Fit the same simple model across multiple time-series
fitMult( yl, model = c("GRW", "URW", "Stasis", "covTrack"), method = c("Joint", "AD"), pool = TRUE, zl = NULL, hess = FALSE )
a list of
the model to fit; see Details
parameterization to use:
if TRUE, sample variances are substituted with their pooled estimate
if TRUE, standard errors computed from the Hessian matrix are returned
This function fits a model with shared parameters across multiple trait time-series. The most likely application would be to model a common evolutionary dynamic across different sequences, perhaps representing time-series of the same trait and lineage from different localities or time intervals.
Four simple models are currently implemented:
vstep of the general random walk are
shared across sequences.
vstep of the
unbiased random walk is shared across sequences.
omega of stasis is shared across sequences.
evar of the
covariate-tracking model are shared across sequences.
Under the joint parameterization,
method = "Joint", an additional parameter,
fit, representing the ancestral (starting) trait value. This parameter is estimated separately
in each sequence so it is not assumed that they all start at the same trait value.
paleoTSfit object with the results of the model-fitting
The models are described in the help for
fitSimple and the functions
linked from there.
x1 <- sim.GRW(ms = 1, vs = 0.2) x2 <- sim.GRW(ms = 1, vs = 0.2) fitMult(list(x1, x2), model = "GRW")
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