Run a function on a treedata.table object
Value
Function output for a single tree (phylo) or a list of function outputs (one per each tree in the MultiPhylo object)
Details
This function allows R functions that use trees and data to be run
ontreedata.table objects.
Examples
data(anolis)
# \donttest{
# A treedata.table object with a phylo $phy
td <- as.treedata.table(anolis$phy, anolis$dat)
#> Tip labels detected in column: X
#> Phylo object detected
#> All tips from original tree/dataset were preserved
tdt(td, geiger::fitContinuous(phy, extractVector(td, "SVL"),
model = "BM", ncores = 1
))
#> Phylo object detected. Expect a single function output
#> GEIGER-fitted comparative model of continuous data
#> fitted ‘BM’ model parameters:
#> sigsq = 0.136160
#> z0 = 4.065918
#>
#> model summary:
#> log-likelihood = -4.700404
#> AIC = 13.400807
#> AICc = 13.524519
#> free parameters = 2
#>
#> Convergence diagnostics:
#> optimization iterations = 100
#> failed iterations = 0
#> number of iterations with same best fit = 100
#> frequency of best fit = 1.000
#>
#> object summary:
#> 'lik' -- likelihood function
#> 'bnd' -- bounds for likelihood search
#> 'res' -- optimization iteration summary
#> 'opt' -- maximum likelihood parameter estimates
# A treedata.table object with a multiPhylo $phy
treesFM <- list(anolis$phy, anolis$phy)
class(treesFM) <- "multiPhylo"
td <- as.treedata.table(treesFM, anolis$dat)
#> Tip labels detected in column: X
#> Multiphylo object detected
#> All tips from original tree/dataset were preserved
tdt(td, geiger::fitContinuous(phy, extractVector(td, "SVL"),
model = "BM",
ncores = 1
))
#> Multiphylo object detected. Expect a list of function outputs
#> [[1]]
#> GEIGER-fitted comparative model of continuous data
#> fitted ‘BM’ model parameters:
#> sigsq = 0.136160
#> z0 = 4.065918
#>
#> model summary:
#> log-likelihood = -4.700404
#> AIC = 13.400807
#> AICc = 13.524519
#> free parameters = 2
#>
#> Convergence diagnostics:
#> optimization iterations = 100
#> failed iterations = 0
#> number of iterations with same best fit = 100
#> frequency of best fit = 1.000
#>
#> object summary:
#> 'lik' -- likelihood function
#> 'bnd' -- bounds for likelihood search
#> 'res' -- optimization iteration summary
#> 'opt' -- maximum likelihood parameter estimates
#>
#> [[2]]
#> GEIGER-fitted comparative model of continuous data
#> fitted ‘BM’ model parameters:
#> sigsq = 0.136160
#> z0 = 4.065918
#>
#> model summary:
#> log-likelihood = -4.700404
#> AIC = 13.400807
#> AICc = 13.524519
#> free parameters = 2
#>
#> Convergence diagnostics:
#> optimization iterations = 100
#> failed iterations = 0
#> number of iterations with same best fit = 100
#> frequency of best fit = 1.000
#>
#> object summary:
#> 'lik' -- likelihood function
#> 'bnd' -- bounds for likelihood search
#> 'res' -- optimization iteration summary
#> 'opt' -- maximum likelihood parameter estimates
#>
# }