View source: R/fit_hbd_psr_on_grid.R

fit_hbd_psr_on_grid | R Documentation |

Given an ultrametric timetree, estimate the pulled speciation rate of homogenous birth-death (HBD) models that best explains the tree via maximum likelihood. Every HBD model is defined by some speciation and extinction rates (`\lambda`

and `\mu`

) over time, as well as the sampling fraction `\rho`

(fraction of extant species sampled). “Homogenous” refers to the assumption that, at any given moment in time, all lineages exhibit the same speciation/extinction rates. For any given HBD model there exists an infinite number of alternative HBD models that predict the same deterministic lineages-through-time curve and yield the same likelihood for any given reconstructed timetree; these “congruent” models cannot be distinguished from one another solely based on the tree.

Each congruence class is uniquely described by the “pulled speciation rate” (PSR), defined as the relative slope of the deterministic LTT over time, `PSR=-M^{-1}dM/d\tau`

(where `\tau`

is time before present). In other words, two HBD models are congruent if and only if they have the same PSR. This function is designed to estimate the generating congruence class for the tree, by fitting the PSR on a discrete time grid.

```
fit_hbd_psr_on_grid( tree,
oldest_age = NULL,
age0 = 0,
age_grid = NULL,
min_PSR = 0,
max_PSR = +Inf,
guess_PSR = NULL,
fixed_PSR = NULL,
splines_degree = 1,
condition = "auto",
relative_dt = 1e-3,
Ntrials = 1,
Nbootstraps = 0,
Ntrials_per_bootstrap = NULL,
Nthreads = 1,
max_model_runtime = NULL,
fit_control = list(),
verbose = FALSE,
diagnostics = FALSE,
verbose_prefix = "")
```

`tree` |
A rooted ultrametric timetree of class "phylo", representing the time-calibrated phylogeny of a set of extant sampled species. |

`oldest_age` |
Strictly positive numeric, specifying the oldest time before present (“age”) to consider when calculating the likelihood. If this is equal to or greater than the root age, then |

`age0` |
Non-negative numeric, specifying the youngest age (time before present) to consider for fitting. If |

`age_grid` |
Numeric vector, listing ages in ascending order at which the PSR is allowed to vary independently. This grid must cover at least the age range from |

`min_PSR` |
Numeric vector of length Ngrid (= |

`max_PSR` |
Numeric vector of length Ngrid, or a single numeric, specifying upper bounds for the fitted PSR at each point in the age grid. If a single numeric, the same upper bound applies at all ages. Use |

`guess_PSR` |
Initial guess for the PSR at each age-grid point. Either |

`fixed_PSR` |
Optional fixed (i.e. non-fitted) PSR values on one or more age-grid points. Either |

`splines_degree` |
Integer between 0 and 3 (inclusive), specifying the polynomial degree of the PSR between age-grid points. If 0, then the PSR is considered piecewise constant, if 1 then the PSR is considered piecewise linear, if 2 or 3 then the PSR is considered to be a spline of degree 2 or 3, respectively. The |

`condition` |
Character, either "crown", "stem", "auto", "stemN" or "crownN" (where N is an integer >=2), specifying on what to condition the likelihood. If "crown", the likelihood is conditioned on the survival of the two daughter lineages branching off at the root at that time. If "stem", the likelihood is conditioned on the survival of the stem lineage, with the process having started at |

`relative_dt` |
Strictly positive numeric (unitless), specifying the maximum relative time step allowed for integration over time, when calculating the likelihood. Smaller values increase integration accuracy but increase computation time. Typical values are 0.0001-0.001. The default is usually sufficient. |

`Ntrials` |
Integer, specifying the number of independent fitting trials to perform, each starting from a random choice of model parameters. Increasing |

`Nbootstraps` |
Integer, specifying an optional number of bootstrap samplings to perform, for estimating standard errors and confidence intervals of maximum-likelihood fitted parameters. If 0, no bootstrapping is performed. Typical values are 10-100. At each bootstrap sampling, a random timetree is generated under the birth-death model according to the fitted PSR, the parameters are estimated anew based on the generated tree, and subsequently compared to the original fitted parameters. Each bootstrap sampling will use roughly the same information and similar computational resources as the original maximum-likelihood fit (e.g., same number of trials, same optimization parameters, same initial guess, etc). |

`Ntrials_per_bootstrap` |
Integer, specifying the number of fitting trials to perform for each bootstrap sampling. If |

`Nthreads` |
Integer, specifying the number of parallel threads to use for performing multiple fitting trials simultaneously. This should generally not exceed the number of available CPUs on your machine. Parallel computing is not available on the Windows platform. |

`max_model_runtime` |
Optional numeric, specifying the maximum number of seconds to allow for each evaluation of the likelihood function. Use this to abort fitting trials leading to parameter regions where the likelihood takes a long time to evaluate (these are often unlikely parameter regions). |

`fit_control` |
Named list containing options for the |

`verbose` |
Logical, specifying whether to print progress reports and warnings to the screen. Note that errors always cause a return of the function (see return values |

`diagnostics` |
Logical, specifying whether to print detailed information (such as model likelihoods) at every iteration of the fitting routine. For debugging purposes mainly. |

`verbose_prefix` |
Character, specifying the line prefix for printing progress reports to the screen. |

It is generally advised to provide as much information to the function `fit_hbd_psr_on_grid`

as possible, including reasonable lower and upper bounds (`min_PSR`

and `max_PSR`

) and a reasonable parameter guess (`guess_PSR`

). It is also important that the `age_grid`

is sufficiently fine to capture the expected major variations of the PSR over time, but keep in mind the serious risk of overfitting when `age_grid`

is too fine and/or the tree is too small.

A list with the following elements:

`success` |
Logical, indicating whether model fitting succeeded. If |

`objective_value` |
The maximized fitting objective. Currently, only maximum-likelihood estimation is implemented, and hence this will always be the maximized log-likelihood. |

`objective_name` |
The name of the objective that was maximized during fitting. Currently, only maximum-likelihood estimation is implemented, and hence this will always be “loglikelihood”. |

`loglikelihood` |
The log-likelihood of the fitted model for the given timetree. |

`fitted_PSR` |
Numeric vector of size Ngrid, listing fitted or fixed pulled speciation rates (PSR) at each age-grid point. Between grid points the fitted PSR should be interpreted as a piecewise polynomial function (natural spline) of degree |

`guess_PSR` |
Numeric vector of size Ngrid, specifying the initial guess for the PSR at each age-grid point. |

`age_grid` |
The age-grid on which the PSR is defined. This will be the same as the provided |

`NFP` |
Integer, number of fitted (i.e., non-fixed) parameters. If none of the PSRs were fixed, this will be equal to Ngrid. |

`AIC` |
The Akaike Information Criterion for the fitted model, defined as |

`BIC` |
The Bayesian information criterion for the fitted model, defined as |

`converged` |
Logical, specifying whether the maximum likelihood was reached after convergence of the optimization algorithm. Note that in some cases the maximum likelihood may have been achieved by an optimization path that did not yet converge (in which case it's advisable to increase |

`Niterations` |
Integer, specifying the number of iterations performed during the optimization path that yielded the maximum likelihood. |

`Nevaluations` |
Integer, specifying the number of likelihood evaluations performed during the optimization path that yielded the maximum likelihood. |

`bootstrap_estimates` |
If |

`standard_errors` |
If |

`CI50lower` |
If |

`CI50upper` |
Similar to |

`CI95lower` |
Similar to |

`CI95upper` |
Similar to |

Stilianos Louca

S. Louca et al. (2018). Bacterial diversification through geological time. Nature Ecology & Evolution. 2:1458-1467.

S. Louca and M. W. Pennell (2020). Extant timetrees are consistent with a myriad of diversification histories. Nature. 580:502-505.

`simulate_deterministic_hbd`

`loglikelihood_hbd`

`fit_hbd_model_parametric`

`fit_hbd_model_on_grid`

`fit_hbd_pdr_parametric`

`fit_hbd_pdr_on_grid`

`fit_hbd_psr_on_best_grid_size`

`model_adequacy_hbd`

```
## Not run:
# Generate a random tree with exponentially varying lambda & mu
Ntips = 10000
rho = 0.5 # sampling fraction
time_grid = seq(from=0, to=100, by=0.01)
lambdas = 2*exp(0.1*time_grid)
mus = 1.5*exp(0.09*time_grid)
sim = generate_random_tree( parameters = list(rarefaction=rho),
max_tips = Ntips/rho,
coalescent = TRUE,
added_rates_times = time_grid,
added_birth_rates_pc = lambdas,
added_death_rates_pc = mus)
tree = sim$tree
root_age = castor::get_tree_span(tree)$max_distance
cat(sprintf("Tree has %d tips, spans %g Myr\n",length(tree$tip.label),root_age))
# Fit PSR on grid
oldest_age=root_age/2 # only consider recent times when fitting
Ngrid = 10
age_grid = seq(from=0,to=oldest_age,length.out=Ngrid)
fit = fit_hbd_psr_on_grid(tree,
oldest_age = oldest_age,
age_grid = age_grid,
min_PSR = 0,
max_PSR = +100,
condition = "crown",
Ntrials = 10,
Nthreads = 4,
max_model_runtime = 1) # limit model evaluation to 1 second
if(!fit$success){
cat(sprintf("ERROR: Fitting failed: %s\n",fit$error))
}else{
cat(sprintf("Fitting succeeded:\nLoglikelihood=%g\n",fit$loglikelihood))
# plot fitted PSR
plot( x = fit$age_grid,
y = fit$fitted_PSR,
main = 'Fitted PSR',
xlab = 'age',
ylab = 'PSR',
type = 'b',
xlim = c(root_age,0))
# plot deterministic LTT of fitted model
plot( x = fit$age_grid,
y = fit$fitted_LTT,
main = 'Fitted dLTT',
xlab = 'age',
ylab = 'lineages',
type = 'b',
log = 'y',
xlim = c(root_age,0))
# get fitted PSR as a function of age
PSR_fun = approxfun(x=fit$age_grid, y=fit$fitted_PSR)
}
## End(Not run)
```

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