Description Usage Arguments Value Author(s) References See Also Examples
This function compares two evolutionary hypotheses via phylogenetic simulation under the null model (model1
) and alternative model (model2
) (Boettiger et al. 2012; Goolsby 2016). Typically the alternative model is nested within the null model, but is not a requirement of this testing procedure. By default, data are randomly simulated under model1 and model2, and model1 and model2 are fit to generate a distribution of likelihood ratios for data simulated under the null hypothesis and a distribution of likelihood ratios from data simulated under the alternative hypothesis. Comparison of the observed likelihood ratio to the null distribution provides an estimate of the P-value, whereas comparison of the alternative distribution to the critical value of the test statistic provides an estimate of statistical power.
1 2 3 |
model1 |
The null model (an object of class |
model2 |
The alternative model (an object of class |
nsim |
The number of iterations for phylogenetic simulation. |
plot |
Whether or not to plot the null and alternative distributions of likelihood ratios. |
estimate_power |
Whether to simulate the alternative distribution (default=TRUE). |
parallel |
Whether to use parallel processing to speed up computations (default=TRUE). |
conf.int |
Whether to estimate confidence inervals for tree transformation parameters in the alternative model (e.g., OU, EB, lambda, kappa, delta). If TRUE and the alternative evolutionary model is not 'BM', estimate_power is automatically set to TRUE. |
An object of class compare.models
.
Eric W. Goolsby
Boettiger C., Coop G., Ralph P. 2012. Is your phylogeny informative? Measuring the power of comparative methods. Evolution. 66:2240-2251.
Golsby E.W. 2015. Likelihood-Based Parameter Estimation for High-Dimensional Phylogenetic Comparative Models: Overcoming the Limitations of 'Distance-Based' Methods. In review.
1 2 3 4 5 6 7 8 9 | rand.data <- sim.traits()
X <- rowMeans(rand.data$trait_data)
null.model <- evo.model(tree = rand.data$tree,
Y = rand.data$trait_data,method = "Pairwise ML")
alt.model <- evo.model(tree = rand.data$tree,
Y = rand.data$trait_data,fixed.effects = X,method = "Pairwise ML")
compare.models(model1 = null.model,model2 = alt.model,
nsim = 100,parallel = FALSE)
|
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