fitMult: Fit the same simple model across multiple time-series

Description

Fit the same simple model across multiple time-series

Usage

fitMult(
  yl,
  model = c("GRW", "URW", "Stasis", "covTrack"),
  method = c("Joint", "AD"),
  pool = TRUE,
  zl = NULL,
  hess = FALSE
)

Value

a paleoTSfit object with the results of the model-fitting

Arguments

yl: a list of paleoTS objects
model: the model to fit; see Details
method: parameterization to use: Joint or AD
pool: if TRUE, sample variances are substituted with their pooled estimate
zl: for the covTrack model only, a list of covariate vectors, one each paleoTS object in yl
hess: if TRUE, standard errors computed from the Hessian matrix are returned

Details

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:

GRW: parameters mstep and vstep of the general random walk are shared across sequences.
URW: parameter vstep of the unbiased random walk is shared across sequences.
Stasis: parameter omega of stasis is shared across sequences.
covTrack: parameters b0, b1, and evar of the covariate-tracking model are shared across sequences.

Under the joint parameterization, method = "Joint", an additional parameter, anc is 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.

Examples

Run this code

x1 <- sim.GRW(ms = 1, vs = 0.2)
x2 <- sim.GRW(ms = 1, vs = 0.2)
fitMult(list(x1, x2), model = "GRW")