model.test.sim: Simulate Model Test

View source: R/model.test.sim.R

model.test.simR Documentation

Simulate Model Test

Description

Simulate models of disparity change through time

Usage

model.test.sim(
  sim = 1,
  model,
  model.rank = 1,
  alternative = "two-sided",
  time.split = NULL,
  time.span = 100,
  variance = 1,
  sample.size = 100,
  parameters = list(),
  fixed.optima = FALSE
)

Arguments

sim

The number of separate simulations to run.

model

Either (i) the named model of evolution to simulate for changes in disparity-through-time using a homogenous or hetergenous model (see list in model.test) or (ii) an object of class dispRity returned from model.test function. If a dispRity object is supplied, all remaining arguments apart from sim and model.rank and alternative are ignored as the model specified by the input model is used.

model.rank

If a dispRity object is supplied, which model is used for simulation. The rank refers to the order of models as specified by AICc, so if model.rank = 1 (default) the best-fitting model is used for simulation.

alternative

If the simulation is based on a dispRity object, what is the alternative hypothesis: can be "two-sided" (default), "greater" or "lesser".

time.split

The age of the change in mode. The age is measured as the time before the most recent sample, and multiple ages can be supplied in a vector. Note this only applies to heterogenous models.

time.span

The length of the sequence (numeric). If one number is supplied this is treated as the length of the sequence and the time span is treated as sequence from 0 to time.span in unit increments. If a vector of length > 1 is supplied, this is treated as the the age of each sample in the sequence.

variance

The variance of each sample (numeric). If one number is supplied this is the variance for all samples in the sequence. If a vector of equal length to the time.span vector is supplied, this is used for the variance of each sample in the sequence

sample.size

The sample size of each sample (numeric). If one number is supplied this is the sample size for all samples in the sequence. If a vector of equal length to the time.span vector is supplied, this is used for the sample size of each sample in the sequence

parameters

A list of model parameters used for simulations. See details.

fixed.optima

A logical value, whether to use an estimated optimum value in OU models (FALSE - default), or whether to set the OU optimum to the ancestral value (TRUE).

Details

DISCLAIMER: this function is working properly (i.e. it does what it is supposed to do), however, the interpretation of the results has not yet been thought through, discussed and peer-reviewed (what does a Brownian motion like disparity curve means biologically?).

The parameters is a list of arguments to be passed to the models. These arguments can be:

  • ancestral.state, ancestral value of the disparity applicable to all models (default = 0.01).

  • sigma.squared, rate of step variance to all models except Stasis (default = 1).

  • alpha, strength of attraction to the optimum in OU models (default = 1).

  • optima.1, the value of the optimum in a OU model, or the first bin optimum in a multi-OU model (default = 0.15).

  • optima.2, the second bin optimum in a multi-OU model (default = 0.15).

  • optima.3, the third bin optimum in a multi-OU model (default = 0.15).

  • theta.1, the mean in a Stasis model, or the first bin mean in a multi-Stasis model (default = 1).

  • theta.2, the second bin optimum in a multi-OU model (default = 1).

  • theta.3, the third bin optimum in a multi-OU model (default = 1).

  • omega, the variance in a Stasis model (default = 1).

  • trend, the trend parameter in the Trend model (default = 0.5).

  • eb.rate, the rate of exponential rate decrease in the EB model (default = -0.1).

Value

A list of class dispRity and model.sim. Each list element contains the simulated central tendency, as well as the variance, sample size, and subsets used to simulate the data.

Author(s)

Mark N Puttick and Thomas Guillerme

References

Blomberg SP, Garland T Jr, & Ives AR. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution. 57, 717-745.

Hansen TF. 1997. Stabilizing selection and the comparative analysis of adaptation. Evolution. 51, 1341-1351.

Harmon LJ, et al. 2010. Early bursts of body size and shape evolution are rare in comparative data. 64, 2385-2396.

Hunt G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology. 32, 578-601. DOI: 10.1666/05070.1.

Hunt G, Hopkins MJ & Lidgard S. 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. Proceedings of the National Academy of Sciences. 112, 4885-4890. DOI: 10.1073/pnas.1403662111

Felsenstein J. 1973. Maximum-likelihood estimation of evolutionary trees from continuous characters. American Journal of Human Genetics. 25, 471-492.

Felsenstein J. 1985. Phylogenies and the comparative method. The American Naturalist. 51, 1-15.

Murrell DJ. 2018. A global envelope test to detect non-random bursts of trait evolution. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13006

Citation for the envelope code:

See Also

model.test, model.test.wrapper, summary.dispRity and plot.dispRity

Examples

## Not run: 
## Disparity through time data
data(BeckLee_disparity)

## List of models to test
models <- list("Trend", "BM")

## Testing the models on the observed disparity
model_test_output <- model.test(BeckLee_disparity, models, time.split = 66)
 
## simulations using the output from model.test
model_test_sim_output <- model.test.sim(sim = 100, model= model_test_output)
 
## Plot the simulated best model
plot(model_test_sim_output)
## Add the observed data
plot(BeckLee_disparity, add = TRUE, col = c("pink", "#ff000050", "#ff000050"))

## Simulating a specific model with specific parameters parameters
model_simulation <- model.test.sim(sim = 100, model = "BM", time.span = 120, variance = 0.1,
                                   sample.size = 100, parameters = list(ancestral.state = 0,
                                   sigma.squared = 0.1))

## Summarising the results
plot(model_simulation, main = "A simple Brownian motion")

## End(Not run)


dispRity documentation built on May 29, 2024, 9:40 a.m.