View source: R/complexModels.R
bootSimpleComplex | R Documentation |
Bootstrap test to see if a complex model is significantly better than a simple model.
bootSimpleComplex(
y,
simpleFit,
complexFit,
nboot = 99,
minb = 7,
ret.full.distribution = FALSE,
parallel = FALSE,
...
)
y |
a |
simpleFit |
a |
complexFit |
a |
nboot |
number of replications for parametric bootstrapping |
minb |
minimum number of populations within each segment |
ret.full.distribution |
logical, indicating if the null distribution for the likelihood ratio from the parametric bootstrap should be returned |
parallel |
logical, if TRUE, the bootstrapping is done using parallel computing |
... |
further arguments, passed to optimization functions |
Simulations suggest that AICc can be overly liberal with complex models with mode shifts or punctuations (Hunt et al., 2015). This function implements an alternative of parametric boostrapping to compare the fit of a simple model with a complex model. It proceeds in five steps:
Compute the observed gain in support from the simple to complex model
as the likelihood ratio, LR_{obs} = -2(logL_{simple} - logL_{complex})
Simulate trait evolution under the specified simple model nboot
times
Fit to each simulated sequence the specified simple and complex models
Measure the gain in support from simple to complex as the bootstrap likelihood ratio for each simulated sequence
Compute the P-value as the percentile of the bootstrap distribution corresponding to the observed LR.
Argument simpleFit
should be a paleoTS
object returned by the
function fitSimple
or similar functions (e.g., opt.joint.GRW,
opt.GRW
, etc.). Argument complexFit
must be a paleoTS
object
returned by fitGpunc
or fitModeShift
.
Calculations can be speeded up by setting parallel = TRUE
, which uses
package doParallel
to run the bootstrap replicates in parallel, using
one fewer than the number of detected cores.
A list of the observed likelihood ratio statistic, LRobs
, the
P-value of the test, and the number of bootstrap replicates. If
ret.full.distribution = TRUE
, the null distribution of likelihood
ratios generated by parametric bootstrapping is also returned.
Hunt, G., M. J. Hopkins and S. Lidgard. 2015. Simple versus complex models of trait evolution and stasis as a response to environmental change. PNAS 112(16): 4885-4890.
sim.Stasis.RW
, fitModeShift
## Not run:
x <- sim.Stasis.RW(ns = c(15, 15), omega = 0.5, ms = 1, order = "Stasis-RW")
ws <- fitSimple(x)
wc <- fitModeShift(x, order = "Stasis-RW", rw.model = "GRW")
bootSimpleComplex(x, ws, wc, nboot = 50, minb = 7) # nboot too low for real analysis!
## End(Not run)
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