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## mcmc.popsize.R (2013-07-19)
## Run reversible jump MCMC to sample demographic histories
## Copyright 2004-2013 Rainer Opgen-Rhein and Korbinian Strimmer
## Portions of this function are adapted from rjMCMC code by
## Karl W Broman (see http://www.biostat.wisc.edu/~kbroman/)
## This file is part of the R-package `ape'.
## See the file ../COPYING for licensing issues.
# public function
# run rjMCMC chain
if (getRversion() >= "2.15.1")
utils::globalVariables(c("loglik", "b.lin", "popsize"))
mcmc.popsize <-
function(tree, nstep, thinning = 1, burn.in = 0, progress.bar = TRUE,
method.prior.changepoints = c("hierarchical", "fixed.lambda"),
max.nodes = 30,
lambda = 0.5, # "fixed.lambda" method.prior.changepoints
gamma.shape = 0.5,
gamma.scale = 2, # gamma distribution from which lambda is drawn (for "hierarchical" method)
method.prior.heights = c("skyline", "constant", "custom"),
prior.height.mean, prior.height.var)
{
method.prior.changepoints <- match.arg(method.prior.changepoints)
method.prior.heights <- match.arg(method.prior.heights)
## Calculate skylineplot, coalescent intervals
## and estimated population sizes
if (inherits(tree, "phylo")) {
ci <- coalescent.intervals(tree)
sk1 <- skyline(ci)
} else if (inherits(tree, "coalescentIntervals")) {
ci <- tree
sk1 <- skyline(ci)
} else
stop("tree must be an object of class phylo or coalescentIntervals")
## consider possibility of more than one lineage
ci$lineages <- ci$lineages[sk1$interval.length > 0]
ci$interval.length <- ci$interval.length[sk1$interval.length > 0]
data <- sk1$time <- sk1$time[sk1$interval.length > 0]
sk1$population.size <- sk1$population.size[sk1$interval.length > 0]
sk1$interval.length <- sk1$interval.length[sk1$interval.length > 0]
## constant prior for heights
if (method.prior.heights == "constant") {
prior.height.mean <- function(position)
mean(sk1$population.size)
prior.height.var <- function(position)
(mean(sk1$population.size))^2
}
## skyline plot prior for heights
if (method.prior.heights == "skyline") {
TIME <- sk1$time
numb.interv <- 10
prior.change.times <- abs((0:numb.interv) * max(TIME)/numb.interv)
prior.height.mean.all <- prior.height.var.all <- vector(length = numb.interv)
for (p.int in 1:(numb.interv)) {
left <- p.int
right <- p.int + 1
sample.pop <- sk1$population.size[sk1$time >= prior.change.times[left] & sk1$time <= prior.change.times[right]]
while (length(sample.pop) < 10) {
if (left > 1) left <- left - 1
if (right < length(prior.change.times)) right <- right + 1
sample.pop <- sk1$population.size[sk1$time >= prior.change.times[left] & sk1$time <= prior.change.times[right]]
}
prior.height.mean.all[p.int] <- sum(sample.pop)/length(sample.pop)
prior.height.var.all[p.int] <- sum((sample.pop-prior.height.mean.all[p.int])^2)/(length(sample.pop) - 1)
}
prior.height.mean <- function(position) {
j <- sum(prior.change.times <= position)
if (j >= length(prior.height.mean.all)) j <- length(prior.height.mean.all)
prior.mean <- prior.height.mean.all[j]
prior.mean
}
prior.height.var <- function(position) {
j <- sum(prior.change.times <= position)
if (j >= length(prior.height.var.all)) j <- length(prior.height.var.all)
prior.var <- prior.height.var.all[j]
prior.var
}
}
if (method.prior.heights == "custom") {
if (missing(prior.height.mean) || missing(prior.height.var))
stop("custom priors not specified")
}
## set prior
prior <- vector(length = 4)
prior[4] <- max.nodes
## set initial position of markov chain and likelihood
pos <- c(0, max(data))
h <- c(rep(mean(sk1$population.size), 2))
b.lin <- choose(ci$lineages, 2)
## loglik <<- loglik.pop # modified by EP
## set lists for data
count.it <- floor((nstep - burn.in)/thinning)
save.pos <- save.h <- vector("list", count.it)
save.loglik <- 1:count.it
save.steptype <- 1:count.it
save.accept <- 1:count.it
## calculate jump probabilities for given lambda of the prior
if (method.prior.changepoints == "fixed.lambda") {
prior[1] <- lambda
jump.prob <- matrix(ncol = 4, nrow = prior[4] + 1)
p <- dpois(0:prior[4], prior[1])/ppois(prior[4] + 1, prior[1])
bk <- c(p[-1]/p[-length(p)], 0)
bk[bk > 1] <- 1
dk <- c(0, p[-length(p)]/p[-1])
dk[dk > 1] <- 1
mx <- max(bk + dk)
bk <- bk/mx*0.9
dk <- dk/mx*0.9
bk[is.na(bk)] <- 0 # added
dk[is.na(dk)] <- 0 # added
jump.prob[, 3] <- bk
jump.prob[, 4] <- dk
jump.prob[1, 2] <- 0
jump.prob[1, 1] <- 1 - bk[1] - dk[1]
jump.prob[-1, 1] <- jump.prob[-1, 2] <-
(1 - jump.prob[-1, 3] - jump.prob[-1, 4])/2
}
## calculate starting loglik
curloglik <- loglik.pop(data, pos, h, b.lin, sk1, ci)
count.i <- 1
## set progress bar
if (progress.bar == TRUE) {
dev.new(width = 3, height = 0.7)
par(mar = c(0.5, 0.5, 2, 0.5))
plot(x = c(0, 0), y = c(0, 1), type = "l", xlim = c(0, 1), ylim = c(0, 1),
main = "rjMCMC in progress", ylab = "", xlab = "", xaxs = "i",
yaxs = "i", xaxt = "n", yaxt = "n")
}
## BEGIN CALCULATION
for (i in (1:nstep + 1)) {
if (progress.bar == TRUE) {
if (i %% 100 == 0) {
z <- i/nstep
zt <- (i - 100)/(nstep)
polygon(c(zt, zt, z, z), c(1, 0, 0, 1), col = "black")
}
}
## calculate jump probabilities without given lamda
if (method.prior.changepoints == "hierarchical") {
prior[1] <- rgamma(1, shape = gamma.shape, scale = gamma.scale)
jump.prob <- matrix(ncol = 4, nrow = prior[4] + 1)
p <- dpois(0:prior[4], prior[1]) / ppois(prior[4] + 1, prior[1])
bk <- c(p[-1]/p[-length(p)], 0)
bk[bk > 1] <- 1
dk <- c(0, p[-length(p)]/p[-1])
dk[dk > 1] <- 1
mx <- max(bk + dk)
bk <- bk/mx*0.9
dk <- dk/mx*0.9
bk[is.na(bk)] <- 0 # added
dk[is.na(dk)] <- 0 # added
jump.prob[, 3] <- bk
jump.prob[, 4] <- dk
jump.prob[1, 2] <- 0
jump.prob[1, 1] <- 1 - bk[1] - dk[1]
jump.prob[-1, 1] <- jump.prob[-1, 2] <-
(1 - jump.prob[-1, 3] - jump.prob[-1, 4])/2
}
## determine what type of jump to make
wh <- sample(1:4, 1, prob = jump.prob[length(h)-1, ])
if (i %% thinning == 0 & i > burn.in) save.steptype[[count.i]] <- wh
if (wh == 1) {
step <- ht.move(data, pos, h, curloglik, prior, b.lin, sk1, ci, prior.height.mean, prior.height.var)
h <- step[[1]]
curloglik <- step[[2]]
if (i %% thinning == 0 & i > burn.in) {
save.pos[[count.i]] <- pos
save.h[[count.i]] <- h
save.loglik[[count.i]] <- step[[2]]
save.accept[[count.i]] <- step[[3]]
}
} else if (wh == 2) {
step <- pos.move(data, pos, h, curloglik, b.lin, sk1, ci)
pos <- step[[1]]
curloglik <- step[[2]]
if (i %% thinning == 0 & i > burn.in) {
save.pos[[count.i]] <- pos
save.h[[count.i]] <- h
save.loglik[[count.i]] <- step[[2]]
save.accept[[count.i]] <- step[[3]]
}
} else if (wh == 3) {
step <- birth.step(data, pos, h, curloglik, prior, jump.prob, b.lin, sk1, ci, prior.height.mean, prior.height.var)
pos <- step[[1]]
h <- step[[2]]
curloglik <- step[[3]]
if (i %% thinning == 0 & i > burn.in) {
save.pos[[count.i]] <- pos
save.h[[count.i]] <- h
save.loglik[[count.i]] <- step[[3]]
save.accept[[count.i]] <- step[[4]]
}
} else {
step <- death.step(data, pos, h, curloglik, prior, jump.prob, b.lin, sk1, ci, prior.height.mean, prior.height.var)
pos <- step[[1]]
h <- step[[2]]
curloglik <- step[[3]]
if (i %% thinning == 0 & i > burn.in) {
save.pos[[count.i]] <- pos
save.h[[count.i]] <- h
save.loglik[[count.i]] <- step[[3]]
save.accept[[count.i]] <- step[[4]]
}
}
if (i %% thinning == 0 & i > burn.in) count.i <- count.i + 1
}
if (progress.bar == TRUE) dev.off()
list(pos = save.pos, h = save.h, loglik = save.loglik,
steptype = save.steptype, accept = save.accept)
}
## private functions
ht.move <-
function(data, pos, h, curloglik, prior, b.lin, sk1, ci, prior.height.mean, prior.height.var)
{
j <- sample(1:length(h), 1)
prior.mean <- prior.height.mean(pos[j])
prior.var <- prior.height.var(pos[j])
prior[3] <- prior.mean/prior.var
prior[2] <- (prior.mean^2)/prior.var
newh <- h
newh[j] <- h[j] * exp(runif(1, -0.5, 0.5))
newloglik <- loglik.pop(data, pos, newh, b.lin, sk1, ci)
lr <- newloglik - curloglik
ratio <- exp(lr + prior[2] * (log(newh[j]) - log(h[j])) - prior[3] * (newh[j] - h[j]))
if (runif(1, 0, 1) < ratio)
return(list(newh, newloglik, 1))
else
return(list(h, curloglik, 0))
}
pos.move <- function(data, pos, h, curloglik, b.lin, sk1, ci)
{
j <- if (length(pos) == 3) 2 else sample(2:(length(pos)-1), 1)
newpos <- pos
left <- pos[j - 1]
right <- pos[j + 1]
newpos[j] <- runif(1, left, right)
newloglik <- loglik.pop(data, newpos, h, b.lin, sk1, ci)
lr <- newloglik - curloglik
ratio <- exp(lr) * (right - newpos[j])*(newpos[j]- left)/
(right - pos[j])/(pos[j] - left)
if (runif(1, 0, 1) < ratio)
return(list(newpos, newloglik, 1))
else
return(list(pos, curloglik, 0))
}
birth.step <-
function(data, pos, h, curloglik, prior, jump.prob, b.lin, sk1, ci, prior.height.mean, prior.height.var)
{
newpos <- runif(1, 0, pos[length(pos)])
j <- sum(pos < newpos)
left <- pos[j]
right <- pos[j + 1]
prior.mean <- prior.height.mean(pos[j])
prior.var <- prior.height.var(pos[j])
prior[3] <- prior.mean/prior.var
prior[2] <- (prior.mean^2)/prior.var
u <- runif(1, -0.5, 0.5)
oldh <- (((newpos - left)/(right - left))*(h[j + 1] - h[j]) + h[j])
newheight <- oldh*(1 + u)
## ratio
## recall that prior = (lambda, alpha, beta, maxk)
k <- length(pos) - 2
L <- max(pos)
prior.logratio <- log(prior[1]) - log(k+1) + log((2*k + 3)*(2*k + 2)) - 2*log(L) +
log(newpos - left) + log(right - newpos) - log(right - left) +
prior[2]*log(prior[3]) - lgamma(prior[2]) +
(prior[2] - 1) * log(newheight) + prior[3]*(newheight)
proposal.ratio <- jump.prob[k + 2, 4]*L/jump.prob[k + 1, 3]/(k + 1)
jacobian <- (((newpos - left)/(right - left))*(h[j + 1] - h[j])) + h[j]
## form new parameters
newpos <- sort(c(pos, newpos))
newh <- c(h[1:j], newheight, h[(j + 1):length(h)])
newloglik <- loglik.pop(data, newpos, newh, b.lin, sk1, ci)
lr <- newloglik - curloglik
ratio <- exp(lr + prior.logratio) * proposal.ratio * jacobian
if (runif(1, 0, 1) < ratio)
return(list(newpos, newh, newloglik, 1))
else
return(list(pos, h, curloglik, 0))
}
death.step <-
function(data, pos, h, curloglik, prior, jump.prob, b.lin, sk1, ci, prior.height.mean, prior.height.var)
{
## position to drop
if (length(pos) == 3) j <- 2
else j <- sample(2:(length(pos) - 1), 1)
left <- pos[j - 1]
right <- pos[j + 1]
prior.mean <- prior.height.mean(pos[j])
prior.var <- prior.height.var(pos[j])
prior[3] <- prior.mean/prior.var
prior[2] <- (prior.mean^2)/prior.var
## get new height
h.left <- h[j - 1]
h.right <- h[j + 1]
newheight <- (((pos[j] - left)/(right - left))*(h.right - h.left) + h.left)
## ratio
## recall that prior = (lambda, alpha, beta, maxk)
k <- length(pos) - 3
L <- max(pos)
prior.logratio <- log(k+1) - log(prior[1]) - log(2*(k + 1)*(2*k + 3)) + 2*log(L) -
log(pos[j] - left) - log(right - pos[j]) + log(right - left) -
prior[2]*log(prior[3]) + lgamma(prior[2]) -
(prior[2]-1) * log(newheight) - prior[3]*(newheight)
proposal.ratio <- (k + 1)*jump.prob[k + 1, 3]/jump.prob[k + 2, 4]/L
jacobian <- ((pos[j] - left)/(right - left))*(h[j + 1] - h[j - 1]) + h[j - 1]
## form new parameters
newpos <- pos[-j]
newh <- h[-j]
newloglik <- loglik.pop(data, newpos, newh, b.lin, sk1, ci)
lr <- newloglik - curloglik
ratio <- exp(lr + prior.logratio) * proposal.ratio * (jacobian^(-1))
if (runif(1, 0, 1) < ratio)
return(list(newpos, newh, newloglik, 1))
else
return(list(pos, h, curloglik, 0))
}
# calculate the log likelihood for a set of data
loglik.pop <-
function(time = sk1$time, pos = c(0, max(sk1$time)), h = mean(sk1$population.size), b = b.lin, sk1, ci)
{
data.time <- c(0, time)
leftside <- 0
i <- 1
h1 <- c(h, h[length(h)])
pos1 <- c(pos, pos[length(pos)])
while (i < length(time)) {
left.pos <- sum(data.time[i + 1] >= pos)
right.pos <- left.pos + 1
h.mix <- (((data.time[i + 1] - pos[left.pos])/(pos[right.pos] - pos[left.pos]))*(h[right.pos] - h[left.pos])) + h[left.pos]
leftside <- leftside + log(b[i]/h.mix)
i <- i + 1
}
rightside <- 0
time1 <- c(0, time)
time.count <- 1
## heigths of jumps
jumps <- sort(c(time1, pos))
h.jumps <- jumps
while (time.count <= length(jumps)) {
left.pos <- sum(jumps[time.count] >= pos)
right.pos <- left.pos + 1
h.jumps[time.count] <- (((jumps[time.count] - pos[left.pos])/(pos[right.pos] - pos[left.pos]))*(h[right.pos] - h[left.pos])) + h[left.pos]
if (is.na(h.jumps[time.count])) h.jumps[time.count] <- h[left.pos]
time.count <- time.count + 1
}
## Vector for lineages
i <- 1
lineages.jumps <- jumps
while (i <= length(jumps)) {
lineages.jumps[i] <- sum(jumps[i] >= time)
if (lineages.jumps[i] == 0) lineages.jumps[i] <- 1
i <- i + 1
}
lineage <- ci$lineages[lineages.jumps]
b1 <- choose(lineage, 2)
## Integral
a <- (h.jumps[-1] - h.jumps[-length(h.jumps)])/(jumps[-1] - jumps[-length(jumps)])
c <- h.jumps[-1] - jumps[-1] * a
area <- (1/a) * log(a*jumps[-1] + c) - (1/a)*log(a * jumps[-length(jumps)] + c)
stepfunction <- (jumps[-1] - jumps[-length(jumps)])/h.jumps[-1]
area[is.na(area)] <- stepfunction[is.na(area)]
rightside <- sum(area * b1[-1])
loglik <- leftside - rightside
loglik
}
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