# Run microsimulation (parallel computing) The function `micSimParallel` is a parallelized version of the function micSim . That is, it runs a continuous-time microsimulation simulation distributed, i.e., using more than one CPU core. ```r micSimParallel(initPop, immigrPop = NULL, initPopList = c(), immigrPopList = c(), transitionMatrix, absStates = NULL, varInitStates = c(), initStatesProb = c(), fixInitStates = c(), maxAge = 99, simHorizon, fertTr = c(), monthSchoolEnrol=c(), cores=1, seeds=1254) ``` ## Arguments - `initPop`: Either an initial population has to be given as a whole or splitted to be run at the distinct cores, see arguments `initPopList`. If it is given as a whole it is automatically splitted by MicSim such that the population parts run on the distinct cores are approx. equally sized. - `immigrPop`: Optionally, a population of migrants entering the virtual population along simulation time can be given, see arguments `immigrPopList`. This migrants population can either be given as a whole or splitted to be run at the distinct cores. If it is given as a whole it is automatically splitted by MicSim such that the population parts run on the distinct cores are approx. equally sized. - `transitionMatrix`: See micSim . - `absStates`: See micSim . - `varInitStates`: See micSim . - `initStatesProb`: See micSim . - `initPopList`: Optional: A list containing the initial population split for the distinct cores. - `immigrPopList`: Optional: A list containing the immigration population split for the distinct cores. - `fixInitStates`: See micSim . - `maxAge`: See micSim . - `simHorizon`: See micSim . - `fertTr`: See micSim . - `monthSchoolEnrol`: See micSim . - `cores`: Number of CPUs to be used. - `seeds`: Seeds for pseudo number generators used for parallel computing. ## Details The argument `cores` must not exceed the number of cores of the computer (cluster) used. In `seeds` as many seeds should be given as cores are used. If less are given, the latter are repeated to complete the set of seeds. ## Returns The data frame `pop` contains the whole synthetic population considered during simulation including all events generated. For more details, see micSim . ## Author(s) Sabine Zinn ## Examples ```r # Clean workspace rm(list=ls()) # Defining simulation horizon startDate <- 20140101 # yyyymmdd endDate <- 20241231 # yyyymmdd simHorizon <- c(startDate=startDate, endDate=endDate) # Seed for random number generator set.seed(234) # Definition of maximal age maxAge <- 100 # Defintion of nonabsorbing and absorbing states sex <- c("m","f") fert <- c("0","1+") marital <- c("NM","M","D","W") edu <- c("no","low","med","high") stateSpace <- expand.grid(sex=sex,fert=fert,marital=marital,edu=edu) absStates <- c("dead","rest") # General month of enrollment to elementary school monthSchoolEnrol <- 9 # Definition of an initial population (for illustration purposes, create a random population) N = 10000 birthDates <- runif(N, min=getInDays(19500101), max=getInDays(20131231)) getRandInitState <- function(birthDate){ age <- trunc((getInDays(simHorizon[1]) - birthDate)/365.25) s1 <- sample(sex,1) s2 <- ifelse(age<=18, fert[1], sample(fert,1)) s3 <- ifelse(age<=18, marital[1], ifelse(age<=22, sample(marital[1:3],1), sample(marital,1))) s4 <- ifelse(age<=7, edu[1], ifelse(age<=18, edu[2], ifelse(age<=23, sample(edu[2:3],1), sample(edu[-1],1)))) initState <- paste(c(s1,s2,s3,s4),collapse="/") return(initState) } initPop <- data.frame(ID=1:N, birthDate=birthDates, initState=sapply(birthDates, getRandInitState)) initPop$birthDate <- getInDateFormat(initPop$birthDate) range(initPop$birthDate) # Definition of immigrants entering the population (for illustration purposes, create immigrants # randomly) M = 2000 immigrDates <- runif(M, min=getInDays(20140101), max=getInDays(20241231)) immigrAges <- runif(M, min=15*365.25, max=70*365.25) immigrBirthDates <- immigrDates - immigrAges IDmig <- max(as.numeric(initPop[,"ID"]))+(1:M) immigrPop <- data.frame(ID = IDmig, immigrDate = immigrDates, birthDate=immigrBirthDates, immigrInitState=sapply(immigrBirthDates, getRandInitState)) immigrPop$birthDate <- getInDateFormat(immigrPop$birthDate) immigrPop$immigrDate <- getInDateFormat(immigrPop$immigrDate) # Definition of initial states for newborns varInitStates <- rbind(c("m","0","NM","no"),c("f","0","NM","no")) # Definition of related occurrence probabilities initStatesProb <- c(0.515,0.485) # Definition of (possible) transition rates # (1) Fertility rates (Hadwiger mixture model) fert1Rates <- function(age, calTime, duration){ # parity 1 b <- ifelse(calTime<=2020, 3.9, 3.3) c <- ifelse(calTime<=2020, 28, 29) rate <- (b/c)*(c/age)^(3/2)*exp(-b^2*(c/age+age/c-2)) rate[age<=15 | age>=45] <- 0 return(rate) } fert2Rates <- function(age, calTime, duration){ # partiy 2+ b <- ifelse(calTime<=2020, 3.2, 2.8) c <- ifelse(calTime<=2020, 32, 33) rate <- (b/c)*(c/age)^(3/2)*exp(-b^2*(c/age+age/c-2)) rate[age<=15 | age>=45 | duration<0.75] <- 0 return(rate) } # (2) Rates for first marriage (normal density) marriage1Rates <- function(age, calTime, duration){ m <- ifelse(calTime<=2020, 25, 30) s <- ifelse(calTime<=2020, 3, 3) rate <- dnorm(age, mean=m, sd=s) rate[age<=16] <- 0 return(rate) } # (3) Remariage rates (log-logistic model) marriage2Rates <- function(age, calTime, duration){ b <- ifelse(calTime<=2020, 0.07, 0.10) p <- ifelse(calTime<=2020, 2.7,2.7) lambda <- ifelse(calTime<=1950, 0.04, 0.03) rate <- b*p*(lambda*age)^(p-1)/(1+(lambda*age)^p) rate[age<=18] <- 0 return(rate) } # (4) Divorce rates (normal density) divorceRates <- function(age, calTime, duration){ m <- 40 s <- ifelse(calTime<=2020, 7, 6) rate <- dnorm(age,mean=m,sd=s) rate[age<=18] <- 0 return(rate) } # (5) Widowhood rates (gamma cdf) widowhoodRates <- function(age, calTime, duration){ rate <- ifelse(age<=30, 0, pgamma(age-30, shape=6, rate=0.06)) return(rate) } # (6) Rates to change educational attainment # Set rate to `Inf' to make transition for age 7 deterministic. noToLowEduRates <- function(age, calTime, duration){ rate <- ifelse(age==7,Inf,0) return(rate) } lowToMedEduRates <- function(age, calTime, duration){ rate <- dnorm(age,mean=16,sd=1) rate[age<=15 | age>=25] <- 0 return(rate) } medToHighEduRates <- function(age, calTime, duration){ rate <- dnorm(age,mean=20,sd=3) rate[age<=18 | age>=35] <- 0 return(rate) } # (7) Mortality rates (Gompertz model) mortRates <- function(age, calTime, duration){ a <- .00003 b <- ifelse(calTime<=2020, 0.1, 0.097) rate <- a*exp(b*age) return(rate) } # (8) Emigration rates emigrRates <- function(age, calTime, duration){ rate <- ifelse(age<=18,0,0.0025) return(rate) } # Transition pattern and assignment of functions specifying transition rates fertTrMatrix <- cbind(c("0->1+","1+->1+"), c("fert1Rates", "fert2Rates")) maritalTrMatrix <- cbind(c("NM->M","M->D","M->W","D->M","W->M"), c("marriage1Rates","divorceRates","widowhoodRates", "marriage2Rates","marriage2Rates")) eduTrMatrix <- cbind(c("no->low","low->med","med->high"), c("noToLowEduRates","lowToMedEduRates","medToHighEduRates")) allTransitions <- rbind(fertTrMatrix, maritalTrMatrix, eduTrMatrix) absTransitions <- rbind(c("dead","mortRates"),c("rest","emigrRates")) transitionMatrix <- buildTransitionMatrix(allTransitions=allTransitions, absTransitions=absTransitions, stateSpace=stateSpace) # Define transitions triggering a birth event fertTr <- fertTrMatrix[,1] # Run microsimulation on cluster with three cores (settings depend on cluster used) ## Not run: cores <- 3 seeds <- c(1233,1245,265) initPopList <- list(initPop[1:5000,], initPop[5001:8000,],initPop[8001:nrow(initPop),]) immigrPopList <- list(immigrPop[1:1000,], immigrPop[1001:1500,],immigrPop[1501:nrow(immigrPop),]) pop <- micSimParallel(initPopList=initPopList, immigrPopList=immigrPopList, transitionMatrix=transitionMatrix, absStates=absStates, varInitStates=varInitStates, initStatesProb=initStatesProb, maxAge=maxAge, simHorizon=simHorizon, fertTr=fertTr, monthSchoolEnrol=monthSchoolEnrol, cores=cores, seeds=seeds) ## End(Not run) ```

micSimParallel function