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abundance-density-functions-tl.R
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abundance-density-functions-tl.R
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# Function like findDisDistr in MetapopEpi
# However finds the distr a certain time since invasion
findDisDistrAtTime <- function(pop, final = 1, invadeT, time = 30){
assertthat::is.count(final)
invadeTime <- cumsum(pop$sampleWaiting)[invadeT / pop$parameters["sample"]]
# check that the sim was long enough
if(sum(pop$sampleWaiting) < invadeTime + time) warning('Did not run sim for long enough')
# Find which sample index is "time" after invadeTime
indextoSample <- which(cumsum(pop$sampleWaiting) > (invadeTime + time))[1]
finalI <- apply(pop$sample[, , (indextoSample - final):indextoSample], 1, mean)
finalDis <- sapply(1:pop$parameters["nPathogens"], function(x) finalI[pop$whichClasses[,
x]] %>% sum)
finalDis
}
#################################
# Density sim definitions #
#################################
# Define our simulation function.
fullSim1 <- function(x){
# Set seed (this is set within each parallel simulation to prevent reusing random numbers).
simSeed <- paste0(seed, x)
set.seed(simSeed)
# Make the population.
p <- makePop(space = side[x],
transmission = tran[x],
meanColonySize = colonySize[x],
nColonies = 20,
model = 'SIR',
events = nEvent,
nPathogens = 2,
recovery = 0.5,
sample = sample,
dispersal = 0.01,
birth = 0.05,
death = 0.05,
crossImmunity = 0.1,
infectDeath = 0,
maxDistance = 100)
p$I[1, , 1] <- colonySize[x] - 20
# Seed endemic pathogen.
p$I[3, , 1] <- 20
# Recalculate rates of each event after seeding.
p <- transRates(p, 1)
# Burn in simulation
p <- runSim(p, end = invadeT)
# Seed invading pathogen.
p$I[2, 1, (invadeT + 1) %% sample] <- 5
# Recalculate rates of each event after seeding.
p <- transRates(p, (invadeT + 1) %% sample)
# Continue simulation
p <- runSim(p, start = invadeT + 1, end = 'end')
# Was the invasion succesful?
invasion <- findDisDistrAtTime(p, final = 10, invadeT, time = TotalTime)[1] > 0
# Save summary stats
d <- data.frame(transmission = NA)
d$transmission <- p$parameters['transmission']
d$dispersal <- p$parameters['dispersal']
d$nExtantDis <- sum(findDisDistrAtTime(p, final = 2, invadeT, time = TotalTime) > 0)
d$nPathogens <- p$parameters['nPathogens']
d$meanK <- sum(p$adjacency != 0 )/p$parameters['nColonies']
d$maxDistance <- p$parameters['maxDistance']
d$nEvents <- p$parameters['events']
d$colonySize <- p$parameters['meanColonySize']
d$colonyNumber <- p$parameters['nColonies']
d$pop <- p$parameters['meanColonySize'] * p$parameters['nColonies']
d$area <- p$parameters['space']^2
d$dens <- d$pop / d$area
d$invadeTime <- cumsum(p$sampleWaiting)[invadeT / p$parameters["sample"]]
d$longenough <- sum(p$sampleWaiting) > d$invadeTime + TotalTime
#d$path2 <- sum(p$sample[c(2, 4), , dim(p$sample)[3]])
# Time until extinction
invadePath <- colSums(p$sample[2, , (2 + invadeT / sample):(which(cumsum(p$sampleWaiting) > TotalTime)[1])]) +
colSums(p$sample[4, , (2 + invadeT / sample):(which(cumsum(p$sampleWaiting) > TotalTime)[1])])
d$invadeTime <- cumsum(p$sampleWaiting)[(2 + invadeT / sample)]
d$extinctionTime <- cumsum(p$sampleWaiting)[min(which(invadePath == 0)) + (2 + invadeT / sample)]
d$totalTime <- sum(p$sampleWaiting)
d$survivalTime <- d$extinctionTime - cumsum(p$sampleWaiting)[(2 + invadeT / sample)]
write(paste0("finished ", x, ". Invasion: ", invasion ), 'runlog.R', append = TRUE)
message(paste0("finished ", x, ". Invasion: ", invasion ))
message(paste('trans:', d$transmission, 'nColonies:', d$colonyNumber,
'colonySize:', d$colonySize, 'dens:', d$dens, 'pop:', d$pop))
message(paste(names(d), collapse = ', '))
message(paste(d, collapse = ', '))
if(saveData){
file <- paste0('Data/DensSim_', formatC(x, width = 4, flag = '0'), '.RData')
save(p, file = file)
}
#rm(p)
return(d)
}
#################################
# Density2 sim definitions #
#################################
# Define our simulation function.
fullSim2 <- function(x){
# Set seed (this is set within each parallel simulation to prevent reusing random numbers).
simSeed <- paste0(seed, x)
set.seed(simSeed)
# Make the population.
p <- makePop(space = side[x],
transmission = tran[x],
meanColonySize = colonySize,
nColonies = colonyNumber[x],
model = 'SIR',
events = nEvent,
nPathogens = 2,
recovery = 0.5,
sample = sample,
dispersal = 0.01,
birth = 0.05,
death = 0.05,
crossImmunity = 0.1,
infectDeath = 0,
maxDistance = 100)
p$I[1, , 1] <- colonySize - 20
# Seed endemic pathogen.
p$I[3, , 1] <- 20
# Recalculate rates of each event after seeding.
p <- transRates(p, 1)
# Burn in simulation
p <- runSim(p, end = invadeT)
# Seed invading pathogen.
p$I[2, 1, (invadeT + 1) %% sample] <- 5
# Recalculate rates of each event after seeding.
p <- transRates(p, (invadeT + 1) %% sample)
# Continue simulation
p <- runSim(p, start = invadeT + 1, end = 'end')
# Was the invasion succesful?
invasion <- findDisDistrAtTime(p, final = 10, invadeT, time = TotalTime)[1] > 0
# Save summary stats
d <- data.frame(transmission = NA)
d$transmission <- p$parameters['transmission']
d$dispersal <- p$parameters['dispersal']
d$nExtantDis <- sum(findDisDistrAtTime(p, final = 2, invadeT, time = TotalTime) > 0)
d$nPathogens <- p$parameters['nPathogens']
d$meanK <- sum(p$adjacency != 0 )/p$parameters['nColonies']
d$maxDistance <- p$parameters['maxDistance']
d$nEvents <- p$parameters['events']
d$colonySize <- p$parameters['meanColonySize']
d$colonyNumber <- p$parameters['nColonies']
d$pop <- p$parameters['meanColonySize'] * p$parameters['nColonies']
d$area <- p$parameters['space']^2
d$dens <- d$pop / d$area
d$invadeTime <- cumsum(p$sampleWaiting)[invadeT / p$parameters["sample"]]
d$longenough <- sum(p$sampleWaiting) > d$invadeTime + TotalTime
#d$path2 <- sum(p$sample[c(2, 4), , dim(p$sample)[3]])
# Time until extinction
invadePath <- colSums(p$sample[2, , (2 + invadeT / sample):(which(cumsum(p$sampleWaiting) > TotalTime)[1])]) +
colSums(p$sample[4, , (2 + invadeT / sample):(which(cumsum(p$sampleWaiting) > TotalTime)[1])])
d$invadeTime <- cumsum(p$sampleWaiting)[(2 + invadeT / sample)]
d$extinctionTime <- cumsum(p$sampleWaiting)[min(which(invadePath == 0)) + (2 + invadeT / sample)]
d$totalTime <- sum(p$sampleWaiting)
d$survivalTime <- d$extinctionTime - cumsum(p$sampleWaiting)[(2 + invadeT / sample)]
write(paste0("finished ", x, ". Invasion: ", invasion ), 'runlog.R', append = TRUE)
message(paste0("finished ", x, ". Invasion: ", invasion ))
message(paste('trans:', d$transmission, 'nColonies:', d$colonyNumber,
'colonySize:', d$colonySize, 'dens:', d$dens, 'pop:', d$pop))
message(paste(names(d), collapse = ', '))
message(paste(d, collapse = ', '))
if(saveData){
file <- paste0('Data/DensSim_', formatC(x, width = 4, flag = '0'), '.RData')
save(p, file = file)
}
#rm(p)
return(d)
}
#################################
# Population sim definitions #
#################################
# Define our simulation function.
fullSim3 <- function(x){
# Set seed (this is set within each parallel simulation to prevent reusing random numbers).
simSeed <- paste0(seed, x)
set.seed(simSeed)
# Make the population.
p <- makePop(space = side[x],
transmission = tran[x],
meanColonySize = colonySize,
nColonies = 20,
model = 'SIR',
events = nEvent,
nPathogens = 2,
recovery = 0.5,
sample = sample,
dispersal = 0.01,
birth = 0.05,
death = 0.05,
crossImmunity = 0.1,
infectDeath = 0,
maxDistance = 100)
p$I[1, , 1] <- colonySize - 20
# Seed endemic pathogen.
p$I[3, , 1] <- 20
# Recalculate rates of each event after seeding.
p <- transRates(p, 1)
# Burn in simulation
p <- runSim(p, end = invadeT)
# Seed invading pathogen.
p$I[2, 1, (invadeT + 1) %% sample] <- 5
# Recalculate rates of each event after seeding.
p <- transRates(p, (invadeT + 1) %% sample)
# Continue simulation
p <- runSim(p, start = invadeT + 1, end = 'end')
# Was the invasion succesful?
invasion <- findDisDistrAtTime(p, final = 10, invadeT, time = TotalTime)[1] > 0
# Save summary stats
d <- data.frame(transmission = NA)
d$transmission <- p$parameters['transmission']
d$dispersal <- p$parameters['dispersal']
d$nExtantDis <- sum(findDisDistrAtTime(p, final = 2, invadeT, time = TotalTime) > 0)
d$nPathogens <- p$parameters['nPathogens']
d$meanK <- sum(p$adjacency != 0 )/p$parameters['nColonies']
d$maxDistance <- p$parameters['maxDistance']
d$nEvents <- p$parameters['events']
d$colonySize <- p$parameters['meanColonySize']
d$colonyNumber <- p$parameters['nColonies']
d$pop <- p$parameters['meanColonySize'] * p$parameters['nColonies']
d$area <- p$parameters['space']^2
d$dens <- d$pop / d$area
d$invadeTime <- cumsum(p$sampleWaiting)[invadeT / p$parameters["sample"]]
d$longenough <- sum(p$sampleWaiting) > d$invadeTime + TotalTime
#d$path2 <- sum(p$sample[c(2, 4), , dim(p$sample)[3]])
# Time until extinction
invadePath <- colSums(p$sample[2, , (2 + invadeT / sample):(which(cumsum(p$sampleWaiting) > TotalTime)[1])]) +
colSums(p$sample[4, , (2 + invadeT / sample):(which(cumsum(p$sampleWaiting) > TotalTime)[1])])
d$invadeTime <- cumsum(p$sampleWaiting)[(2 + invadeT / sample)]
d$extinctionTime <- cumsum(p$sampleWaiting)[min(which(invadePath == 0)) + (2 + invadeT / sample)]
d$totalTime <- sum(p$sampleWaiting)
d$survivalTime <- d$extinctionTime - cumsum(p$sampleWaiting)[(2 + invadeT / sample)]
write(paste0("finished ", x, ". Invasion: ", invasion ), 'runlog.R', append = TRUE)
message(paste0("finished ", x, ". Invasion: ", invasion ))
message(paste('trans:', d$transmission, 'nColonies:', d$colonyNumber,
'colonySize:', d$colonySize, 'dens:', d$dens, 'pop:', d$pop))
message(paste(names(d), collapse = ', '))
message(paste(d, collapse = ', '))
if(saveData){
file <- paste0('Data/PopSim_', formatC(x, width = 4, flag = '0'), '.RData')
save(p, file = file)
}
#rm(p)
return(d)
}