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This is an example of how to run a numerical experimental design with landsepi.
First create a dataframe containing, for each line, the value of target parameters (i.e. those destined to vary from one simulation to another). The dataframe must also contain columns to store simulation outputs.
For the sake of simplicity, the following numerical design contains only 5 simulations for which 7 target parameters have been arbitrarily chosen to vary and 3 output variable are to be computed. Off course, real numerical designs will include more simulations, in particular those representing factorial experimental plans.
myDesign <- data.frame(nTSpY = c(120, 110, 100, 90, 80)
, pI0 = c(0, 1E-4, 5E-4, 1E-3, 1E-2)
, infection_rate = c(0.5, 0.4, 0.3, 0.2, 0.1)
, id_landscape = 1:5
, aggreg = c(0.07, 0.07, 0.25, 10, 10)
, R_efficiency = c(1.00, 0.90, 0.80, 0.70, 0.60)
, growth_rate = c(0.1, 0.2, 0.3, 0.1, 0.1)
## create columns to store outputs
, durab_MG1 = NA
, durab_MG2 = NA
, mean_audpc = NA)Then add an identifier for each simulation and specify a random seed (for stochasticity).
n <- nrow(myDesign)
myDesign <- cbind(simul = 1:n, seed = sample(n*100, n), myDesign)
myDesign
#> simul seed nTSpY pI0 infection_rate id_landscape aggreg R_efficiency
#> 1 1 322 120 0e+00 0.5 1 0.07 1.0
#> 2 2 41 110 1e-04 0.4 2 0.07 0.9
#> 3 3 121 100 5e-04 0.3 3 0.25 0.8
#> 4 4 392 90 1e-03 0.2 4 10.00 0.7
#> 5 5 248 80 1e-02 0.1 5 10.00 0.6
#> growth_rate durab_MG1 durab_MG2 mean_audpc
#> 1 0.1 NA NA NA
#> 2 0.2 NA NA NA
#> 3 0.3 NA NA NA
#> 4 0.1 NA NA NA
#> 5 0.1 NA NA NACreate the object simul_params and a repository to store
inputs and outputs of the simulations.
Then run the experimental design by updating target parameters with
the values stored in myDesign. Note that some parameters
vary jointly (e.g. landscape and dispersal matrix). See tutorial on
how to run a simple simulation
for details on the different functions.
for (i in 1:n){
print(paste("Running simulation", i, "/", n))
## Set Nyears and nTSpY
simul_params <- setTime(simul_params
, Nyears = 6
, nTSpY = myDesign$nTSpY[i]) ## update nTSpY
## set seed (to run stochastic replicates)
simul_params <- setSeed(simul_params, myDesign$seed[i]) ## update seed
## Pathogen parameters
simul_params@ReproSexProb <- logical(0) ## initialize vector of sexual probability (one for each time step)
basic_patho_param <- loadPathogen("rust")
basic_patho_param$infection_rate <- myDesign$infection_rate[i] ## update inf. rate
simul_params <- setPathogen(simul_params, basic_patho_param)
## Landscape parameters
landscape <- loadLandscape(myDesign$id_landscape[i]) ## update landscape
simul_params <- setLandscape(simul_params, landscape)
## Dispersal parameters
disp_patho_clonal <- loadDispersalPathogen(myDesign$id_landscape[i])[[1]] ## update dispersal
simul_params <- setDispersalPathogen(simul_params, disp_patho_clonal)
## Genes
gene1 <- loadGene(name = "MG 1", type = "majorGene")
gene1$mutation_prob<-1e-4
gene2 <- loadGene(name = "MG 2", type = "majorGene")
gene2$mutation_prob<-1e-4
genes <- data.frame(rbind(gene1, gene2), stringsAsFactors = FALSE)
genes$efficiency <- myDesign$R_efficiency[i] ## update resistance efficiency
simul_params <- setGenes(simul_params, genes)
## Cultivars
cultivar1 <- loadCultivar(name = "Susceptible", type = "wheat")
cultivar2 <- loadCultivar(name = "Resistant1", type = "wheat")
cultivar3 <- loadCultivar(name = "Resistant2", type = "wheat")
cultivars <- data.frame(rbind(cultivar1, cultivar2, cultivar3)
, stringsAsFactors = FALSE)
cultivars$growth_rate <- myDesign$growth_rate[i] ## update growth rate
simul_params <- setCultivars(simul_params, cultivars)
## Allocate genes to cultivars
simul_params <- allocateCultivarGenes(simul_params, "Resistant1", c("MG 1"))
simul_params <- allocateCultivarGenes(simul_params, "Resistant2", c("MG 2"))
## Allocate cultivars to croptypes
croptypes <- loadCroptypes(simul_params, names = c("Susceptible crop"
, "Resistant crop 1"
, "Resistant crop 2"))
croptypes <- allocateCroptypeCultivars(croptypes, "Susceptible crop", "Susceptible")
croptypes <- allocateCroptypeCultivars(croptypes, "Resistant crop 1", "Resistant1")
croptypes <- allocateCroptypeCultivars(croptypes, "Resistant crop 2", "Resistant2")
simul_params <- setCroptypes(simul_params, croptypes)
## Allocate croptypes to landscape
rotation_sequence <- croptypes$croptypeID ## No rotation: 1 rotation_sequence element
rotation_period <- 0 ## same croptypes every years
prop <- c(1/3, 1/3, 1/3) ## croptypes proportions
aggreg <- myDesign$aggreg[i]
simul_params <- allocateLandscapeCroptypes(simul_params,
rotation_period = rotation_period,
rotation_sequence = rotation_sequence,
rotation_realloc = FALSE,
prop = prop,
aggreg = aggreg,
graphic = FALSE)
## Initial conditions
simul_params <- setInoculum(simul_params, myDesign$pI0[i]) ## update pI0
#Only susceptible hosts are infected, see vignette1 for a more complex inoculum
## configure outputs
outputlist <- loadOutputs(epid_outputs = "audpc", evol_outputs = "durability")
simul_params <- setOutputs(simul_params, outputlist)
## Check, (save) and run simulation
checkSimulParams(simul_params)
# simul_params <- saveDeploymentStrategy(simul_params, overwrite = TRUE)
res <- runSimul(simul_params, writeTXT=FALSE, graphic = FALSE)
## Extract outputs
myDesign$durab_MG1[i] <- res$evol_outputs$durability[,"MG 1"]
myDesign$durab_MG2[i] <- res$evol_outputs$durability[,"MG 2"]
myDesign$mean_audpc[i] <- mean(res$epid_outputs$audpc$total)
## Create myDesign.txt at first simulation and then append
if (i ==1){
write.table(myDesign[i,], paste(simul_params@OutputDir, "/myDesign.txt", sep="")
, append=FALSE, row.names = FALSE, col.names = TRUE)
} else {
write.table(myDesign[i,], paste(simul_params@OutputDir, "/myDesign.txt", sep="")
, append=TRUE, row.names = FALSE, col.names = FALSE)
}
}
myDesignThe argument “keepRawResults” from function runSimul()
allows to keep a set of binary files after the end of the
simulation.
This set of binary files is composed of one file per simulated year and per compartment:
- H: healthy hosts,
- Hjuv: juvenile healthy hosts (for host reproduction),
- L: latently infected hosts,
- I: infectious hosts,
- R: removed hosts,
- P: propagules.
Each file indicates for every time-step the number of individuals in each field, and when appropriate for each host and pathogen genotypes.
## Disable computation of outputs:
outputlist <- loadOutputs(epid_outputs = "", evol_outputs = "")
simul_params <- setOutputs(simul_params, outputlist)
## Run simulation and keep raw binary files:
runSimul(simul_params, writeTXT=FALSE, graphic = FALSE, keepRawResults = TRUE)The following code loads the content of the binary files in lists (named “H”, “Hjuv”, “P”, “L”, “I”, “R”). Each element of these lists contains a matrix or an array of the number of individuals associated with each field, host genotype and pathogen genotype for a given time step (i.e. the number of elements of each list is the total number of time steps). Then, users can compute their own output variables using these lists.
## retrieve parameters from the object simul_params
path <- simul_params@OutputDir
Nyears <- simul_params@TimeParam$Nyears
nTSpY <- simul_params@TimeParam$nTSpY
nTS <- Nyears * nTSpY ## Total number of time-steps
Npoly <- nrow(simul_params@Landscape)
Nhost <- nrow(simul_params@Cultivars)
Npatho <- prod(simul_params@Genes$Nlevels_aggressiveness)
## Initialise lists
H <- as.list(1:nTS)
Hjuv <- as.list(1:nTS)
P <- as.list(1:nTS)
L <- as.list(1:nTS)
I <- as.list(1:nTS)
R <- as.list(1:nTS)
index <- 0
## Read binary files and store values in the lists as matrices or arrays
for (year in 1:Nyears) {
binfileH <- file(paste(path, sprintf("/H-%02d", year), ".bin", sep = ""), "rb")
H.tmp <- readBin(con = binfileH, what = "int", n = Npoly * Nhost * nTSpY, size = 4
, signed = T, endian = "little")
close(binfileH)
binfileHjuv = file(paste(path, sprintf("/Hjuv-%02d", year), ".bin",sep=""), "rb")
Hjuv.tmp <- readBin(con=binfileHjuv, what="int", n=Npoly*Nhost*nTSpY, size = 4
, signed=T,endian="little")
close(binfileHjuv)
binfileP <- file(paste(path, sprintf("/P-%02d", year), ".bin", sep = ""), "rb")
P.tmp <- readBin(con = binfileP, what = "int", n = Npoly * Npatho * nTSpY, size = 4
, signed = T, endian = "little")
close(binfileP)
binfileL <- file(paste(path, sprintf("/L-%02d", year), ".bin", sep = ""), "rb")
L.tmp <- readBin(con = binfileL, what = "int", n = Npoly * Npatho * Nhost * nTSpY
, size = 4 , signed = T, endian = "little")
close(binfileL)
binfileI <- file(paste(path, sprintf("/I-%02d", year), ".bin", sep = ""), "rb")
I.tmp <- readBin(con = binfileI, what = "int", n = Npoly * Npatho * Nhost * nTSpY
, size = 4 , signed = T, endian = "little")
close(binfileI)
binfileR <- file(paste(path, sprintf("/R-%02d", year), ".bin", sep = ""), "rb")
R.tmp <- readBin(con = binfileR, what = "int", n = Npoly * Npatho * Nhost * nTSpY
, size = 4 , signed = T, endian = "little")
close(binfileR)
## Convert vectors in matrices or arrays
for (t in 1:nTSpY) {
H[[t + index]] <- matrix(H.tmp[((Nhost * Npoly) * (t-1)+1):(t * (Nhost * Npoly))]
, ncol = Nhost, byrow = T)
Hjuv[[t + index]] <- matrix(Hjuv.tmp[((Nhost*Npoly)*(t-1)+1):(t*(Nhost*Npoly))]
, ncol=Nhost,byrow=T)
P[[t + index]] <- matrix(P.tmp[((Npatho * Npoly) * (t-1)+1):(t * (Npatho * Npoly))]
, ncol = Npatho, byrow = T)
L[[t + index]] <- array(data = L.tmp[((Npatho * Npoly * Nhost) *
(t-1)+1):(t * (Npatho * Npoly * Nhost))]
, dim = c(Nhost, Npatho, Npoly))
I[[t + index]] <- array(data = I.tmp[((Npatho * Npoly * Nhost) *
(t-1)+1):(t * (Npatho * Npoly * Nhost))]
, dim = c(Nhost, Npatho, Npoly))
R[[t + index]] <- array(data = R.tmp[((Npatho * Npoly * Nhost) *
(t-1)+1):(t * (Npatho * Npoly * Nhost))]
, dim = c(Nhost, Npatho, Npoly))
}
index <- index + nTSpY
}These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.