LTT {ape} | R Documentation |
This function draws the lineage-through time (LTT) plots predicted under a speciation-extinction model (aka birth-death model) with specified values of speciation and extinction rates (which may vary with time).
A prediction interval is plotted by default which requires to define a sample size (100 by default), and different curves can be combined.
LTT(birth = 0.1, death = 0, N = 100, Tmax = 50, PI = 95, scaled = TRUE, eps = 0.1, add = FALSE, backward = TRUE, ltt.style = list("black", 1, 1), pi.style = list("blue", 1, 2), ...)
birth |
the speciation rate, this may be either a numeric value
or a funtion of time (named |
death |
id. for the extinction rate. |
N |
the size of the tree. |
Tmax |
the age of the root of the tree. |
PI |
the percentage value of the prediction interval; set this value to 0 to not draw this interval. |
scaled |
a logical values specifying whether to scale the y-axis between 0 and 1. |
eps |
a numerical value giving the resolution of the time axis. |
add |
a logical values specifying whether to make a new plot (the default). |
backward |
a logical value: should the time axis be traced from the present (the default), or from the root of the tree? |
ltt.style |
a list with three elements giving the style of the
LTT curve with, respectively, the colour ( |
pi.style |
id. for the prediction interval. |
... |
arguments passed to |
For the moment, this works well when birth
and death
are
constant. Some improvements are under progress for time-dependent
rates (but see below for an example).
Emmanuel Paradis
Hallinan, N. (2012) The generalized time variable reconstructed birth–death process. Journal of Theoretical Biology, 300, 265–276.
Paradis, E. (2011) Time-dependent speciation and extinction from phylogenies: a least squares approach. Evolution, 65, 661–672.
Paradis, E. (2015) Random phylogenies and the distribution of branching times. Journal of Theoretical Biology, 387, 39–45.
### predicted LTT plot under a Yule model with lambda = 0.1 ### and 50 species after 50 units of time... LTT(N = 50) ### ... and with a birth-death model with the same rate of ### diversification (try with N = 500): LTT(0.2, 0.1, N = 50, PI = 0, add = TRUE, ltt.style = list("red", 2, 1)) ### predictions under different tree sizes: layout(matrix(1:4, 2, 2, byrow = TRUE)) for (N in c(50, 100, 500, 1000)) { LTT(0.2, 0.1, N = N) title(paste("N =", N)) } layout(1) ## Not run: ### speciation rate decreasing with time birth.logis <- function(t) 1/(1 + exp(0.02 * t + 4)) LTT(birth.logis) LTT(birth.logis, 0.05) LTT(birth.logis, 0.1) ## End(Not run)