bd.time(phy, birth, death, BIRTH = NULL, DEATH = NULL, ip, lower, upper, fast = FALSE, boot = 0, trace = 0)
"phylo"
.birth
.death
.trace
steps. This can be helpful if convergence
is particularly slow.nlminb
.
yule.time
. The model is fitted by minimizing the least squares deviation between
the observed and the predicted distributions of branching times. These
computations rely heavily on numerical integrations. If fast =
FALSE
, integrations are done with R's integrate
function. If fast = TRUE
, a faster but less accurate function
provided in ape is used. If fitting a complex model to a large
phylogeny, a strategy might be to first use the latter option, and
then to use the estimates as starting values with fast = FALSE
.
ltt.plot
, birthdeath
,
yule.time
, LTT
set.seed(3)
tr <- rbdtree(0.1, 0.02)
bd.time(tr, 0, 0) # fits a simple BD model
bd.time(tr, 0, 0, ip = c(.1, .01)) # 'ip' is useful here
## the classic logistic:
birth.logis <- function(a, b) 1/(1 + exp(-a*t - b))
## Not run:
# bd.time(tr, birth.logis, 0, ip = c(0, -2, 0.01))
# ## slow to get:
# ## $par
# ## a b death
# ## -0.003486961 -1.995983179 0.016496454
# ##
# ## $SS
# ## [1] 20.73023
# ## End(Not run)
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