sample.basic.models: Samples simple increase/decrease models through time with...
In ACDC: Analysis of Congruent Diversification Classes

Description Usage Arguments Value Examples

Samples simple increase/decrease models through time with noise.

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,
  min.rate = 0,
  max.rate = 10
)

`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
`min.rate`	The minimum rate (rescaling fone after after drawing rates).
`max.rate`	The maximum rate (rescaling fone after after drawing rates).

Speciation or extinction rate at a number of timepoints.

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