RPANDA (version 2.3)

likelihood_t_env: Likelihood of a dataset under environmental models of trait evolution.

Description

Computes the likelihood of a dataset under either the linear or exponential environmental model, or an user defined environmental model. This function is used internally by fit_t_env.

Usage

likelihood_t_env(phylo, data, model=c("EnvExp", "EnvLin"), ...)

Value

the log-likelihood value of the environmental model

Arguments

phylo

an object of class 'phylo' (see ape documentation)

data

a named vector of continuous data with names corresponding to phylo$tip.label

...

"param", "fun", "times", "mtot" and "error" arguments.

-param: a vector with the parameters used in the environmental function. The first value is sig2 and the second is beta.

-fun: a time contnuous function of an environmental variable (see e.g. ?fit_t_env)

-times: a vector of branching times starting at zero (e.g. max(branching.times(phylo))-branching.times(phylo))

-mtot: root age of the tree (e.g. max(branching.times(phylo)))

-error: a vector of standard error (se) for each species

If the "times" argument is not provided, the "phylo" object is used to compute it as well as "mtot".

Note that the argument "mu" can be used to specify the root state (e.g. when using an mcmc sampler)

model

model chosen to fit trait data, "EnvExp" is the exponential-environmental model, and "EnvLin" is the linear-environmental model. Otherwise, an user specified model can be provided.

Author

Julien Clavel

Details

the "fun" argument can be filled by an environmental dataframe.

References

Clavel, J. & Morlon, H., 2017. Accelerated body size evolution during cold climatic periods in the Cenozoic. Proceedings of the National Academy of Science, 114(16): 4183-4188.

See Also

fit_t_env

Examples

test = FALSE
if(test){
data(Cetacea)
data(InfTemp)
# \donttest{
# Simulate a trait with temperature dependence on the Cetacean tree
set.seed(123)

trait <- sim_t_env(Cetacea, param=c(0.1,-0.2), env_data=InfTemp, model="EnvExp", 
					root.value=0, step=0.001, plot=TRUE)
					
# Compute the likelihood 
likelihood_t_env(Cetacea, trait, param=c(0.1, 0), fun=InfTemp, model="EnvExp")

# Provide the times
brtime<-branching.times(Cetacea)
mtot<-max(brtime)
times<-mtot-brtime

likelihood_t_env(Cetacea,trait,param=c(0.1, 0), fun=InfTemp, 
                  times=times, mtot=mtot, model="EnvExp")

# Provide the environmental function rather than the dataset (faster if used recursively)
#require(pspline)
#spline_result <- sm.spline(InfTemp[,1],InfTemp[,2], df=50)
#env_func <- function(t){predict(spline_result,t)}
#t<-unique(InfTemp[,1])
# We build the interpolated smoothing spline function
#env_data<-splinefun(t,env_func(t))
  
#likelihood_t_env(Cetacea, trait, param=c(0.1, 0), fun=env_data, 
#                 times=times, mtot=mtot, model="EnvExp")
# }
	}