How long it takes to generate a set of trees?

For 10 trees?

library(dmea)
set.seed(128)
s = sim_phyl()
#plot(s$newick)
s2 = drop.fossil(s$newick)
s2 = phylo2p(s2)
wt = s2$t
n_trees = 100
pars = c(0.8,0.0175,0.1)
p = proc.time()
S = sim_srt(wt=wt, pars=pars, parallel=F, n_trees=n_trees)
print(proc.time()-p)
##    user  system elapsed 
##   5.876   0.016   5.893
p = proc.time()
pars = subplex(par = pars, fn = llik_st , setoftrees = S)
print(proc.time()-p)
##    user  system elapsed 
##   2.952   0.000   2.953

For 1000 trees?

library(dmea)
set.seed(128)
s = sim_phyl()
#plot(s$newick)
s2 = drop.fossil(s$newick)
s2 = phylo2p(s2)
wt = s2$t
n_trees = 1000
pars = c(0.8,0.0175,0.1)
p = proc.time()
S = sim_srt(wt=wt, pars=pars, parallel=F, n_trees=n_trees)
print(proc.time()-p)
##    user  system elapsed 
##  57.696   0.004  57.701
p = proc.time()
pars = subplex(par = pars, fn = llik_st , setoftrees = S)
print(proc.time()-p)
##    user  system elapsed 
##  32.612   0.000  32.616

For 1000 parallel?

library(dmea)
set.seed(128)
s = sim_phyl()
#plot(s$newick)
s2 = drop.fossil(s$newick)
s2 = phylo2p(s2)
wt = s2$t
n_trees = 1000
pars = c(0.8,0.0175,0.1)
p = proc.time()
S = sim_srt(wt=wt, pars=pars, parallel=T, n_trees=n_trees)
print(proc.time()-p)
##    user  system elapsed 
##   0.616   0.028  40.093
p = proc.time()
pars = subplex(par = pars, fn = llik_st , setoftrees = S)
print(proc.time()-p)
##    user  system elapsed 
##  29.084   0.000  29.086

For 10000 trees?

library(dmea)
set.seed(128)
s = sim_phyl()
#plot(s$newick)
s2 = drop.fossil(s$newick)
s2 = phylo2p(s2)
wt = s2$t
n_trees = 10000
pars = c(0.8,0.0175,0.1)
p = proc.time()
S = sim_srt(wt=wt, pars=pars, parallel=F, n_trees=n_trees)
print(proc.time()-p)
##    user  system elapsed 
## 571.352   0.060 571.430
p = proc.time()
pars = subplex(par = pars, fn = llik_st , setoftrees = S)
print(proc.time()-p)
##    user  system elapsed 
## 437.044   0.972 438.579

For 10000 parallel?

library(dmea)
set.seed(128)
s = sim_phyl()
#plot(s$newick)
s2 = drop.fossil(s$newick)
s2 = phylo2p(s2)
wt = s2$t
n_trees = 10000
pars = c(0.8,0.0175,0.1)
p = proc.time()
S = sim_srt(wt=wt, pars=pars, parallel=T, n_trees=n_trees)
print(proc.time()-p)
##    user  system elapsed 
##   5.952   0.132 387.399
p = proc.time()
pars = subplex(par = pars, fn = llik_st , setoftrees = S)
print(proc.time()-p)
##    user  system elapsed 
## 385.044   0.932 386.126

Computational times of normal simulation functions

n_it = 1
a = vector("list", n_it)
# diversity dependence model. parameters are par=c(lambda,mu,K)
time=proc.time()
for (i in 1:(n_it)){
  s = sim_phyl()
  a[[i]] = s$wt
}
t2 = proc.time()-time
paste(t2[3]/60,'min')
## [1] "0.00348333333333333 min"
n_it = 10
a = vector("list", n_it)
# diversity dependence model. parameters are par=c(lambda,mu,K)
time=proc.time()
for (i in 1:(n_it)){
  s = sim_phyl()
  a[[i]] = s$wt
}
t2 = proc.time()-time
paste(t2[3]/60,'min')
## [1] "0.03335 min"
n_it = 100
a = vector("list", n_it)
# diversity dependence model. parameters are par=c(lambda,mu,K)
time=proc.time()
for (i in 1:(n_it)){
  s = sim_phyl()
  a[[i]] = s$wt
}
t2 = proc.time()-time
paste(t2[3]/60,'min')
## [1] "0.315333333333333 min"