calcCutOff: Calculate multiple-test cut-off

Description Usage Arguments Value Author(s) See Also Examples

View source: R/calcCutOff.R

Description

Calculate the log-likelihood, AIC, or AICc cut-off necessary for type-one error to reach acceptable levels

Usage

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calcCutOff(
  phy,
  n = 1000,
  mc.cores = 1,
  model,
  measure = "AICc",
  alpha.error = 0.05,
  ...
)

Arguments

phy

An object of class phylo (see ape).

n

Number of simulations

mc.cores

Number of cores for parallel processing for linux-type systems (not applicable to Windows)

model

Evolutionary model, typically "tm1", "tm2", or "timeSlice", which is used to test the empirical data

measure

Measure used to summarise the model. One of "lnL" (log-likelihood), "AIC", or "AICc"

alpha.error

Target for the desired type-one error rate for the model (default 0.05)

...

Arguments to be passed to transformPhylo.ML which should be identical to the model applied to empirical data

Value

The cut-off requred to produce an type-one error rate equal to quantile.cut.off (default = 0.05) when data are simulated under Brownian motion, and these data are analysed under the appropriate model.

Author(s)

Mark Puttick

See Also

transformPhylo.ML, transformPhylo.ll, transformPhylo, transformPhylo.MCMC

Examples

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data(anolis.tree)
set.seed(393)
# calculated necessary AICc cut-off to reduce type-one error to 5% 
# for a timeSlice model with a split at 30Ma (only 5 simulations used,
# it's recommend to use 1000 for analyses)
calcCutOff(anolis.tree, n=5, model="timeSlice", splitTime=30)

PuttickMacroevolution/motmot documentation built on June 5, 2020, 7 p.m.