analogue (version 0.10-0)

timetrack: Timetracks of change in species composition

Description

Project passive (e.g. sediment core) samples into an ordination of a set of training samples.

Usage

timetrack(X, passive, env, method = c("cca", "rda"),
          transform = "none", formula, scaling = 3,
          rank = "full", model = c("CCA", "CA"), ...)
## S3 method for class 'timetrack':
fitted(object, type = c("passive", "ordination"),
       model = NULL, ...)
## S3 method for class 'timetrack':
plot(x, choices = 1:2, pch = c(1,2),
     col = c("black","red"), ...)

Arguments

X

matrix-like object containing the training set or reference samples.

passive

matrix-like object containing the samples to be projected into the ordination of X. Usually a set of sediment core samples.

env

optional vector or matrix of environmental or constraining variables. If provided, a constrained ordination of X is performed.

method

character, resolving to an ordination function available in vegan. Currently only "cca", the default, and "rda" are supported.

transform

character; the name of the transformation to apply to both X and passive. The transformations are performed using tran and valid options are given by that function's method argument.

formula

a model formula; if provided, it defines the model formula for the ordination function and is supplied as argument formula to the ordination function.

scaling

numeric; the ordination scaling to apply. Useful options are likely to be 1 or 3 where the focus is on the samples.

rank

character; see argument of same name in function cca or rda.

model

character; see argument of same name in function cca or rda.

object, x

an object of class "timetrack".

type

character; which fitted values should be returned?

choices

numeric; the length-2 vector of ordination axes to plot.

pch

The length-2 vector of plotting characters. The first element is used for the ordination samples, the second for the passive samples.

col

The length-2 vector of plotting colours. The first element is used for the ordination samples, the second for the passive samples.

...

arguments passed to other methods. timetrack passes arguments on to tran and the ordination function given in method. fitted passes arguments on to other fitted methods as ap

Value

• The plot method results in a plot on the currently active device, whilst the fitted method returns the matrix of fitted locations on the set of ordination axes.

  timetrack returns an object of class "timetrack", a list with the following components:

• ordination
  the ordination object, the result of the call to the function of the name method.
• fitted.values
  the matrix of fitted locations for the passive samples on the ordination axes.
• method
  the ordination function used.
• formula
  if supplied, the model formula used to define the ordination model.
• scaling
  the ordination scaling applied.
• rank
  The rank or the number of axes used in the approximation. The default is to use all axes (full rank) of the "model".
• model
  Show constrained ("CCA") or unconstrained ("CA") results.
• labels
  a list of names for the X, passive, and env arguments.
• call
  The matched function call.

Details

The timetrack is a way to visualise changes in species composition from sediment core samples within an underlying reference ordination or, usually, training set samples. This technique has been most often applied in situations where the underlying ordination is a constrained ordination and thence the timetrack of sediment core samples within the ordination reflects both the change in species composition and the indicative changes in the constraining variables.

The sediment core samples are projected passively into the underlying ordination. By projected passively, the locations of the core samples are predicted on the basis of the ordination species scores. A common set of species (columns) is required to passively place the sediment samples into the ordination. To achieve this, the left outer join of the species compositions of the training set and passive set is determined; the left outer join results in the passive data matrix having the same set of species (variables; columns) as the training set. Any training set species not in the passive set are added to the passive set with abundance 0. Any passive species not in the training set are removed from the passive set.

See Also

cca and rda for the underlying ordination functions.

Examples

## load the RLGH and SWAP data sets
data(rlgh, swapdiat)

## Fit the timetrack ordination
mod <- timetrack(swapdiat, rlgh, transform = "hellinger",
                 method = "rda")
mod

## Plot the timetrack
plot(mod)