#' Classic Skyline Estimates
#'
#' Classic Skyline estimates for isochronous or heterochronous data.
#'
#' @param phy a list containing a numeric vector of coalescent times (\code{coal_times}), a numeric vector of sampling times (\code{samp_times}, and a integer vector of number of samples taken at each sampling time (\code{n_sampled}).
#'
#' @return A list containing a vector of indices \code{k}, a vector of midpoints between coalescent times \code{mid_ctime}, and a vector of skyline population estimates \code{theta}.
#'
#' @export
#'
skyLine <- function(phy){
# classic skyline for isochronous or heterochronous data.
# phy is a list from summarize_phylo() with
# coal_times, samp_times, and n_sampled
# outputs vector of Ne estimates sorted from most recent.
st <- phy$samp_times
ns <- phy$n_sampled
ct <- phy$coal_times
sm <- data.frame(type='s', time=st, nadd=ns)
cm <- data.frame(type='c', time=ct, nadd=-1)
zm <- merge(sm, cm, all=T)
zm <- zm[order(zm$time), ]
zm$ncount <- cumsum(zm$nadd)
zn <- nrow(zm)
zuid <- numeric(zn)
zuid[zm$type=='c'] <- 1:nrow(cm)
for (j in (zn-1):1) {
if (zuid[j]==0) zuid[j] <- zuid[j+1]
}
zm$zuid <- zuid
wk <- diff(zm$time)
dm <- data.frame(uid=zm$zuid[-1], ncount=zm$ncount[-zn], wk=round(wk,8) )
dm$thsub <- dm$ncount*(dm$ncount-1)*dm$wk/2
adm <- aggregate(dm[,"thsub"], by=list(uid=dm$uid), sum)
zc1 <- c(0,cm$time[-nrow(cm)])
zc2 <- cm$time
ctmid <- zc1 + (zc2-zc1)/2
return(list(k=adm$uid, mid_ctime=ctmid, theta=adm$x))
}
#' Make grid for coalescent data
#'
#' Makes grid for effective population size estimation from coalescent data.
#' @export
makeGrid <- function(coal_times, samp_times, Ngrid){
gbds <- range(c(coal_times, samp_times))
grd <- seq(gbds[1],gbds[2],length=Ngrid)
intl <- grd[2] - grd[1]
mids <- grd[-1] - intl/2
return(list(grid=grd, midpts=mids))
}
## log likelihood for a constant Ne
const_coal_loglike <- function(theta, y, C, D){
-theta*sum(y) - exp(-theta)*sum(C*D)
}
## squared exponential cov fun for ihPois_GP
#' @export
expCovih <- function(ssv, pvec){
# plist is list of parms {sigmaf2, ll}
sigmaf2 <- pvec[1]
ll <- pvec[2]
nn <- length(ssv)
cmat <- matrix(0, nn, nn)
for (i in 1:nn){
for (j in 1:nn){
cmat[i,j] <- sigmaf2*exp(-0.5*((i-j)/ll)^2)
}
}
cmat
}
muGPconst <- function(ssv, cmu=0){
rep(cmu, length(ssv))
}
#' Simulate from inhomogeneous, heterochronous coalescent.
#'
#' For simulating coalescent times using the method of thinning.
#' This function is based on code written by Michael Karcher for the \code{phylodyn} package.
#' @param samp_times numeric vector of sampling times.
#' @param n_sampled numeric vector of samples taken per sampling time.
#' @param traj function that returns effective population size at time t.
#' @param lower_bound numeric lower limit on \code{traj} function on its support.
#' @param ... additional arguments to be passed to \code{traj} function.
#'
#' @return A list containing vectors of coalescent times \code{coal_times},
#' intercoalescent times \code{intercoal_times}, and number of active lineages
#' \code{lineages}, as well as passing along \code{samp_times} and
#' \code{n_sampled}.
#'
#' @examples
#' coaltimeSim(0:2, 3:1, unif_traj, lower_bound=10, level=10)
#' @export
coaltimeSim <- function(samp_times, n_sampled, traj, lower_bound, ...)
{
coal_times = NULL
lineages = NULL
curr = 1
active_lineages = n_sampled[curr]
time = samp_times[curr]
while (time <= max(samp_times) || active_lineages > 1)
{
if (active_lineages == 1)
{
curr = curr + 1
active_lineages = active_lineages + n_sampled[curr]
time = samp_times[curr]
}
time = time + rexp(1, 0.5*active_lineages*(active_lineages-1)/lower_bound)
if (curr < length(samp_times) && time >= samp_times[curr + 1])
{
curr = curr + 1
active_lineages = active_lineages + n_sampled[curr]
time = samp_times[curr]
}
else if (runif(1) <= lower_bound/traj(time, ...))
{
coal_times = c(coal_times, time)
lineages = c(lineages, active_lineages)
active_lineages = active_lineages - 1
}
}
return(list(coal_times = coal_times, lineages = lineages,
intercoal_times = c(coal_times[1], diff(coal_times)),
samp_times = samp_times, n_sampled = n_sampled))
}
#' Simulate from inhomogeneous, heterochronous coalescent driven by Gaussian process.
#'
#' For simulating coalescent times using the method of thinning.
#' This function is based on code written by Michael Karcher for the \code{phylodyn} package.
#'
#' @param samp_times numeric vector of sampling times.
#' @param n_sampled numeric vector of samples taken per sampling time.
#' @param trajvec vector of population sizes (should be dense)
#' @param lower_bound numeric lower limit on \code{traj} function on its support.
#' @param tvec grid boundaries for trajvec. Length is length(trajvec)+1
#'
#' @return A list containing vectors of coalescent times \code{coal_times},
#' intercoalescent times \code{intercoal_times}, and number of active lineages
#' \code{lineages}, as well as passing along \code{samp_times} and
#' \code{n_sampled}.
#' @export
#'
coaltimeSimGP <- function(samp_times, n_sampled, trajvec, tvec, lower_bound)
{
# trajvec has gp trajectory (should be dense)
# tvec is grid boundaries for trajvec (length(trajvec) + 1 )
coal_times = NULL
lineages = NULL
curr = 1
active_lineages = n_sampled[curr]
time = samp_times[curr]
tup <- tvec[-1]
tlo <- tvec[-length(tvec)]
tint <- tvec[2]-tvec[1]
tmids <- tvec[-1]-tint/2
tadj <- diff(range(tvec))/(10*(length(tvec)-1))
while (time <= max(samp_times) || active_lineages > 1)
{
if (active_lineages == 1)
{
curr = curr + 1
active_lineages = active_lineages + n_sampled[curr]
time = samp_times[curr]
}
time = time + rexp(1, 0.5*active_lineages*(active_lineages-1)/lower_bound)
# convert time to index of trajvec
dtime <- time
if (sum(time==tlo)>0) dtime <- time + tadj #adjujst for cell boundaries
if (time >= max(tvec)) dtime <- max(tvec) - tadj # set long times equal to max
tind <- which(dtime > tlo & dtime < tup)
if (curr < length(samp_times) && time >= samp_times[curr + 1])
{
curr = curr + 1
active_lineages = active_lineages + n_sampled[curr]
time = samp_times[curr]
}
else if (runif(1) <= lower_bound/trajvec[tind] )
{
coal_times = c(coal_times, time)
lineages = c(lineages, active_lineages)
active_lineages = active_lineages - 1
}
}
return(list(coal_times = coal_times, lineages = lineages,
intercoal_times = c(coal_times[1], diff(coal_times)),
samp_times = samp_times, n_sampled = n_sampled))
}
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