trait.process {treats}

Trait processes

Description

Different trait processes implemented in treats.

Usage

trait.process(x0, edge.length, ...)

Arguments

x0

The previous state. This can be a single value (unidimensional process) or more (multidimensional processes).

edge.length

The branch length (default must be 1). This is always a single value.

...

Any optional argument for the specific process (see details).

Details

The different trait processes implemented in treats are:

• BM.process A Brownian motion process (uni or multidimensional). This function is based on mvrnorm. This process can take following optional arguments:

  • Sigma a positive-definite symmetric matrix specifying the covariance matrix of the variables (default is diag(length(x0))).

  • ... any named additional argument to be passed to mvrnorm.

• discrete.process This process can take following optional arguments:

  • transitions a positive-definite squared transition matrix. If left missing, a 2 states equal rates matrix is used.

  Note that for this process, 0 corresponds to state 1, 1 corresponds to state 2, etc... The current version of this process does not allow other discrete traits notation (but future versions will!).

• OU.process A Ornstein-Uhlenbeck process (uni or multidimensional). This function is based on mvrnorm. This process can take following optional arguments:

  • Sigma the traits variance/covariance (default is diag(length(x0))).

  • alpha the alpha parameter (default = is 1).

  • optimum the theta parameter (default = is 0).

  • ... any named additional argument to be passed to mvrnorm.

• no.process An non-process unidimensional function. This function generates a trait value not depending on the branch length nor the previous state This process can take following optional arguments:

  • fun a random number function (default is rnorm).

  • ... any named additional argument to be passed to fun.

• multi.peak.process A Ornstein-Uhlenbeck process (uni or multidimensional) with multiple optimal values. This function is based on mvrnorm. This process can take following optional arguments:

  • Sigma the traits variance/covariance (default is diag(length(x0))).

  • alpha the alpha parameter (default = is 1).

  • peaks the multiple optimal values to be attracted to (default = is 0). This can be a numeric vector to be applied to all the values of x0 or a list of the same length as x0 for different multiple optimums for each x0.

  • ... any named additional argument to be passed to mvrnorm.

• repulsion.process An unidimensional Brownian Motion process that generates a trait value not overlapping with the other living taxa ancestral values. This function is based on rnorm. This process can take following optional arguments:

  • sd the normal distribution standard deviation.

  • repulsion the minimal distance requested between trait values.

  • max.try the maximum number of values to draw (if the repulsion value is to hard to achieve).

  • trait.values LEAVE AS NULL (it designates the trait value table from the birth death process and is handled internally by treats).

  • lineage LEAVE AS NULL (it designates the lineage object from the birth death process and is handled internally by treats).

  • trait LEAVE AS NULL (it which trait to use and is analysed an is handled internally by treats).

More details about the trait.process functions is explained in the treats manual: http://tguillerme.github.io/treats.

Value

Returns one or more "numeric" value(s).

Author(s)

Thomas Guillerme

See Also

treats make.traits

Examples

## NOTE: You can visualise most process by making them
## into a "treats" "traits" object using make.traits():

## The Brownian motion process
BM.process(x0 = 0)
plot(make.traits(process = BM.process))
## A covariance matrix between 3 traits
varcovar_matrix <- matrix(c(1/3,1/3,1/3,1/3,2/3,0,1/3,0,2/3), ncol = 3)
BM.process(x0 = c(0,0,0), Sigma = varcovar_matrix)

## The Ornstein-Uhlenbeck process
OU.process(x0 = 0)
plot(make.traits(process = OU.process))

## No process
no.process()
plot(make.traits(process = no.process))

## Multi peaks with peaks at the values 1, 5 and 10
multi.peak.process(peaks = c(1, 5, 10))
plot(make.traits(multi.peak.process, process.args = list(peaks = c(1, 5, 10))))

## Repulsion process
repulsion.process(x0 = 0, repulsion = 1)
plot(make.traits(repulsion.process, process.args = list(repulsion = 5)))

## Discrete trait process
## Generating a stepwise transition matrix for 3 states (with an overal random transition rate)
stepwise_matrix <- transition.matrix(type = "stepwise", states = 3)
## Generatin and plotting the the trait
plot(make.traits(discrete.process, process.args = list(transitions = stepwise_matrix)))