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simulations.R
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simulations.R
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# Written by Micha³ Makowski
library(doSNOW)
library(foreach)
source("simulationsFunctions.R")
# Cluster structure
clusterBHtest <- createSimulationMatrix(nVec = 75,
pVec = 100,
graphTypeVec = "cluster",
alphaVec = c(0.05, 0.05*99/2/100),
penalizeDiagonalVec = FALSE,
iterationsVec = 3000)
cluster <- createSimulationMatrix(nVec = c(50, 100, 150),
pVec = 100,
graphTypeVec = "cluster",
alphaVec = c(0.1, 0.05, 0.01),
penalizeDiagonalVec = FALSE,
iterationsVec = 3000)
# Scall-free structure14
scale3sBHtest <- createSimulationMatrix(nVec = 50,
pVec = 50,
graphTypeVec = "hub",
alphaVec = c(0.05, 0.05*49/2/50),
penalizeDiagonalVec = FALSE,
iterationsVec = 2000)
scale3 <- createSimulationMatrix(nVec = c(50, 100, 150),
pVec = 100,
graphTypeVec = "hub",
alphaVec = c(0.1, 0.05, 0.01),
penalizeDiagonalVec = FALSE,
iterationsVec = 2000)
# Scall-free structure14
scale3sBHtest <- createSimulationMatrix(nVec = 50,
pVec = 50,
graphTypeVec = "scale-free",
alphaVec = c(0.05, 0.05*49/2/50),
penalizeDiagonalVec = FALSE,
iterationsVec = 3000)
scale3 <- createSimulationMatrix(nVec = c(50, 100, 150),
pVec = 100,
graphTypeVec = "scale-free",
alphaVec = c(0.1, 0.05, 0.01),
penalizeDiagonalVec = FALSE,
iterationsVec = 3000)
# N increment test
Nincrement <- createSimulationMatrix(nVec = c(20,80,320,1280,5120),
pVec = 80,
graphTypeVec = "cluster",
alphaVec = 0.05,
penalizeDiagonalVec = FALSE,
iterationsVec = 3000)
cl<-makeCluster(7) #change the 2 to your number of CPU cores
registerDoSNOW(cl)
doparList <-list(list(clusters, NULL),
list(scale3s, NULL),
list(hubs, NULL),
list(cluster, NULL),
list(scale3, NULL),
list(hub, NULL),
list(Nincrement, NULL))
results <- list()
results <- foreach(i = doparList) %dopar% {
source("simulationsFunctions.R")
simulations(i[[1]],
saveAll = TRUE,
additionalMethods = i[[2]])
}
stopCluster(cl)
for(r in 1:length(results))
{
filenameAll <- paste0("Simulation_", nrow(results[[r]]), "_",
format(Sys.time(), '%y_%m_%d_%H_%M'))
output <- results[[r]]
additionalMethods <- doparList[[r]][[2]]
save(output, additionalMethods,
file = paste0("./!02 Data/01 Binded/", filenameAll, ".RData"))
}
#
# rocCluster <- createSimulationMatrix(nVec = 100,
# pVec = 100,
# graphTypeVec = c("cluster"),
# alphaVec = seq(from = 0.01, to = 1, by = 0.01),
# penalizeDiagonalVec = FALSE,
# partialVec = TRUE,
# iterationsVec = 3000)
#
# rocClusterTest <- simulations(rocCluster,
# saveAll = FALSE)
# lambdaBanerjee(1000, 1000, alpha = 100)
# plot(1-rocClusterTest$SP[rocClusterTest$procedure == "gLASSO"],
# rocClusterTest$SN[rocClusterTest$procedure == "gLASSO"], "l")
# plot(1-rocClusterTest$SP[rocClusterTest$procedure == "holmgSLOPE"],
# rocClusterTest$SN[rocClusterTest$procedure == "holmgSLOPE"], "l")
# plot(1-rocClusterTest$SP[rocClusterTest$procedure == "BHgSLOPE"],
# rocClusterTest$SN[rocClusterTest$procedure == "BHgSLOPE"], "l")
# measures(n = 50, p = 100, graphType = "hub", alpha = 0.1, iterations = 100)
# nVec = 150,
# pVec = 200,
# graphTypeVec = "cluster",
# alphaVec = 0.05,
# penalizeDiagonalVec = FALSE,
# partialVec = FALSE,
# iterationsVec = 1000