paleobuddy (version 1.1.0)

make.rate: Create a flexible rate for birth-death or sampling simulations

Description

Generates a function determining the variation of a rate (speciation, extinction, sampling) with respect to time. To be used on birth-death or sampling functions, it takes as the base rate (1) a constant, (2) a function of time, (3) a function of time and a time-series (usually an environmental variable), or (4) a vector of numbers describing rates as a step function. Requires information regarding the maximum simulation time, and allows for optional extra parameters to tweak the baseline rate.

Usage

make.rate(rate, tMax = NULL, envRate = NULL, rateShifts = NULL)

Value

A constant or time-varying function (depending on input) that can then be used as a rate in the other paleobuddy functions.

Arguments

rate

The baseline function with which to make the rate. It can be a

A number

For constant birth-death rates.

A function of time

For rates that vary with time. Note that this can be any function of time.

A function of time and an environmental variable

For rates varying with time and an environmental variable, such as temperature. Note that supplying a function on more than one variable without an accompanying envRate will result in an error.

A numeric vector

To create step function rates. Note this must be accompanied by a corresponding vector of rate shift times, rateShifts.

tMax

Ending time of simulation, in million years after the clade's origin. Needed to ensure rateShifts runs the correct way.

envRate

A data.frame representing a time-series, usually an environmental variable (e.g. CO2, temperature, etc) varying with time. The first column of this data.frame must be time, and the second column must be the values of the variable. The function will return an error if the user supplies envRate without rate being a function of two variables. paleobuddy has two environmental data frames, temp and co2. One can check RPANDA for more examples.

Note that, since simulation functions are run in forward-time (i.e. with 0 being the origin time of the simulation), the time column of envRate is assumed to do so as well, so that the row corresponding to t = 0 is assumed to be the value of the time-series when the simulation starts, and t = tMax is assumed to be its value when the simulation ends (the present).

Acknowledgements: The strategy to transform a function of t and envRate into a function of t only using envRate was adapted from RPANDA.

rateShifts

A vector indicating the time of rate shifts in a step function. The first element must be the first or last time point for the simulation, i.e. 0 or tMax. Since functions in paleobuddy run from 0 to tMax, if rateShifts runs from past to present (meaning rateShifts[2] < rateShifts[1]), we take tMax - rateShifts as the shifts vector. Note that supplying rateShifts when rate is not a numeric vector of the same length will result in an error.

Author

Bruno do Rosario Petrucci

References

Morlon H. et al (2016) RPANDA: an R package for macroevolutionary analyses on phylogenetic trees. Methods in Ecology and Evolution 7: 589-597.

Examples

# first we need a time vector to use on plots
time <- seq(0, 50, 0.1)

###
# we can have a step function rate

# vector of rates
rate <- c(0.1, 0.2, 0.3, 0.2)

# vector of rate shifts
rateShifts <- c(0, 10, 20, 35)
# this could be c(50, 40, 30, 15) for equivalent results

# make the rate
r <- make.rate(rate, tMax = 50, rateShifts = rateShifts)

# plot it
plot(time, rev(r(time)),type = 'l', xlim = c(max(time), min(time)))

# note that this method of generating a step function rate is slower to
# numerically integrate

# it is also not possible a rate and a shifts vector and a time-series 
# dependency, so in cases where one looks to run many simulations, or have a
# step function modified by an environmental variable, consider 
# using ifelse() (see below)

###
# we can have an environmental variable (or any time-series)

# temperature data
data(temp)

# function
rate <- function(t, env) {
  return(0.05*env)
}

# make the rate
r <- make.rate(rate, envRate = temp)

# plot it
plot(time, rev(r(time)), type = 'l', xlim = c(max(time), min(time)))

###
# we can have a rate that depends on time AND temperature

# temperature data
data(temp)

# function
rate <- function(t, env) {
  return(0.001*exp(0.1*t) + 0.05*env)
}

# make a rate
r <- make.rate(rate, envRate = temp)

# plot it
plot(time, rev(r(time)), type = 'l', xlim = c(max(time), min(time)))

###
# as mentioned above, we could also use ifelse() to 
# construct a step function that is modulated by temperature

# temperature data
data(temp)

# function
rate <- function(t, env) {
  return(ifelse(t < 10, 0.1 + 0.01*env,
                ifelse(t < 30, 0.2 - 0.005*env,
                       ifelse(t <= 50, 0.1 + 0.005*env, 0))))
}

# rate
r <- make.rate(rate, envRate = temp)

# plot it
plot(time, rev(r(time)), type = 'l', xlim = c(max(time), min(time)))

# while using ifelse() to construct a step function is more
# cumbersome, it leads to much faster numerical integration,
# so in cases where the method above is proving too slow,
# consider using ifelse() even if there is no time-series dependence

###
# make.rate will leave some types of functions unaltered

# constant rates
r <- make.rate(0.5)

# plot it
plot(time, rep(r, length(time)), type = 'l', 
     xlim = c(max(time), min(time)))

###
# linear rates

# function
rate <- function(t) {
  return(0.01*t)
}

# create rate
r <- make.rate(rate)

# plot it
plot(time, rev(r(time)), type = 'l', xlim = c(max(time), min(time)))

###
# any time-varying function, really

# function
rate <- function(t) {
  return(abs(sin(t))*0.1 + 0.05)
}

# create rate
r <- make.rate(rate)

# plot it
plot(time, r(time), type = 'l')