Nothing
within.param.loop <-
function (net, reps = 1, ays = 0.00, nus = 1/7, transs = 2/14,
thetas = 0.0, mus = 0.001, verbose = 1, conv=.1,
eff=.1, inoc.size=100, df.out = 1, ...)
{
mean.degree <- mean(degree(net, cmode = "indegree"))
results <- list()
y <- 0
counter <- 0
num.sims <- reps * length(ays) * length(nus) * length(transs) *
length(thetas) * length(mus) * length(conv) * length(eff) * length(inoc.size)
A <- data.frame(a = double(num.sims), nu = double(num.sims),
trans = double(num.sims), theta = double(num.sims), mu = double(num.sims),
es = double(num.sims), length = double(num.sims), max.D = double(num.sims),
mean.D = double(num.sims), var.D = double(num.sims),
simp.recip = double(num.sims), conv=double(num.sims), eff=double(num.sims),
inoc=double(num.sims), time.half.peak=double(num.sims), time.peak=double(num.sims),
time.half.post.peak=double(num.sims), time.inoc.post.peak=double(num.sims),
num.inf.half.peak=integer(num.sims), num.inf.peak=integer(num.sims),
num.inf.half.post.peak=integer(num.sims), num.inf.inoc.post.peak=integer(num.sims),
frac.ecp.half.peak=double(num.sims), frac.ecp.peak=double(num.sims),
frac.ecp.post.peak=double(num.sims), frac.ecp.inoc.post.peak=double(num.sims),
total.ecp=integer(num.sims))
for (i in 1:length(ays)) {
a <- ays[i]
for (j in 1:length(nus)) {
nu <- nus[j]
for (k in 1:length(transs)) {
trans <- transs[k]
if (trans >= 1)
trans <- 0.99
for (l in 1:length(thetas)) {
theta <- thetas[l]
for (m in 1:length(mus)) {
mu <- mus[m]
if (verbose)
print(paste("a:", a, " nu:", nu, " trans:~",
round(trans, 2), " theta:", theta, " mu:",
mu))
counter = counter + 1
# results[[counter]] <- within.epi.sim.ssa(net, reps = reps,
# trans = trans, max.steps = 1e+05, infectious.period = 1/nu,
# mutation.rate = mu, verbose = 0, immune.decay = a,
# initial.susceptibility = (1 - theta), inoc.size=inoc.size,
# eff=eff, conv=conv)
results[[counter]] <- within.epi.sim.ssa(net=net, trans=trans, mu=mu, eff=eff, conv=conv,
reps=reps, infectious.period=1/nu, inoc.size=inoc.size,... )
if (df.out) {
for (r in 1:counter) {
for (p in 1:reps) {
if (reps > 1) {
g <- results[[r]][[p]]
}
else {
g <- results[[r]]
}
row.num <- y + r + (p - 1)
inoc <- sum(na.omit(g %v% "time_infected" == 0))
mts <- make.multi.phy.time.series(g)
es <- sum(g %v% "status" %in% "recovered")
A[row.num, ] <- c(a, nu, trans, theta,
mu, es,
max(na.omit(g %v% "time_recovered")),
max(na.omit(g %v% "infection_history")),
mean(na.omit(g %v% "infection_history")),
var(na.omit(g %v% "infection_history")),
1/net.simpsons.index(g),
g %n% "conversion_factor",
g %n% "efficiency",
inoc,
mts.cruncher(mts, inoc),
sum(na.omit(g %v% "phylo_id" != 1))/es
)
}
}
y <- y + (p - 1) + r
counter = 0
}
}
}
}
}
}
print(paste("num sims:", num.sims))
if (df.out) {
A
}
else results
}
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