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, <doi:10.1098/rspb.2011.1439>, Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, <doi:10.1086/667574>, Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, <doi:10.1111/2041-210X.12565> and Laudanno et al. 2021. Syst. Biol. 70: 389–407, <doi:10.1093/sysbio/syaa048>. Also contains functions to simulate the diversity-dependent process. |
| RoxygenNote: | 7.2.3 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | yes |
| Packaged: | 2023-07-10 11:00:23 UTC; rampa |
| Author: | Rampal S. Etienne 
[aut, cre],
Bart Haegeman
[aut],
Hanno Hildenbrandt
[ctb],
Giovanni Laudanno
[ctb] |
| Maintainer: | Rampal S. Etienne <r.s.etienne@rug.nl> |
| Repository: | CRAN |
| Date/Publication: | 2023-07-10 23:40:47 UTC |
DDD: Diversity-Dependent Diversification
Description
Implements maximum likelihood and bootstrap methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent, under various models including various types of key innovations. See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, doi:10.1098/rspb.2011.1439, Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, doi:10.1086/667574, Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099, doi:10.1111/2041-210X.12565 and Laudanno et al. 2021. Syst. Biol. 70: 389–407, doi:10.1093/sysbio/syaa048. Also contains functions to simulate the diversity-dependent process.
Author(s)
Maintainer: Rampal S. Etienne r.s.etienne@rug.nl (ORCID)
Authors:
Bart Haegeman bart.haegeman@sete.cnrs.fr (ORCID)
Other contributors:
Hanno Hildenbrandt h.hildenbrandt@rug.nl (ORCID) [contributor]
Giovanni Laudanno glaudanno@gmail.com (ORCID) [contributor]
Function to convert a table with speciation and extinction events to a set of branching times
Description
Converting a table with speciation and extinction events to a set of branching times
Usage
L2brts(L, dropextinct = T)
Arguments
L |
Matrix of events as produced by dd_sim: |
dropextinct |
Sets whether the phylogeny should drop species that are extinct at the present |
Value
brts |
A set of branching times |
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
sim = dd_sim(c(0.2,0.1,20),10)
phy = L2brts(sim$L)
plot(phy)
Function to convert a table with speciation and extinction events to a phylogeny
Description
Converting a table with speciation and extinction events to a phylogeny
Usage
L2phylo(L, dropextinct = T)
Arguments
L |
Matrix of events as produced by dd_sim: |
dropextinct |
Sets whether the phylogeny should drop species that are extinct at the present |
Value
phy |
A phylogeny of the phylo type |
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
sim = dd_sim(c(0.2,0.1,20),10)
phy = L2phylo(sim$L)
plot(phy)
Maximization of the loglikelihood under the diversity-independent, possibly time-dependent diversification model
Description
This function computes the maximum likelihood estimates of the parameters of a diversity-independent diversification model for a given set of phylogenetic branching times. It also outputs the corresponding loglikelihood that can be used in model comparisons.
Usage
bd_ML(
brts,
initparsopt = c(0.1, 0.05 * (tdmodel <= 1) + 10 * (length(brts) + missnumspec) *
(tdmodel > 1)),
idparsopt = c(1, 2 + (tdmodel > 1)),
idparsfix = (1:4)[-idparsopt],
parsfix = rep(0, 4)[idparsfix],
missnumspec = 0,
tdmodel = 0,
cond = 1,
btorph = 1,
soc = 2,
tol = c(0.001, 1e-04, 1e-06),
maxiter = 1000 * round((1.25)^length(idparsopt)),
changeloglikifnoconv = FALSE,
optimmethod = "subplex",
num_cycles = 1,
methode = "odeint::runge_kutta_cash_karp54",
verbose = FALSE
)
Arguments
brts |
A set of branching times of a phylogeny, all positive |
initparsopt |
The initial values of the parameters that must be optimized |
idparsopt |
The ids of the parameters that must be optimized, e.g. 1:3
for intrinsic speciation rate, extinction rate and carrying capacity. The
ids are defined as follows: |
idparsfix |
The ids of the parameters that should not be optimized, e.g. c(1,3) if lambda0 and lambda1 should not be optimized, but only mu0 and mu1. In that case idparsopt must be c(2,4). The default is to fix all parameters not specified in idparsopt. |
parsfix |
The values of the parameters that should not be optimized |
missnumspec |
The number of species that are in the clade but missing in the phylogeny |
tdmodel |
Sets the model of time-dependence: |
cond |
Conditioning: |
btorph |
Sets whether the likelihood is for the branching times (0) or the phylogeny (1) |
soc |
Sets whether stem or crown age should be used (1 or 2) |
tol |
Sets the tolerances in the optimization. Consists of: |
maxiter |
Sets the maximum number of iterations in the optimization |
changeloglikifnoconv |
if TRUE the loglik will be set to -Inf if ML does not converge |
optimmethod |
Method used in optimization of the likelihood. Current default is 'subplex'. Alternative is 'simplex' (default of previous versions) |
num_cycles |
the number of cycles of opimization. If set at Inf, it will do as many cycles as needed to meet the tolerance set for the target function. |
methode |
The method used to solve the master equation under tdmodel = 4, default is 'odeint::runge_kutta_cash_karp54'. |
verbose |
Show the parameters and loglikelihood for every call to the loglik function |
Details
The output is a dataframe containing estimated parameters and maximum loglikelihood. The computed loglikelihood contains the factor q! m! / (q + m)! where q is the number of species in the phylogeny and m is the number of missing species, as explained in the supplementary material to Etienne et al. 2012.
Value
A dataframe with the following elements:
lambda0 |
gives the maximum likelihood estimate of lambda0 |
mu0 |
gives the maximum likelihood estimate of mu0 |
lambda1 |
gives the maximum likelihood estimate of lambda1 |
mu1 |
gives the maximum likelihood estimate of mu1 |
loglik |
gives the maximum loglikelihood |
df |
gives the number of estimated parameters, i.e. degrees of feedom |
conv |
gives a message on convergence of optimization; conv = 0 means convergence |
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
Examples
cat("Estimating parameters for a set of branching times brts with the default settings:")
brts = 1:20
bd_ML(brts = brts, cond = 1)
Loglikelihood for diversity-independent diversification model
Description
This function computes loglikelihood of a diversity-independent diversification model for a given set of branching times and parameter values.
Usage
bd_loglik(
pars1,
pars2,
brts,
missnumspec,
methode = "odeint::runge_kutta_cash_karp54"
)
Arguments
pars1 |
Vector of parameters:
|
pars2 |
Vector of model settings:
|
brts |
A set of branching times of a phylogeny, all positive |
missnumspec |
The number of species that are in the clade but missing in the phylogeny |
methode |
The method used to solve the master equation, default is 'odeint::runge_kutta_cash_karp54'. |
Value
The loglikelihood
Author(s)
Rampal S. Etienne, Bart Haegeman & Cesar Martinez
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
Examples
bd_loglik(pars1 = c(0.5,0.1), pars2 = c(0,1,1,0,2), brts = 1:10,
missnumspec = 0)
Function to convert a set of branching times into a phylogeny with random topology This code is taken from the package TESS by Sebastian Hoehna, where the function is called tess.create.phylo
Description
Converting a set of branching times to a phylogeny
Usage
brts2phylo(times, root = FALSE, tip.label = NULL)
Arguments
times |
Set of branching times |
root |
When root is FALSE, the largest branching time will be assumed to be the crown age. When root is TRUE, it will be the stem age. |
tip.label |
Tip labels. If set to NULL, the labels will be t1, t2, etc. |
Value
phy |
A phylogeny of the phylo type |
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Function to do convolution of two vectors
Description
Convolution of two vectors
Usage
conv(x, y)
Arguments
x |
first vector |
y |
second vector |
Value
vector that is the convolution of x and y
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
conv(1:10,1:10)
Maximization of the loglikelihood under a diversity-dependent diversification model with decoupling of a subclade's diversication dynamics from the main clade's dynamics
Description
This function computes the maximum likelihood estimates of the parameters of a diversity-dependent diversification model with decoupling of the diversification dynamics of a subclade from the dynamics of the main clade for a given set of phylogenetic branching times of main clade and subclade and the time of splitting of the lineage that will form the subclade. It also outputs the corresponding loglikelihood that can be used in model comparisons.
Usage
dd_KI_ML(
brtsM,
brtsS,
tsplit,
initparsopt = c(0.5, 0.1, 2 * (1 + length(brtsM) + missnumspec[1]), 2 * (1 +
length(brtsS) + missnumspec[length(missnumspec)]), (tsplit + max(brtsS))/2),
parsfix = NULL,
idparsopt = c(1:3, 6:7),
idparsfix = NULL,
idparsnoshift = (1:7)[c(-idparsopt, (-1)^(length(idparsfix) != 0) * idparsfix)],
res = 10 * (1 + length(c(brtsM, brtsS)) + sum(missnumspec)),
ddmodel = 1,
missnumspec = 0,
cond = 1,
soc = 2,
tol = c(0.001, 1e-04, 1e-06),
maxiter = 1000 * round((1.25)^length(idparsopt)),
changeloglikifnoconv = FALSE,
optimmethod = "subplex",
num_cycles = 1,
methode = "analytical",
correction = TRUE,
verbose = FALSE
)
Arguments
brtsM |
A set of branching times of the main clade in a phylogeny, all positive |
brtsS |
A set of branching times of the subclade in a phylogeny, all positive |
tsplit |
The branching time at which the lineage forming the subclade branches off, positive |
initparsopt |
The initial values of the parameters that must be optimized |
parsfix |
The values of the parameters that should not be optimized |
idparsopt |
The ids of the parameters that must be optimized, e.g. 1:7
for all parameters. The ids are defined as follows: |
idparsfix |
The ids of the parameters that should not be optimized, e.g. c(1,3,4,6) if lambda and K should not be optimized, but only mu. In that case idparsopt must be c(2,5,7). The default is to fix all parameters not specified in idparsopt. |
idparsnoshift |
The ids of the parameters that should not shift; This can only apply to ids 4, 5 and 6, e.g. idparsnoshift = c(4,5) means that lambda and mu have the same values before and after tshift |
res |
sets the maximum number of species for which a probability must be computed, must be larger than 1 + max(length(brtsM),length(brtsS)) |
ddmodel |
sets the model of diversity-dependence: |
missnumspec |
The number of species that are in the clade but missing in the phylogeny. One can specify the sum of the missing species in main clade and subclade or a vector c(missnumspec_M,missnumspec_S) with missing species in main clade and subclade respectively. |
cond |
Conditioning: |
soc |
Sets whether stem or crown age should be used (1 or 2); stem age only works when cond = 0 |
tol |
Sets the tolerances in the optimization. Consists of: |
maxiter |
Sets the maximum number of iterations in the optimization |
changeloglikifnoconv |
if TRUE the loglik will be set to -Inf if ML does not converge |
optimmethod |
Method used in optimization of the likelihood. Current default is 'subplex'. Alternative is 'simplex' (default of previous versions) |
num_cycles |
the number of cycles of opimization. If set at Inf, it will do as many cycles as needed to meet the tolerance set for the target function. |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
correction |
Sets whether the correction should be applied (TRUE) or not (FALSE) |
verbose |
Show the parameters and loglikelihood for every call to the loglik function |
Details
The output is a dataframe containing estimated parameters and maximum loglikelihood. The computed loglikelihood contains the factor q! m!/(q + m)! where q is the number of species in the phylogeny and m is the number of missing species, as explained in the supplementary material to Etienne et al. 2012.
Value
lambda_M |
gives the maximum likelihood estimate of lambda of the main clade |
mu_M |
gives the maximum likelihood estimate of mu of the main clade |
K_M |
gives the maximum likelihood estimate of K of the main clade |
lambda_2 |
gives the maximum likelihood estimate of lambda of the subclade |
mu_S |
gives the maximum likelihood estimate of mu of the subclade |
K_S |
gives the maximum likelihood estimate of K of the subclade |
t_d |
gives the time of the decoupling event |
loglik |
gives the maximum loglikelihood |
df |
gives the number of estimated parameters, i.e. degrees of feedom |
conv |
gives a message on convergence of optimization; conv = 0 means convergence |
Note
The optimization may get trapped in local optima. Try different starting values to search for the global optimum.
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_KI_loglik, dd_ML,
dd_SR_ML,
Examples
cat("This will estimate parameters for two sets of branching times brtsM, brtsS\n")
cat("without conditioning.\n")
cat("The tolerance of the optimization is set high so runtime is fast in this example.\n")
cat("In real applications, use the default or more stringent settins for tol.\n")
brtsM = 4:10
brtsS = seq(0.1,3.5,0.7)
tsplit = 5
dd_KI_ML(brtsM = brtsM, brtsS = brtsS, tsplit = tsplit, idparsopt = c(1:3,6,7),
initparsopt = c(0.885, 2e-14, 6.999, 6.848, 4.001), idparsfix = NULL,
parsfix = NULL,idparsnoshift = c(4,5), cond = 0, tol = c(3E-1,3E-1,3E-1),
optimmethod = 'simplex')
Loglikelihood for diversity-dependent diversification models with decoupling of a subclade from a main clade at time t = t_d
Description
This function computes loglikelihood of a diversity-dependent diversification model for a given set of branching times and parameter values where the diversity-dependent dynamics of a subclade decouple from the dynamics of the main clade at time t_d, potentially accompanied by a shift in parameters.
Usage
dd_KI_loglik(
pars1,
pars2,
brtsM,
brtsS,
missnumspec,
methode = "odeint::runge_kutta_cash_karp54"
)
Arguments
pars1 |
Vector of parameters: |
pars2 |
Vector of model settings: |
brtsM |
A set of branching times of the main clade in the phylogeny, all positive |
brtsS |
A set of branching times of the subclade in the phylogeny, all positive |
missnumspec |
The number of species that are in the clade but missing in the phylogeny. One can specify the sum of the missing species in main clade and subclade or a vector c(missnumspec_M,missnumspec_S) with missing species in main clade and subclade respectively. |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
Value
The loglikelihood
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_KI_ML, dd_loglik
dd_SR_loglik
Examples
pars1 = c(0.25,0.12,25.51,1.0,0.16,8.61,9.8)
pars2 = c(200,1,0,18.8,1,2)
missnumspec = 0
brtsM = c(25.2,24.6,24.0,22.5,21.7,20.4,19.9,19.7,18.8,17.1,15.8,11.8,9.7,8.9,5.7,5.2)
brtsS = c(9.6,8.6,7.4,4.9,2.5)
dd_KI_loglik(pars1,pars2,brtsM,brtsS,missnumspec)
Function to simulate a key innovation in macro-evolution with the innovative clade decoupling from the diversity-dependent diversification dynamics of the main clade
Description
Simulating a diversity-dependent diversification process where at a given time a new clade emerges with different inherent speciation rate and extinction rate and clade-level carrying capacity and with decoupled dynamics
Usage
dd_KI_sim(pars, age, ddmodel = 1)
Arguments
pars |
Vector of parameters: |
age |
Sets the crown age for the simulation |
ddmodel |
Sets the model of diversity-dependence: |
Value
out |
A list with the following elements: The first element
is the tree of extant species in phylo format |
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
dd_KI_sim(c(0.2,0.1,20,0.1,0.05,30,4),10)
Bootstrap likelihood ratio test of diversity-dependent diversification model
Description
This function computes the maximum likelihood and the associated estimates of the parameters of a diversity-dependent diversification model for a given set of phylogenetic branching times. It then performs a bootstrap likelihood ratio test of the diversity-dependent (DD) model against the constant-rates (CR) birth-death model. Finally, it computes the power of this test.
Usage
dd_LR(
brts,
initparsoptDD,
initparsoptCR,
missnumspec,
outputfilename = NULL,
seed = 42,
endmc = 1000,
alpha = 0.05,
plotit = TRUE,
res = 10 * (1 + length(brts) + missnumspec),
ddmodel = 1,
cond = 1,
btorph = 1,
soc = 2,
tol = c(0.001, 1e-04, 1e-06),
maxiter = 2000,
changeloglikifnoconv = FALSE,
optimmethod = "subplex",
methode = "analytical"
)
Arguments
brts |
A set of branching times of a phylogeny, all positive |
initparsoptDD |
The initial values of the parameters that must be optimized for the diversity-dependent (DD) model: lambda_0, mu and K |
initparsoptCR |
The initial values of the parameters that must be optimized for the constant-rates (CR) model: lambda and mu |
missnumspec |
The number of species that are in the clade but missing in the phylogeny |
outputfilename |
The name (and location) of the file where the output will be saved. Default is no save. |
seed |
The seed for the pseudo random number generator for simulating the bootstrap data |
endmc |
The number of bootstraps |
alpha |
The significance level of the test |
plotit |
Boolean to plot results or not |
res |
Sets the maximum number of species for which a probability must be computed, must be larger than 1 + length(brts) |
ddmodel |
Sets the model of diversity-dependence: |
cond |
Conditioning: |
btorph |
Sets whether the likelihood is for the branching times (0) or the phylogeny (1) |
soc |
Sets whether stem or crown age should be used (1 or 2) |
tol |
Sets the tolerances in the optimization. Consists of: |
maxiter |
Sets the maximum number of iterations in the optimization |
changeloglikifnoconv |
if TRUE the loglik will be set to -Inf if ML does not converge |
optimmethod |
Method used in optimization of the likelihood. Current default is 'subplex'. Alternative is 'simplex' (default of previous versions) |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
Details
The output is a list with 3 elements:
Value
treeCR |
a list of trees generated under the constant-rates model using the ML parameters under the CR model |
treeDD |
a list of trees generated under the diversity-dependent model using the ML parameters under the diversity-dependent model |
out |
a dataframe with the
parameter estimates and maximum likelihoods for diversity-dependent and
constant-rates models |
pvalue |
p-value of the test |
LRalpha |
Likelihood ratio at the signifiance level alpha |
poweroftest |
power of the test for significance level alpha |
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2016. Meth. Ecol. Evol. 7: 1092-1099,
doi: 10.1111/2041-210X.12565
- Etienne, R.S. et al. 2012, Proc. Roy.
Soc. B 279: 1300-1309, doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B.
Haegeman 2012. Am. Nat. 180: E75-E89, doi: 10.1086/667574
See Also
Maximization of the loglikelihood under a diversity-dependent diversification model
Description
This function computes the maximum likelihood estimates of the parameters of a diversity-dependent diversification model for a given set of phylogenetic branching times. It also outputs the corresponding loglikelihood that can be used in model comparisons.
Usage
dd_ML(
brts,
initparsopt = initparsoptdefault(ddmodel, brts, missnumspec),
idparsopt = 1:length(initparsopt),
idparsfix = (1:(3 + (ddmodel == 5)))[-idparsopt],
parsfix = parsfixdefault(ddmodel, brts, missnumspec, idparsopt),
res = 10 * (1 + length(brts) + missnumspec),
ddmodel = 1,
missnumspec = 0,
cond = 1,
btorph = 1,
soc = 2,
tol = c(0.001, 1e-04, 1e-06),
maxiter = 1000 * round((1.25)^length(idparsopt)),
changeloglikifnoconv = FALSE,
optimmethod = "subplex",
num_cycles = 1,
methode = "analytical",
verbose = FALSE
)
Arguments
brts |
A set of branching times of a phylogeny, all positive |
initparsopt |
The initial values of the parameters that must be optimized |
idparsopt |
The ids of the parameters that must be optimized, e.g. 1:3
for intrinsic speciation rate, extinction rate and carrying capacity. The
ids are defined as follows: |
idparsfix |
The ids of the parameters that should not be optimized, e.g. c(1,3) if lambda and K should not be optimized, but only mu. In that case idparsopt must be 2. The default is to fix all parameters not specified in idparsopt. |
parsfix |
The values of the parameters that should not be optimized |
res |
Sets the maximum number of species for which a probability must be computed, must be larger than 1 + length(brts) |
ddmodel |
Sets the model of diversity-dependence: |
missnumspec |
The number of species that are in the clade but missing in the phylogeny |
cond |
Conditioning: |
btorph |
Sets whether the likelihood is for the branching times (0) or the phylogeny (1) |
soc |
Sets whether stem or crown age should be used (1 or 2) |
tol |
Sets the tolerances in the optimization. Consists of: |
maxiter |
Sets the maximum number of iterations in the optimization |
changeloglikifnoconv |
if TRUE the loglik will be set to -Inf if ML does not converge |
optimmethod |
Method used in optimization of the likelihood. Current default is 'subplex'. Alternative is 'simplex' (default of previous versions) |
num_cycles |
the number of cycles of opimization. If set at Inf, it will do as many cycles as needed to meet the tolerance set for the target function. |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
verbose |
Show the parameters and loglikelihood for every call to the loglik function |
Details
The output is a dataframe containing estimated parameters and maximum loglikelihood. The computed loglikelihood contains the factor q! m! / (q + m)! where q is the number of species in the phylogeny and m is the number of missing species, as explained in the supplementary material to Etienne et al. 2012.
Value
lambda |
gives the maximum likelihood estimate of lambda |
mu |
gives the maximum likelihood estimate of mu |
K |
gives the maximum likelihood estimate of K |
r |
(only if ddmodel == 5) gives the ratio of linear dependencies in speciation and extinction rates |
loglik |
gives the maximum loglikelihood |
df |
gives the number of estimated parameters, i.e. degrees of feedom |
conv |
gives a message on convergence of optimization; conv = 0 means convergence |
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_loglik, dd_SR_ML,
dd_KI_ML,
Examples
cat("Estimating the intrinsic speciation rate lambda and the carrying capacity K")
cat("for a fixed extinction rate of 0.1, conditioning on clade survival and two missing species:")
brts = 1:5
dd_ML(brts = brts,initparsopt = c(1.3078,7.4188), idparsopt = c(1,3), parsfix = 0.1,
cond = 1, missnumspec = 2, tol = c(1E-3,1E-3,1E-4), optimmethod = 'simplex')
Maximization of the loglikelihood under a diversity-dependent diversification model with decoupling of a subclade's diversication dynamics from the main clade's dynamics
Description
This function computes the maximum likelihood estimates of the parameters of a diversity-dependent diversification model where the diversity-dependent dynamics of an innovative subclade have different parameters from the dynamics of the main clade from time t_d, but both are governed by the same carrying capacity and experience each other's diversity. Required isa given set of phylogenetic branching times of main clade and subclade and the time of splitting of the lineage that will form the subclade. The function also outputs the corresponding loglikelihood that can be used in model comparisons.
Usage
dd_MS_ML(
brtsM,
brtsS,
tsplit,
initparsopt = c(0.5, 0.1, 2 * (1 + length(brtsM) + length(brtsS) + sum(missnumspec)),
(tsplit + max(brtsS))/2),
parsfix = NULL,
idparsopt = c(1:3, 6),
idparsfix = NULL,
idparsnoshift = (1:6)[c(-idparsopt, (-1)^(length(idparsfix) != 0) * idparsfix)],
res = 10 * (1 + length(c(brtsM, brtsS)) + sum(missnumspec)),
ddmodel = 1.3,
missnumspec = 0,
cond = 0,
soc = 2,
tol = c(0.001, 1e-04, 1e-06),
maxiter = 1000 * round((1.25)^length(idparsopt)),
changeloglikifnoconv = FALSE,
optimmethod = "subplex",
num_cycles = 1,
methode = "ode45",
correction = FALSE,
verbose = FALSE
)
Arguments
brtsM |
A set of branching times of the main clade in a phylogeny, all positive |
brtsS |
A set of branching times of the subclade in a phylogeny, all positive |
tsplit |
The branching time at which the lineage forming the subclade branches off, positive |
initparsopt |
The initial values of the parameters that must be optimized |
parsfix |
The values of the parameters that should not be optimized |
idparsopt |
The ids of the parameters that must be optimized, e.g. 1:7
for all parameters. The ids are defined as follows: |
idparsfix |
The ids of the parameters that should not be optimized, e.g. c(1,3,4,6) if lambda and K should not be optimized, but only mu. In that case idparsopt must be c(2,5,7). The default is to fix all parameters not specified in idparsopt. |
idparsnoshift |
The ids of the parameters that should not shift; This can only apply to ids 4, 5 and 6, e.g. idparsnoshift = c(4,5) means that lambda and mu have the same values before and after tshift |
res |
sets the maximum number of species for which a probability must be computed, must be larger than 1 + max(length(brtsM),length(brtsS)) |
ddmodel |
sets the model of diversity-dependence: |
missnumspec |
The number of species that are in the clade but missing in the phylogeny. One can specify the sum of the missing species in main clade and subclade or a vector c(missnumspec_M,missnumspec_S) with missing species in main clade and subclade respectively. |
cond |
Conditioning: |
soc |
Sets whether stem or crown age should be used (1 or 2); stem age only works when cond = 0 |
tol |
Sets the tolerances in the optimization. Consists of: |
maxiter |
Sets the maximum number of iterations in the optimization |
changeloglikifnoconv |
if TRUE the loglik will be set to -Inf if ML does not converge |
optimmethod |
Method used in optimization of the likelihood. Current default is 'subplex'. Alternative is 'simplex' (default of previous versions) |
num_cycles |
the number of cycles of opimization. If set at Inf, it will do as many cycles as needed to meet the tolerance set for the target function. |
methode |
The method used in the ode solver. This can be either 'analytical' for explicit matrix exponentation or any of the solvers in the deSolve package. |
correction |
Sets whether the correction should be applied (TRUE) or not (FALSE) |
verbose |
Show the parameters and loglikelihood for every call to the loglik function |
Details
The output is a dataframe containing estimated parameters and maximum loglikelihood. The computed loglikelihood contains the factor q! m!/(q + m)! where q is the number of species in the phylogeny and m is the number of missing species, as explained in the supplementary material to Etienne et al. 2012.
Value
lambda_M |
gives the maximum likelihood estimate of lambda of the main clade |
mu_M |
gives the maximum likelihood estimate of mu of the main clade |
K_M |
gives the maximum likelihood estimate of K of the main clade |
lambda_2 |
gives the maximum likelihood estimate of lambda of the subclade |
mu_S |
gives the maximum likelihood estimate of mu of the subclade |
t_d |
gives the time of the key innovation event |
loglik |
gives the maximum loglikelihood |
df |
gives the number of estimated parameters, i.e. degrees of feedom |
conv |
gives a message on convergence of optimization; conv = 0 means convergence |
Note
The optimization may get trapped in local optima. Try different starting values to search for the global optimum.
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_MS_loglik, dd_ML,
dd_KI_ML, dd_SR_ML,
Examples
cat("This will estimate parameters for two sets of branching times brtsM, brtsS\n")
cat("without conditioning.\n")
cat("The tolerance of the optimization is set high so runtime is fast in this example.\n")
cat("In real applications, use the default or more stringent settins for tol.\n")
brtsM = 4:10
brtsS = seq(0.1,3.5,0.7)
tsplit = 5
dd_MS_ML(brtsM = brtsM, brtsS = brtsS, tsplit = tsplit, idparsopt = c(1:3,6),
initparsopt = c(0.885, 2e-14, 10, 4.001), idparsfix = NULL, parsfix = NULL,
idparsnoshift = c(4,5), cond = 0, tol = c(3E-1,3E-1,3E-1))
Loglikelihood for macro-evolutionary succession under diversity-dependent diversification with the key innovation at time t = t_d
Description
This function computes the loglikelihood of a diversity-dependent diversification model for a given set of branching times and parameter values where the diversity-dependent dynamics of an innovative subclade have different parameters from the dynamics of the main clade from time t_d, but both are governed by the same carrying capacity and experience each other's diversity.
Usage
dd_MS_loglik(
pars1,
pars2,
brtsM,
brtsS,
missnumspec,
methode = "odeint::runge_kutta_cash_karp54"
)
Arguments
pars1 |
Vector of parameters: |
pars2 |
Vector of model settings: |
brtsM |
A set of branching times of the main clade in the phylogeny, all positive |
brtsS |
A set of branching times of the subclade in the phylogeny, all positive |
missnumspec |
The number of species that are in the clade but missing in the phylogeny. One can specify the sum of the missing species in main clade and subclade or a vector c(missnumspec_M,missnumspec_S) with missing species in main clade and subclade respectively. |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
Value
The loglikelihood
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_MS_ML, dd_loglik,
dd_KI_loglik, dd_SR_loglik
Examples
pars1 = c(0.2,0.1,40,1.0,0.1,9.8)
pars2 = c(200,1,0,18.8,1,2)
missnumspec = 0
brtsM = c(25.2,24.6,24.0,22.5,21.7,20.4,19.9,19.7,18.8,17.1,15.8,11.8,9.7,8.9,5.7,5.2)
brtsS = c(9.6,8.6,7.4,4.9,2.5)
dd_MS_loglik(pars1,pars2,brtsM,brtsS,missnumspec)
Function to simulate the macro-evolutionary succession process assuming diversity-dependent diversification
Description
Simulating a diversity-dependent diversification process where at a given time a new clade emerges with different inherent speciation rate and extinction rate
Usage
dd_MS_sim(pars, age, ddmodel = 1.3)
Arguments
pars |
Vector of parameters: |
age |
Sets the crown age for the simulation |
ddmodel |
Sets the model of diversity-dependence: |
Value
out |
A list with the following elements: The first element
is the tree of extant species in phylo format |
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
dd_MS_sim(c(0.2,0.1,20,0.1,0.05,4),10)
Maximization of the loglikelihood under a diversity-dependent diversification model with a shift in the parameters
Description
This function computes the maximum likelihood estimates of the parameters of a diversity-dependent diversification model with shifting parameters at time t = tshift for a given set of phylogenetic branching times. It also outputs the corresponding loglikelihood that can be used in model comparisons.
Usage
dd_SR_ML(
brts,
initparsopt = c(0.5, 0.1, 2 * (1 + length(brts) + missnumspec), 2 * (1 + length(brts) +
missnumspec), max(brts)/2),
parsfix = NULL,
idparsopt = c(1:3, 6:7),
idparsfix = NULL,
idparsnoshift = (1:7)[c(-idparsopt, (-1)^(length(idparsfix) != 0) * idparsfix)],
res = 10 * (1 + length(brts) + missnumspec),
ddmodel = 1,
missnumspec = 0,
cond = 1,
btorph = 1,
soc = 2,
allbp = FALSE,
tol = c(0.001, 1e-04, 1e-06),
maxiter = 1000 * round((1.25)^length(idparsopt)),
changeloglikifnoconv = FALSE,
optimmethod = "subplex",
num_cycles = 1,
methode = "analytical",
verbose = FALSE
)
Arguments
brts |
A set of branching times of a phylogeny, all positive |
initparsopt |
The initial values of the parameters that must be optimized |
parsfix |
The values of the parameters that should not be optimized |
idparsopt |
The ids of the parameters that must be optimized, e.g. 1:7
for all parameters. The ids are defined as follows: |
idparsfix |
The ids of the parameters that should not be optimized, e.g. c(1,3,4,6) if lambda and K should not be optimized, but only mu. In that case idparsopt must be c(2,5,7). The default is to fix all parameters not specified in idparsopt. |
idparsnoshift |
The ids of the parameters that should not shift; This can only apply to ids 4, 5 and 6, e.g. idparsnoshift = c(4,5) means that lambda and mu have the same values before and after tshift |
res |
sets the maximum number of species for which a probability must be computed, must be larger than 1 + length(brts) |
ddmodel |
sets the model of diversity-dependence: |
missnumspec |
The number of species that are in the clade but missing in the phylogeny |
cond |
Conditioning: |
btorph |
Sets whether the likelihood is for the branching times (0) or the phylogeny (1) |
soc |
Sets whether stem or crown age should be used (1 or 2) |
allbp |
Sets whether a search should be done with various initial conditions, with tshift at each of the branching points (TRUE/FALSE) |
tol |
Sets the tolerances in the optimization. Consists of: |
maxiter |
Sets the maximum number of iterations in the optimization |
changeloglikifnoconv |
if TRUE the loglik will be set to -Inf if ML does not converge |
optimmethod |
Method used in optimization of the likelihood. Current default is 'subplex'. Alternative is 'simplex' (default of previous versions) |
num_cycles |
the number of cycles of opimization. If set at Inf, it will do as many cycles as needed to meet the tolerance set for the target function. |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
verbose |
Show the parameters and loglikelihood for every call to the loglik function |
Details
The output is a dataframe containing estimated parameters and maximum loglikelihood. The computed loglikelihood contains the factor q! m!/(q + m)! where q is the number of species in the phylogeny and m is the number of missing species, as explained in the supplementary material to Etienne et al. 2012.
Value
lambda_1 |
gives the maximum likelihood estimate of lambda before the shift |
mu_1 |
gives the maximum likelihood estimate of mu before the shift |
K_1 |
gives the maximum likelihood estimate of K before the shift |
lambda_2 |
gives the maximum likelihood estimate of lambda after the shift |
mu_2 |
gives the maximum likelihood estimate of mu after the shift |
K_2 |
gives the maximum likelihood estimate of K after the shift |
t_shift |
gives the time of the shift |
loglik |
gives the maximum loglikelihood |
df |
gives the number of estimated parameters, i.e. degrees of feedom |
conv |
gives a message on convergence of optimization; conv = 0 means convergence |
Note
The optimization may get trapped in local optima. Try different starting values to search for the global optimum.
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_SR_loglik, dd_ML,
dd_KI_ML,
Examples
cat("This will estimate parameters for a sets of branching times brts without conditioning.\n")
cat("The tolerance of the optimization is set ridiculously high to make runtime fast.\n")
cat("In real applications, use the default or more stringent settings for tol.\n")
brts = 1:10
dd_SR_ML(brts = brts, initparsopt = c(0.4581, 1E-6, 17.69, 11.09, 8.9999), idparsopt = c(1:3,6,7),
idparsfix = NULL, parsfix = NULL, idparsnoshift = c(4,5), cond = 0,
tol = c(1E-1,1E-1,1E-1),optimmethod = 'simplex'
)
Loglikelihood for diversity-dependent diversification models with a shift in the parameters at time t = tshift
Description
This function computes loglikelihood of a diversity-dependent diversification model for a given set of branching times and parameter values where the parameters are allowed to shift at time t = tshift
Usage
dd_SR_loglik(pars1, pars2, brts, missnumspec, methode = "analytical")
Arguments
pars1 |
Vector of parameters: |
pars2 |
Vector of model settings: |
brts |
A set of branching times of a phylogeny, all positive |
missnumspec |
The number of species that are in the clade but missing in the phylogeny |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
Value
The loglikelihood
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_SR_ML, dd_loglik,
dd_KI_loglik
Examples
dd_SR_loglik(pars1 = c(0.2,0.1,50,0.2,0.1,70,5), pars2 = c(100,1,1,1,0,2),
brts = 1:10, missnumspec = 0)
Function to simulate the diversity-dependent diversification process with a shift in one or more of the parameters
Description
Simulating the diversity-dependent diversification process with a parameter shift at a certain time
Usage
dd_SR_sim(pars, age, ddmodel = 1)
Arguments
pars |
Vector of parameters: |
age |
Sets the crown age for the simulation |
ddmodel |
Sets the model of diversity-dependence: |
Value
out |
A list with the following four elements: The first
element is the tree of extant species in phylo format |
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
dd_SR_sim(c(0.2,0.1,20,0.2,0.1,40,5),10)
Loglikelihood for diversity-dependent diversification models
Description
This function computes loglikelihood of a diversity-dependent diversification model for a given set of branching times and parameter values.
Usage
dd_loglik(pars1, pars2, brts, missnumspec, methode = "analytical")
Arguments
pars1 |
Vector of parameters:
|
pars2 |
Vector of model settings:
|
brts |
A set of branching times of a phylogeny, all positive |
missnumspec |
The number of species that are in the clade but missing in the phylogeny |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used. Before version 3.1 these were solvers from the deSolve package such as 'lsoda' and 'ode45'. Currently solvers from odeint are used, such as 'odeint::runge_kutta_cash_karp54', 'odeint::runge_kutta_fehlberg78', 'odeint::runge_kutta_dopri5', or odeint::bulirsch_stoer'. The first two are recommended in most cases. |
Value
The loglikelihood
Author(s)
Rampal S. Etienne & Bart Haegeman
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
See Also
dd_ML, dd_SR_loglik,
dd_KI_loglik
Examples
dd_loglik(pars1 = c(0.5,0.1,100), pars2 = c(100,1,1,1,0,2), brts = 1:10, missnumspec = 0)
Loglikelihood for diversity-dependent diversification models with multiple decoupling (rate shift) events
Description
This function computes loglikelihood of a diversity-dependent diversification model for a given set of branching times and parameter values where the diversity-dependent dynamics of subclades decouple from the dynamics of main clades, potentially accompanied by a shift in parameters.
Usage
dd_multiple_KI_loglik(
pars1_list,
pars2,
brts_k_list,
missnumspec_list,
reltol = 1e-14,
abstol = 1e-16,
methode = "odeint::runge_kutta_cash_karp54"
)
Arguments
pars1_list |
list of paramater sets one for each rate regime (subclade). The parameters are: lambda (speciation rate), mu (extinction rate), and K (clade-level carrying capacity). |
pars2 |
Vector of model settings: |
brts_k_list |
list of matrices, one for each rate regime (subclade). Each matrix has in the first row the branching times including the shift/decoupling time and the present time (0) in negative time (i.e. 10 mya = -10). In the second row it has the number of lineages, i.e. starting at 2 for a phylogeny with a crown and increasing by one at each branching time and decreasing by one at each decoupling/shift time. The last element is the same as the second last. |
missnumspec_list |
list containing the number of missing species for each clade. If only a single number m of missing species is known for the entire phylogeny, then each element of the list should be 0:m. One can also create this from m using the function create_missnumspec_list |
reltol |
relative tolerance in integration of the ODE system, default at 1e-14 |
abstol |
tolerance tolerance in integration of the ODE system, default at 1e-16 |
methode |
The method used to solve the master equation, default is 'analytical' which uses matrix exponentiation; alternatively numerical ODE solvers can be used, such as 'odeint::runge_kutta_cash_karp54'. These were used in the package before version 3.1. |
Function to simulate the diversity-dependent diversification process
Description
Simulating the diversity-dependent diversification process
Usage
dd_sim(pars, age, ddmodel = 1)
Arguments
pars |
Vector of parameters: |
age |
Sets the crown age for the simulation |
ddmodel |
Sets the model of diversity-dependence: |
Value
out |
A list with the following four elements: The first
element is the tree of extant species in phylo format |
Author(s)
Rampal S. Etienne
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
dd_sim(c(0.2,0.1,20),10)
Carries out optimization (finding a minimum)
Description
A wrapper to use several optimization routines, currently only 'simplex' (a method adopted from Matlab, or 'subplex', from the R package subplex). The function is called from several packages by the same author.
Usage
optimizer(
optimmethod = "simplex",
optimpars = c(1e-04, 1e-04, 1e-06, 1000),
num_cycles = 1,
fun,
trparsopt,
jitter = 0,
...
)
Arguments
optimmethod |
The method to use for optimization, either 'simplex' or 'subplex' |
optimpars |
Parameters of the optimization: relative tolerance in function arguments, relative tolerance in function value, absolute tolerance in function arguments, and maximum number of iterations |
num_cycles |
Number of cycles of the optimization. When set to Inf, the optimization will be repeated until the result is, within the tolerance, equal to the starting values, with a maximum of 10 cycles. |
fun |
Function to be optimized |
trparsopt |
Initial guess of the parameters to be optimized |
jitter |
Perturbation of an initial parameter value when precisely equal to 0.5; this is only relevant when subplex is chosen. The default value is 0, so no jitter is applied. A recommended value when using it is 1E-5. |
... |
Any other arguments of the function to be optimimzed, or settings of the optimization routine |
Value
out |
A list containing optimal function arguments
( |
.
Author(s)
Rampal S. Etienne
Examples
cat("No examples")
Function to convert phylogeny to a table with speciation and extinction events
Description
Converting a phylogeny to a table with speciation and extinction events
Usage
phylo2L(phy)
Arguments
phy |
A phylogeny of the phylo type |
Value
L |
Matrix of events as produced by dd_sim: |
Author(s)
Liang Xu
References
- Etienne, R.S. et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
doi: 10.1098/rspb.2011.1439
- Etienne, R.S. & B. Haegeman 2012. Am. Nat.
180: E75-E89, doi: 10.1086/667574
Examples
sim = dd_sim(c(0.2,0.1,20),10)
phy = sim$tas
L = phylo2L(phy)
phy2 = L2phylo(L, dropextinct = FALSE)
graphics::par(mfrow = c(1,3))
graphics::plot(phy)
graphics::plot(phy2)
graphics::plot(L2phylo(sim$L, dropextinct = FALSE))
Sampling in which zero probabilities are removed
Description
Sampling in which zero probabilities are removed
Usage
rng_respecting_sample(x, size, replace, prob)
Arguments
x |
either a vector of one or more elements from which to choose, or a positive integer. See ‘Details.’ |
size |
a non-negative integer giving the number of items to choose. |
replace |
should sampling be with replacement? |
prob |
a vector of probability weights for obtaining the elements of the vector being sampled. |
Value
a vector of length size with elements drawn from either x or from the integers 1:x.
Note
thanks to Pedro Neves for finding this feature in base::sample
Author(s)
Richel J.C. Bilderbeek
See Also
See sample for more details
Examples
# Number of draws
n <- 1000
# Do normal sampling
set.seed(42)
draws_1 <- DDD:::rng_respecting_sample(
1:3, size = n, replace = TRUE, prob = c(1.0, 1.0, 1.0)
)
# Do a sampling with one element of probability zero
set.seed(42)
draws_2 <- DDD:::rng_respecting_sample(
1:4, size = n, replace = TRUE, prob = c(1.0, 1.0, 1.0, 0.0)
)
testit::assert(sum(draws_2 == 4) == 0)
testit::assert(draws_1 == draws_2)
# Use base sampling will give different results,
# as it results in different RNG values
set.seed(42)
draws_3 <- sample(
1:4, size = n, replace = TRUE, prob = c(1.0, 1.0, 1.0, 0.0)
)
testit::assert(sum(draws_3 == 4) == 0)
testit::assert(!all(draws_1 == draws_3))
Rounds up in the usual manner
Description
The standard round function in R rounds x.5 to the nearest even integer. This is odd behavior that is corrected in roundn
Usage
roundn(x, digits = 0)
Arguments
x |
Number to be rounded |
digits |
Sets the number of decimals in rounding. |
Value
n |
A number |
Author(s)
Rampal S. Etienne
Examples
round(2.5)
roundn(2.5)
round(3.5)
roundn(3.5)
round(2.65,digits = 1)
roundn(2.65,digits = 1)
round(2.75,digits = 1)
roundn(2.75,digits = 1)
Takes samples in the usual manner
Description
The standard sample function in R samples from n numbers when x = n. This is unwanted behavior when the size of the vector to sample from changes dynamically. This is corrected in sample2
Usage
sample2(x, size, replace = FALSE, prob = NULL)
Arguments
x |
A vector of one or more elements |
size |
A non-negative integer giving the number of items to choose. |
replace |
Should sampling be with replacement? |
prob |
A vector of probability weights for obtaining the elements of the vector being sampled. |
Value
sam |
A vector of length |
Author(s)
Rampal S. Etienne
Examples
sample(x = 10,size = 5,replace = TRUE)
sample2(x = 10,size = 5,replace = TRUE)
Carries out optimization using a simplex algorithm (finding a minimum)
Description
Function to optimize target function using a simplex method adopted from Matlab
Usage
simplex(fun, trparsopt, optimpars, ...)
Arguments
fun |
Function to be optimized |
trparsopt |
Initial guess of the parameters to be optimized |
optimpars |
Parameters of the optimization: relative tolerance in function arguments, relative tolerance in function value, absolute tolerance in function arguments, and maximum number of iterations |
... |
Any other arguments of the function to be optimimzed, or settings of the optimization routine |
Value
out |
A list containing optimal function arguments
( |
.
Author(s)
Rampal S. Etienne
Examples
cat("No examples")
Simulation of a diversity-dependent-like time-dependent process
Description
Simulates a phylogenetic tree branching according to a time-dependent process calibrated on the expected number of species under a diversity-dependent process over time.
Usage
td_sim(pars, age, ddmodel = 1, methode = "ode45")
Arguments
pars |
Vector of parameters:
|
age |
crown age of the tree to simulate, i.e. the simulation time. |
ddmodel |
the diversity-dependent model used as reference for the time-dependent model. |
methode |
The method used to solve the master equation.
See |
Value
A list with the following four elements: The first element is the
tree of extant species in phylo format
The second element is the tree of all species, including extinct species,
in phylo format
The third element is a matrix of all species where
- the first column is the time at which a species is born
- the second column is the label of the parent of the species;
positive and negative values only indicate whether the species belongs to
the left or right crown lineage
- the third column is the label of the daughter species itself;
positive and negative values only indicate whether the species belongs to
the left or right crown lineage
- the fourth column is the time of extinction of the species. If this
equals -1, then the species is still extant.
Author(s)
César Martinez, Rampal S. Etienne
Transforming parameters from -Inf to Inf into parameters from -1 to 1
Description
Function to transform pars in a way that is more useful for optimization: trpars <- sign(pars) * pars/(sign(pars) + pars);
Usage
transform_pars(pars)
Arguments
pars |
Parameters to be transformed |
Value
Transformed parameters
Author(s)
Rampal S. Etienne
Untransforming parameters from -1 to 1 into parameters from -Inf to Inf.
Description
Function to untransform pars after optimization: pars <- sign(trpars) * trpars/(sign(trpars) - trpars);
Usage
untransform_pars(trpars)
Arguments
trpars |
Parameters to be untransformed |
Value
Untransformed parameters
Author(s)
Rampal S. Etienne