as.paleoTS {paleoTS} | R Documentation |
A class for storing information on evolutionary time-series data.
as.paleoTS(mm, vv, nn, tt, MM = NULL, genpars = NULL, label = "") read.paleoTS(file=NULL, hh=FALSE, oldest="first", ...)
mm |
vector of sample means |
vv |
vector of sample variances |
nn |
vector of sample sizes |
tt |
vector of sample ages |
MM |
vector of true population means (for simulated time series) |
genpars |
generating parameters (for simulated time series) |
label |
optional label describing time series data |
file |
file name with time series data, by default uses file.choose() |
hh |
logical indicating if file has a header row |
oldest |
value indicating temporal order of samples (see below) |
... |
further arguments to read.table |
Function as.paleoTS()
combines time series data into an object of class paleoTS
. This function will usually not be used directly; read.paleoTS()
is used to get the relevant data from text files, and sim.rw()
and sim.stasis()
are used to simulate paleontological time-series from generating parameters.
If no file name is given for read.paleoTS
, the user will be prompted to select a file using the file.choose()
interactive prompt. Samples are assumed to be ordered from oldest to youngest (if not, use oldest="last"
), with ages indicating time elapsed from the beginning of the sequence, rather than geological age before the present. If sample ages decrease through the sequence, as if given in millions of years ago, tt
will automatically be converted to time elapsed from the beginning of the sequence.
mm |
vector of sample means |
vv |
vector of sample variances |
nn |
vector of sample sizes |
tt |
vector of sample ages |
MM |
vector of true population means (for simulated time series) |
genpars |
generating parameters (for simulated time series) |
label |
optional label describing time series data |
Gene Hunt
Hunt, G. 2007. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology, in press.
## create paleoTS object y <- as.paleoTS(mm=rnorm(10), vv=rep(1,10), nn=rep(25,10), tt=1:10, label="white noise time series") plot(y)