Simplify examples

man/make.bisse.Rd

@@ -197,105 +197,6 @@ plot(h, phy)
 
 lik <- make.bisse(phy, phy$tip.state)
 lik(pars) # -159.71
-
-## Heuristic guess at a starting point, based on the constant-rate
-## birth-death model (uses \link{make.bd}).
-p <- starting.point.bisse(phy)
-
-\dontrun{
-## Start an ML search from this point.  This takes some time (~7s)
-fit <- find.mle(lik, p, method="subplex")
-logLik(fit) # -158.6875
-
-## The estimated parameters aren't too far away from the real ones, even
-## with such a small tree
-rbind(real=pars,
-      estimated=round(coef(fit), 2))
-
-## Test a constrained model where the speciation rates are set equal
-## (takes ~4s).
-lik.l <- constrain(lik, lambda1 ~ lambda0)
-fit.l <- find.mle(lik.l, p[-1], method="subplex")
-logLik(fit.l) # -158.7357
-
-## Despite the difference in the estimated parameters, there is no
-## statistical support for this difference:
-anova(fit, equal.lambda=fit.l)
-
-## Run an MCMC.  Because we are fitting six parameters to a tree with
-## only 50 species, priors will be needed.  I will use an exponential
-## prior with rate 1/(2r), where r is the character independent
-## diversificiation rate:
-prior <- make.prior.exponential(1 / (2 * (p[1] - p[3])))
-
-## This takes quite a while to run, so is not run by default
-tmp <- mcmc(lik, fit$par, nsteps=100, prior=prior, w=.1, print.every=0)
-
-w <- diff(sapply(tmp[2:7], range))
-samples <- mcmc(lik, fit$par, nsteps=1000, prior=prior, w=w,
-                print.every=100)
-
-## See \link{profiles.plot} for more information on plotting these
-## profiles.
-col <- c("blue", "red")
-profiles.plot(samples[c("lambda0", "lambda1")], col.line=col, las=1,
-              xlab="Speciation rate", legend="topright")
-}
-
-## BiSSE reduces to the birth-death model and Mk2 when diversification
-## is state independent (i.e., lambda0 ~ lambda1 and mu0 ~ mu1).
-lik.mk2 <- make.mk2(phy, phy$tip.state)
-lik.bd <- make.bd(phy)
-
-## 1. BiSSE / Birth-Death
-## Set the q01 and q10 parameters to arbitrary numbers (need not be
-## symmetric), and constrain the lambdas and mus to be the same for each
-## state.  The likelihood function now has just two parameters and
-## will be proprtional to Nee's birth-death based likelihood:
-lik.bisse.bd <- constrain(lik,
-                          lambda1 ~ lambda0, mu1 ~ mu0,
-                          q01 ~ .01, q10 ~ .02)
-pars <- c(.1, .03)
-## These differ by -167.3861 for both parameter sets:
-lik.bisse.bd(pars)   - lik.bd(pars)
-lik.bisse.bd(2*pars) - lik.bd(2*pars)
-
-## 2. BiSSE / Mk2
-## Same idea as above: set all diversification parameters to arbitrary
-## values (but symmetric this time):
-lik.bisse.mk2 <- constrain(lik,
-                           lambda0 ~ .1, lambda1 ~ .1,
-                           mu0 ~ .03, mu1 ~ .03)
-## Differ by -150.4740 for both parameter sets.
-lik.bisse.mk2(pars)   - lik.mk2(pars)
-lik.bisse.mk2(2*pars) - lik.mk2(2*pars)
-
-## 3. Sampled BiSSE / Birth-Death
-## Pretend that the tree is only .6 sampled:
-lik.bd2 <- make.bd(phy, sampling.f=.6)
-lik.bisse2 <- make.bisse(phy, phy$tip.state, sampling.f=c(.6, .6))
-lik.bisse2.bd <- constrain(lik.bisse2,
-                           lambda1 ~ lambda0, mu1 ~ mu0,
-                           q01 ~ .01, q10 ~ .01)
-
-## Difference of -167.2876
-lik.bisse2.bd(pars)   - lik.bd2(pars)
-lik.bisse2.bd(2*pars) - lik.bd2(2*pars)
-
-## 4. Unresolved clade BiSSE / Birth-Death
-unresolved <- data.frame(tip.label=I(c("sp25", "sp30", "sp40", "sp56", "sp20")),
-                         Nc =c(10, 9, 6, 5, 2),
-                         n0=0, n1=0)
-unresolved.bd <- structure(unresolved$Nc, names=unresolved$tip.label)
-lik.bisse3 <- make.bisse(phy, phy$tip.state, unresolved)
-lik.bisse3.bd <- constrain(lik.bisse3,
-                           lambda1 ~ lambda0, mu1 ~ mu0,
-                           q01 ~ .01, q10 ~ .01)
-lik.bd3 <- make.bd(phy, unresolved=unresolved.bd)
-
-## Difference of -167.1523
-lik.bisse3.bd(pars) - lik.bd3(pars)
-lik.bisse3.bd(pars*2) - lik.bd3(pars*2)
 }
 
 \references{