Fit simple models of trait evolution
fitSimple(
y,
model = c("GRW", "URW", "Stasis", "StrictStasis", "OU", "ACDC", "covTrack"),
method = c("Joint", "AD", "SSM"),
pool = TRUE,
z = NULL,
hess = FALSE
)a paleoTSfit object with the model fitting results
a paleoTS object
the model to be fit, one of "GRW", "URW", "Stasis", "OU", "ACDC",
"covTrack"
parameterization to use: Joint, AD or SSM; see Details
if TRUE, sample variances are substituted with their pooled estimate
a vector of a covariate, used only for the "covTrack" model
if TRUE, standard errors computed from the Hessian matrix are returned
This is a convenience function that calls the specific individual
functions for each model and parameterization, such as opt.GRW and
opt.joint.GRW. The models that this function can fit are:
GRW: General Random Walk. Under this model, evolutionary
changes, or "steps" are drawn from a distribution with a mean of mstep
and variance of vstep. mstep determines directionality and
vstep determines volatility (Hunt, 2006).
URW:
Unbiased Random Walk. Same as GRW with mstep = 0, and thus evolution
is non-directional. For a URW, vstep is the rate parameter.
Stasis: This parameterization follows Sheets & Mitchell (2001), with
a constant mean theta and variance omega (equivalent to white
noise).
Strict Stasis: Same as Stasis with omega = 0,
indicating no real evolutionary differences; all observed variation is
sampling error (Hunt et al. 2015).
OU: Ornstein-Uhlenbeck
model (Hunt et al. 2008). This model is that of a population ascending a
nearby peak in the adaptive landscape. The optimal trait value is theta,
alpha indicates the strength of attraction to that peak (= strength of
stabilizing selection around theta), vstep measures the random walk component (from genetic drift) and anc is the trait value
at the start of the sequence.
ACDC: Accelerating or decelerating evolution
model (Blomberg et al. 2003). This model is that of a population undergoing a
random walk with a step variance that increases or decreases over time. The initial step variance is vstep0,
and the parameter r controls its rate of increase (if positive) or decrease (if negative) over time.
When r < 0, the is equivalent to the "Early burst" model of Harmon et al.
covTrack: Covariate-tracking (Hunt et al. 2010). The trait tracks
a covariate with slope b1, consistent with an adaptive response. evar is the
residual variance, and, under method = "Joint", b0 is the intercept of the
relationship between trait and covariate.
model.
Hunt, G. 2006. Fitting and comparing models of phyletic
evolution: random walks and beyond. Paleobiology 32(4): 578-601.
Hunt, G. 2008. Evolutionary patterns within fossil lineages: model-based
assessment of modes, rates, punctuations and process. p. 117-131 In
From Evolution to Geobiology: Research Questions Driving Paleontology at the
Start of a New Century. Bambach, R. and P. Kelley (Eds).
Hunt, G., M. A.
Bell and M. P. Travis. 2008. Evolution toward a new adaptive optimum:
phenotypic evolution in a fossil stickleback lineage. Evolution 62(3):
700-710.
Sheets, H. D., and C. Mitchell. 2010. Why the null matters:
statistical tests, random walks and evolution. Genetica 112–
113:105–125.
Blomberg, S. P., T. Garland, and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioural traits are more labile.
Evolution 57(4):717-745.
Harmon, L. J. et al. 2010. Early bursts of body size and shape evolution are rare in comparative data. Evolution 64(8):2385-2396.
opt.GRW, opt.joint.GRW,
opt.joint.OU, opt.covTrack
y <- sim.Stasis(ns = 20, omega = 2)
w1 <- fitSimple(y, model = "GRW")
w2 <- fitSimple(y, model = "URW")
w3 <- fitSimple(y, model = "Stasis")
compareModels(w1, w2, w3)
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