## Random data (following APE)
data(bird.orders)
set.seed(1)
x <- structure(rnorm(length(bird.orders$tip.label)),
names=bird.orders$tip.label)
## Not run:
# ## With the VCV approach
# fit1 <- find.mle(make.bm(bird.orders, x), .1)
#
# ## With the pruning calculations
# lik.pruning <- make.bm(bird.orders, x, control=list(method="pruning"))
# fit2 <- find.mle(lik.pruning, .1)
#
# ## All the same (need to drop the function from this though)
# all.equal(fit1[-7], fit2[-7])
#
# ## If this is the same as the estimates from Geiger, to within the
# ## tolerances expected for the calculation and optimisation:
# fit3 <- fitContinuous(bird.orders, x)
# all.equal(fit3$Trait1$lnl, fit1$lnLik)
# all.equal(fit3$Trait1$beta, fit1$par, check.attributes=FALSE)
# ## End(Not run)
Run the code above in your browser using DataCamp Workspace