| Title: | Congruent Rate Analyses in Birth-Death Scenarios |
| Version: | 1.2.0 |
| Encoding: | UTF-8 |
| Description: | Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) <doi:10.1038/s41586-020-2176-1>. |
| LazyData: | true |
| Depends: | R (≥ 3.5.0), ggplot2 |
| Imports: | magrittr, deSolve, dplyr, tibble, colorspace, patchwork, latex2exp, tidyr, pracma, ape |
| License: | GPL-3 |
| Suggests: | knitr, rmarkdown |
| RoxygenNote: | 7.2.3 |
| URL: | https://github.com/afmagee/CRABS |
| NeedsCompilation: | no |
| Packaged: | 2023-10-24 10:01:07 UTC; bkopper |
| Author: | Bjørn Tore Kopperud [aut, cre], Sebastian Höhna [aut], Andrew F. Magee [aut], Jérémy Andréoletti [aut] |
| Maintainer: | Bjørn Tore Kopperud <kopperud@protonmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-10-24 10:20:07 UTC |
CRABS: Congruent Rate Analyses in Birth-death Scenarios
Description
Features tools for exploring congruent phylogenetic birth-death models. It can construct the pulled speciation- and net-diversification rates from a reference model. Given alternative speciation- or extinction rates, it can construct new models that are congruent with the reference model. Functionality is included to sample new rate functions, and to visualize the distribution of one congruence class. See also Louca & Pennell (2020) doi:10.1038/s41586-020-2176-1.
References
Louca, S., & Pennell, M. W. (2020). Extant timetrees are consistent with a myriad of diversification histories. Nature, 580(7804), 502-505. https://doi.org/10.1038/s41586-020-2176-1
Höhna, S., Kopperud, B. T., & Magee, A. F. (2022). CRABS: Congruent rate analyses in birth–death scenarios. Methods in Ecology and Evolution, 13, 2709– 2718. https://doi.org/10.1111/2041-210X.13997
Kopperud, B. T., Magee, A. F., & Höhna, S. (2023). Rapidly Changing Speciation and Extinction Rates Can Be Inferred in Spite of Nonidentifiability. Proceedings of the National Academy of Sciences 120 (7): e2208851120. https://doi.org/10.1073/pnas.2208851120
Andréoletti, J. & Morlon, H. (2023). Exploring congruent diversification histories with flexibility and parsimony. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.14240
Author(s)
Maintainer: Bjørn Tore Kopperud kopperud@protonmail.com
Authors:
Sebastian Höhna
Andrew F. Magee
Jérémy Andréoletti
See Also
Useful links:
Create a set of congruent models
Description
Create a set of congruent models
Usage
congruent.models(
model,
mus = NULL,
lambdas = NULL,
keep_ref = TRUE,
ode_solver = TRUE
)
Arguments
model |
The reference model. An object of class "CRABS" |
mus |
A list of extinction-rate functions |
lambdas |
A list of speciation-rate functions |
keep_ref |
Whether or not to keep the reference model in the congruent set |
ode_solver |
Whether to use a numerical ODE solver to solve for lambda |
Value
An object of class "CRABSset"
Examples
data(primates_ebd)
lambda <- approxfun(primates_ebd$time, primates_ebd$lambda)
mu <- approxfun(primates_ebd$time, primates_ebd$mu)
## A reference model
times <- seq(0, max(primates_ebd$time), length.out = 500)
model <- create.model(lambda, mu, times = times)
mu1 <- lapply(c(0.5, 1.5, 3.0), function(m) function(t) m)
model_set1 <- congruent.models(model, mus = mu1)
model_set1
lambda0 <- lambda(0.0) ## Speciation rates must all be equal at the present
bs <- c(0.0, 0.01, 0.02)
lambda1 <- lapply(bs, function(b) function(t) lambda0 + b*t)
model_set2 <- congruent.models(model, lambdas = lambda1)
model_set2
Compute likelihood
Description
Compute likelihood
Usage
crabs.loglikelihood(phy, model, rho = 1)
Arguments
phy |
an object of class "phylo" |
model |
an object of class "CRABS" |
rho |
the taxon sampling fraction |
Value
the log-likelihood of the tree given the model
Examples
library(ape)
lambda <- function(t) exp(0.3*t) - 0.5*t
mu <- function(t) exp(0.3*t) - 0.2*t - 0.8
model <- create.model(lambda, mu, times = seq(0, 3, by = 0.005))
set.seed(123)
phy <- rcoal(25)
crabs.loglikelihood(phy, model)
Computes the congruent class, i.e., the pulled rates.
Description
Computes the congruent class, i.e., the pulled rates.
Usage
create.model(
func_spec0,
func_ext0,
times = seq(from = 0, to = 5, by = 0.005),
func_p_spec = NULL,
func_p_div = NULL
)
Arguments
func_spec0 |
The speciation rate function (measured in time before present). |
func_ext0 |
The extinction rate function (measured in time before present). |
times |
the time knots for the piecewise-linear rate functions |
func_p_spec |
the pulled speciation rate function |
func_p_div |
the pulled net-diversification rate function |
Value
A list of rate functions representing this congruence class.
Examples
lambda1 <- function(t) exp(0.3*t) - 0.5*t + 1
mu1 <- function(t) exp(0.3*t) - 0.2*t + 0.2
model1 <- create.model(lambda1, mu1, times = seq(0, 5, by = 0.005))
model1
data("primates_ebd")
lambda2 <- approxfun(primates_ebd[["time"]], primates_ebd[["lambda"]])
mu2 <- approxfun(primates_ebd[["time"]], primates_ebd[["mu"]])
model2 <- create.model(lambda2, mu2, primates_ebd[["time"]])
model2
Plots the rate functions after filtering them according to a given penalty and predefined thresholds.
Description
Plots the rate functions after filtering them according to a given penalty and predefined thresholds.
Usage
full.plot.regularity.thresholds(
samples,
filtering_fractions = c(0.01, 0.05, 0.2, 0.9),
penalty = "L1",
rates = c("lambda", "mu")
)
Arguments
samples |
A list of (congruent) CRABS models |
filtering_fractions |
A vector of thresholds for filtering, as fractions of the most regular trajectories. |
penalty |
The choice of penalty, among "L1", "L2" and "L1_derivative" (penalty on derivative shifts). |
rates |
A vector of rate(s) to be plotted, among "lambda" (speciation), "mu" (extinction), "delta" (net-diversification) and "epsilon" (turnover). |
Value
Plots an array of rate trajectories for the chosen rates and thresholds.
Examples
data("primates_ebd")
set.seed(123)
l <- approxfun(primates_ebd[["time"]], primates_ebd[["lambda"]])
mu <- approxfun(primates_ebd[["time"]], primates_ebd[["mu"]])
times <- primates_ebd[["time"]]
model <- create.model(l, mu, times)
sample.joint.rates <- function(n) {
sample.basic.models.joint(times = times,
p.delta = model$p.delta,
beta.param = c(0.5,0.3),
lambda0 = l(0.0),
mu0.median = mu(0.0))
}
joint.samples <- sample.congruence.class(model = model,
num.samples = 100,
rate.type = "joint",
sample.joint.rates = sample.joint.rates)
full.plot.regularity.thresholds(joint.samples)
Global scale for GMRF using linear interpolation of pre-computed values
Description
Global scale for GMRF using linear interpolation of pre-computed values
Usage
get.gmrf.global.scale(v)
Arguments
v |
The number of pieces in the approximation |
Value
Global scale
Global scale for HSMRF using linear interpolation of pre-computed values
Description
Global scale for HSMRF using linear interpolation of pre-computed values
Usage
get.hsmrf.global.scale(v)
Arguments
v |
The number of pieces in the approximation |
Value
Global scale
Create a set of congruent models
Description
Create a set of congruent models
Usage
joint.congruent.models(model, mus, lambdas, keep_ref = TRUE)
Arguments
model |
The reference model. An object of class "CRABS" |
mus |
A list of extinction-rate functions |
lambdas |
A list of speciation-rate functions |
keep_ref |
Whether or not to keep the reference model in the congruent set |
Value
An object of class "CRABSset"
Examples
# This function should not have to be used externally.
# It is called in the CRABS function `sample.congruence.class` when `rate.type=="joint"`.
model2df
Description
model2df
Usage
model2df(model, gather = TRUE, rho = 1, compute.pulled.rates = TRUE)
Arguments
model |
an object of class "CRABS" |
gather |
boolean. Whether to return wide or long data frame |
rho |
the sampling fraction at the present. Used to calculate the pulled speciation rate |
compute.pulled.rates |
whether to compute the pulled rates |
Value
a data frame
Examples
lambda <- function(t) 2.0 + sin(0.8*t)
mu <- function(t) 1.5 + exp(0.15*t)
times <- seq(from = 0, to = 4, length.out = 1000)
model <- create.model( lambda, mu, times = times)
model2df(model)
Plots the rate functions including the pulled rates.
Description
Plots the rate functions including the pulled rates.
Usage
## S3 method for class 'CRABS'
plot(x, ...)
Arguments
x |
An object of class "CRABS" |
... |
other parameters |
Value
a patchwork object
Examples
data(primates_ebd)
lambda <- approxfun(primates_ebd$time, primates_ebd$lambda)
mu <- approxfun(primates_ebd$time, primates_ebd$mu)
times <- seq(0, max(primates_ebd$time), length.out = 500)
model <- create.model(lambda, mu, times = times)
plot(model)
Plots the rate functions
Description
Plots the rate functions
Usage
## S3 method for class 'CRABSset'
plot(x, ...)
Arguments
x |
A list of congruent birth-death x |
... |
other parameters |
Value
a patchwork object object
Examples
data(primates_ebd)
lambda <- approxfun(primates_ebd$time, primates_ebd$lambda)
mu <- approxfun(primates_ebd$time, primates_ebd$mu)
times <- seq(0, max(primates_ebd$time), length.out = 500)
model <- create.model(lambda, mu, times = times)
mus <- list(function(t) 0.2 + exp(0.01*t),
function(t) 0.2 + sin(0.35*t) + 0.1*t,
function(t) 1.0,
function(t) 0.5 + 0.2*t)
models <- congruent.models(model, mus = mus)
plot(models)
Primates phylogenetic tree
Description
The example tree taken from the RevBayes tutorial website
Usage
data(primates)
Format
An object of class phylo of length 5.
RevBayes Primates birth-death model
Description
The results of a bayesian horseshoe markov random field (HSMRF) episodic birth-death model, fitted on the primates tree. One hundred episodes. Each estimate is the posterior median. The time unit is millions of years before the present.
Usage
data(primates_ebd)
Format
An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 3 columns.
Primates birth-death model
Description
See ?primates_ebd, but including posterior samples instead of a summary.
Usage
data(primates_ebd_log)
Format
An object of class tbl_df (inherits from tbl, data.frame) with 251 rows and 604 columns.
TESS Primates birth-death model
Description
The results of a bayesian episodic birth-death model in the R-package TESS, fitted on the primates tree. One hundred episodes. Each estimate is the posterior median. The time unit is millions of years before the present.
Usage
data(primates_ebd_tess)
Format
An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 3 columns.
TreePar Primates birth-death model
Description
The results of a birth-death model in the R-package TreePar, fitted on the primates tree. The estimated model has two epochs, that are maximum-likelihood estimates. The time unit is millions of years before the present.
Usage
data(primates_ebd_treepar)
Format
An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 3 columns.
Print method for CRABS object
Description
Print method for CRABS object
Usage
## S3 method for class 'CRABS'
print(x, ...)
Arguments
x |
and object of class CRABS |
... |
other arguments |
Value
nothing
Examples
data(primates_ebd)
lambda <- approxfun(primates_ebd$time, primates_ebd$lambda)
mu <- approxfun(primates_ebd$time, primates_ebd$mu)
times <- seq(0, max(primates_ebd$time), length.out = 500)
model <- create.model(lambda, mu, times = times)
print(model)
Title
Description
Title
Usage
## S3 method for class 'CRABSposterior'
print(x, ...)
Arguments
x |
a list of CRABS objects |
... |
additional parameters |
Value
nothing
Examples
data(primates_ebd_log)
posterior <- read.RevBayes(primates_ebd_log, max_t = 65, n_samples = 20)
print(posterior)
Print method for CRABSset object
Description
Print method for CRABSset object
Usage
## S3 method for class 'CRABSset'
print(x, ...)
Arguments
x |
an object of class CRABSset |
... |
other arguments |
Value
nothing
Examples
data(primates_ebd)
lambda <- approxfun(primates_ebd$time, primates_ebd$lambda)
mu <- approxfun(primates_ebd$time, primates_ebd$mu)
times <- seq(0, max(primates_ebd$time), length.out = 500)
model <- create.model(lambda, mu, times = times)
mus <- list(function(t) 0.2 + exp(0.01*t),
function(t) 0.2 + sin(0.35*t) + 0.1*t,
function(t) 1.0,
function(t) 0.5 + 0.2*t)
models <- congruent.models(model, mus = mus)
print(models)
print.CRABSsets
Description
print.CRABSsets
Usage
## S3 method for class 'CRABSsets'
print(x, ...)
Arguments
x |
a list of (congruent) CRABS sets |
... |
additional parameters |
Value
nothing
Examples
data(primates_ebd_log)
posterior <- read.RevBayes(primates_ebd_log, max_t = 65, n_samples = 10)
samples <- sample.congruence.class.posterior(posterior,
num.samples = 5,
rate.type = "extinction",
rate0.median = 0.1,
model = "MRF",
max.rate = 1.0)
print(samples)
read RevBayes log file
Description
read RevBayes log file
Usage
read.RevBayes(x, n_times, max_t = 100, n_samples = 20, summary_type = "none",
extinction_prefix = "extinction_rate.", speciation_prefix = "speciation_rate.")
Arguments
x |
path to log, or data frame |
n_times |
number of time knots |
max_t |
tree height |
n_samples |
first n posterior samples |
summary_type |
either "none" for all the posterior samples, or "mean" or "median" for the posterior mean/median |
extinction_prefix |
the prefix string for the extinction rate column names. Must be unique |
speciation_prefix |
the prefix string for the speciation rate column names. Must be unique |
Value
a set of CRABS models, each being a sample in the posterior
Examples
data(primates_ebd_log)
posterior <- read.RevBayes(primates_ebd_log, n_times = 500, max_t = 65, n_samples = 20)
Samples simple increase/decrease models through time with noise.
Description
Samples simple increase/decrease models through time with noise.
Usage
sample.basic.models(
times,
rate0 = NULL,
model = "exponential",
direction = "decrease",
noisy = TRUE,
MRF.type = "HSMRF",
monotonic = FALSE,
fc.mean = 3,
rate0.median = 0.1,
rate0.logsd = 1.17481,
mrf.sd.scale = 1,
min.rate = 0,
max.rate = 10
)
Arguments
times |
the time knots |
rate0 |
The rate at present, otherwise drawn randomly. |
model |
"MRF" for pure MRF model, otherwise MRF has a trend of type "exponential","linear", or "episodic<n>" |
direction |
"increase" or "decrease" (measured in past to present) |
noisy |
If FALSE, no MRF noise is added to the trajectory |
MRF.type |
"HSMRF" or "GMRF", type for stochastic noise. |
monotonic |
Whether the curve should be forced to always move in one direction. |
fc.mean |
Determines the average amount of change when drawing from the model. |
rate0.median |
When not specified, rate at present is drawn from a lognormal distribution with this median. |
rate0.logsd |
When not specified, rate at present is drawn from a lognormal distribution with this sd |
mrf.sd.scale |
scale the sd of the mrf process up or down. defaults to 1.0 |
min.rate |
The minimum rate (rescaling fone after after drawing rates). |
max.rate |
The maximum rate (rescaling fone after after drawing rates). |
Value
Speciation or extinction rate at a number of timepoints.
Examples
data("primates_ebd")
l <- approxfun(primates_ebd[["time"]], primates_ebd[["lambda"]])
mu <- approxfun(primates_ebd[["time"]], primates_ebd[["mu"]])
times <- primates_ebd[["time"]]
model <- create.model(l, mu, times)
mus <- sample.basic.models(times = times,
rate0 = 0.05,
"MRF",
MRF.type = "HSMRF",
fc.mean = 2.0,
min.rate = 0.0,
max.rate = 1.0)
model_set <- congruent.models(model, mus = mus)
model_set
Jointly samples speciation and extinction trajectories through time, with noise.
Description
Jointly samples speciation and extinction trajectories through time, with noise.
Usage
sample.basic.models.joint(
times,
p.delta,
lambda0,
mu0 = NULL,
MRF.type = "HSMRF",
beta.param = c(0.3, 0.3),
mu0.median = 0.1,
mu0.logsd = 1.17481,
mrf.sd.scale = 1,
min.lambda = 0,
min.mu = 0,
max.lambda = 10,
max.mu = 10,
min.p = -0.05,
max.p = 1.05
)
Arguments
times |
the time knots |
p.delta |
The The pulled diversification rate function (measured in time before present). |
lambda0 |
The speciation rate at present. |
mu0 |
The extinction rate at present, otherwise drawn randomly. |
MRF.type |
"HSMRF" or "GMRF", type for stochastic noise. |
beta.param |
Parameters of the Beta distribution used for |
mu0.median |
When not specified, extinction rate at present is drawn from a lognormal distribution with this median. |
mu0.logsd |
When not specified, extinction rate at present is drawn from a lognormal distribution with this sd |
mrf.sd.scale |
scale the sd of the mrf process up or down. defaults to 1.0 |
min.lambda |
The minimum speciation rate (rescaling done after after drawing rates). |
min.mu |
The minimum extinction rate (rescaling done after after drawing rates). |
max.lambda |
The maximum speciation rate (rescaling done after after drawing rates). |
max.mu |
The maximum extinction rate (rescaling done after after drawing rates). |
min.p |
The lower bound of parameter p's trajectory. |
max.p |
The upper bound of parameter p's trajectory. |
Value
Speciation or extinction rate at a number of timepoints.
Examples
data("primates_ebd")
l <- approxfun(primates_ebd[["time"]], primates_ebd[["lambda"]])
mu <- approxfun(primates_ebd[["time"]], primates_ebd[["mu"]])
times <- primates_ebd[["time"]]
model <- create.model(l, mu, times)
sample.joint.rates <- function(n) {
sample.basic.models.joint(times = times,
p.delta = model$p.delta,
beta.param = c(0.5,0.3),
lambda0 = l(0.0),
mu0.median = mu(0.0))
}
joint.samples <- sample.congruence.class(model = model,
num.samples = 40,
rate.type = "joint",
sample.joint.rates = sample.joint.rates)
joint.samples
Stochastic exploration of congruent models.
Description
Stochastic exploration of congruent models.
Usage
sample.congruence.class(
model,
num.samples,
rate.type = "both",
sample.speciation.rates = NULL,
sample.extinction.rates = NULL,
sample.joint.rates = NULL
)
Arguments
model |
the reference model, an object of class "CRABS" |
num.samples |
The number of samples to be drawn |
rate.type |
either "extinction", "speciation", "both" or "joint" |
sample.speciation.rates |
a function that when called returns a speciation rate function |
sample.extinction.rates |
a function that when called returns a extinction rate function |
sample.joint.rates |
a function that when called returns a list with a speciation rate function and an extinction rate function |
Value
A named list with congruent rates.
Examples
data("primates_ebd")
l <- approxfun(primates_ebd[["time"]], primates_ebd[["lambda"]])
mu <- approxfun(primates_ebd[["time"]], primates_ebd[["mu"]])
times <- primates_ebd[["time"]]
model <- create.model(l, mu, primates_ebd[["time"]])
# Sampling extinction rates
extinction_rate_samples <- function(){
res <- sample.basic.models(times = times,
rate0 = 0.05,
model = "MRF",
MRF.type = "HSMRF",
fc.mean = 2.0,
min.rate = 0.0,
max.rate = 1.0)
return(res)
}
samples <- sample.congruence.class(model,
num.samples = 8,
rate.type = "extinction",
sample.extinction.rates = extinction_rate_samples)
samples
# Jointly sampling speciation and extinction rates
sample.joint.rates <- function(n) {
sample.basic.models.joint(times = times,
p.delta = model$p.delta,
beta.param = c(0.5,0.3),
lambda0 = l(0.0),
mu0.median = mu(0.0))
}
joint.samples <- sample.congruence.class(model = model,
num.samples = 40,
rate.type = "joint",
sample.joint.rates = sample.joint.rates)
joint.samples
Stochastic exploration of congruent models for all samples in the posterior
Description
This function takes a posterior sample as input: a list of CRABS objects.
It will then iterate over the samples, and for each posterior sample it will
sample from the posterior class. It will sample using the sample.basic.models
function, and all additional parameters are passed to sample.basic.models.
Usage
sample.congruence.class.posterior(
posterior,
num.samples,
rate.type = "extinction",
mu0.equal = FALSE,
rate0 = NULL,
...
)
Arguments
posterior |
a list of CRABS model objects |
num.samples |
The number of samples to be drawn |
rate.type |
either "extinction", "speciation", "both" or "joint" |
mu0.equal |
whether to propose alternative mu starting at mu0 equal to the posterior sample. default to FALSE |
rate0 |
rate0 allows the user to fix the extinction rate at the present to a single value. defaults to NULL, for drawing it randomly |
... |
Arguments passed on to
|
Value
A named list with congruent rates.
Examples
data(primates_ebd_log)
posterior <- read.RevBayes(primates_ebd_log, max_t = 65, n_samples = 10)
samples <- sample.congruence.class.posterior(posterior,
num.samples = 5,
rate.type = "extinction",
rate0.median = 0.1,
model = "MRF",
max.rate = 1.0)
print(samples)
Sample custom functions through time.
Description
Sample custom functions through time.
Usage
sample.rates(
times,
lambda0 = NULL,
rsample = NULL,
rsample0 = NULL,
autocorrelated = FALSE
)
Arguments
times |
the time knots |
lambda0 |
The rate at present |
rsample |
Function to sample next rate |
rsample0 |
Function to sample rate at present |
autocorrelated |
Should rates be autocorrelated? |
Value
Sampled rate vector
Examples
data("primates_ebd")
l <- approxfun(primates_ebd[["time"]], primates_ebd[["lambda"]])
mu <- approxfun(primates_ebd[["time"]], primates_ebd[["mu"]])
times <- primates_ebd[["time"]]
model <- create.model(l, mu, times)
rsample <- function(n) runif(n, min = 0.0, max = 0.9)
mu <- sample.rates(times, 0.5, rsample = rsample)
model_set <- congruent.models(model, mus = mu)
model_set
Summarize trends in the posterior
Description
Summarize trends in the posterior
Usage
summarize.posterior(posterior, threshold = 0.01, rate_name = "lambda",
return_data = FALSE, rm_singleton = FALSE, per_time = TRUE,
window_size = 1, relative_deltas = FALSE)
Arguments
posterior |
a list of CRABS objects, each one representing a sample from the posterior |
threshold |
a threshold for when |
rate_name |
either "lambda" or "mu" or "delta" |
return_data |
instead of plots, return the plotting dataframes |
rm_singleton |
whether or not to remove singletons. Pass starting at present, going towards ancient |
per_time |
whether to compute |
window_size |
the window size "k" in |
relative_deltas |
whether to divide |
Value
a ggplot object
Examples
data(primates_ebd_log)
posterior <- read.RevBayes(primates_ebd_log, max_t = 65, n_samples = 10)
samples <- sample.congruence.class.posterior(posterior,
num.samples = 5,
rate.type = "extinction",
rate0.median = 0.1,
model = "MRF",
max.rate = 1.0)
p <- summarize.posterior(samples, threshold = 0.05)
Summarize trends in the congruence class
Description
Summarize trends in the congruence class
Usage
summarize.trends(model_set, threshold = 0.005, rate_name = "lambda",
window_size = 1, method = "neighbour", per_time = TRUE, return_data = FALSE,
rm_singleton = FALSE, relative_deltas = FALSE, group_names = NULL)
Arguments
model_set |
an object of type "CRABSset" |
threshold |
a threshold for when |
rate_name |
either "lambda" or "mu" or "delta" |
window_size |
the window size "k" in |
method |
default to "neighbour", i.e. to compare rate values at neighbouring time points. |
per_time |
whether to compute |
return_data |
instead of plots, return the plotting dataframes |
rm_singleton |
whether or not to remove singletons. Pass starting at present, going towards ancient |
relative_deltas |
whether to divide |
group_names |
a vector of prefixes, if you want to group the models in a facet. For example 'c("reference", "model")' |
Value
a patchwork object
Examples
data(primates_ebd)
lambda <- approxfun(primates_ebd$time, primates_ebd$lambda)
mu <- approxfun(primates_ebd$time, primates_ebd$mu)
times <- seq(0, max(primates_ebd$time), length.out = 500)
reference <- create.model(lambda, mu, times = times)
mus <- list(function(t) exp(0.01*t) - 0.01*t - 0.9,
function(t) exp(-0.02*t) - 0.2,
function(t) exp(-0.07*t) + 0.02*t - 0.5,
function(t) 0.2 + 0.01*t,
function(t) 0.2)
model_set <- congruent.models(reference, mus = mus)
p <- summarize.trends(model_set, 0.02)