parall.gmj: A function to run parallel chains of (R)(G)MJMCMC algorithms

In aliaksah/EMJMCMC2016: EMJMCMC

A function to run parallel chains of (R)(G)MJMCMC algorithms

Usage

(X,M=16,preschedule=F)

Arguments

X	a vector of lists of parameters of runemjmcmc as well as several additional fields that must come after runemjmcmc parameters such as: vect$simlen - the number of parameters of runemjmcmc in vect, vect$cpu - the cpu id for to set the unique seed, vect$NM - the number of unique best models from runemjmcmc to base the output report upon
M	a number of cpus to be used (can only be equal to 1 on Windows OS currently, up to a maximal number of cores can be used on linux based systems)
preschedule	if pseudoscheduling should be used for the jobs if their number exeeds M (if TRUE) otherwise the jobs are performed sequentially w.r.t. their order

Value

a vector of lists of

post.populi	the total mass (sum of the marginal likelihoods times the priors of the visited models) from the addressed run of runemjmcmc
p.post	posterior probabilities of the covariates approximated by the addressed run of runemjmcmc
cterm	the best value of marginal likelihood times the prior from the addressed run of runemjmcmc
fparam	the final set of covariates returned by the addressed run of runemjmcmc

See Also

runemjmcmc, parall.gmj

Examples

j=1
M=4
X4<- as.data.frame(array(data = rbinom(n = 50*1000,size = 1,prob = runif(n = 50*1000,0,1)),dim = c(1000,50)))
Y4<-rnorm(n = 1000,mean = 1+7*(X4$V4*X4$V17*X4$V30*X4$V10)+7*(X4$V50*X4$V19*X4$V13*X4$V11) + 9*(X4$V37*X4$V20*X4$V12)+ 7*(X4$V1*X4$V27*X4$V3)+3.5*(X4$V9*X4$V2) + 6.6*(X4$V21*X4$V18) + 1.5*X4$V7 + 1.5*X4$V8,sd = 1)
X4$Y4<-Y4
  
formula1 = as.formula(paste(colnames(X4)[51],"~ 1 +",paste0(colnames(X4)[-c(51)],collapse = "+")))
data.example = as.data.frame(X4)

vect<-list(formula = formula1,outgraphs=F,data = X4,estimator = estimate.logic.lm,estimator.args =  list(data = data.example,n = 100, m = 50),recalc_margin = 249, save.beta = F,interact = T,relations = c("","lgx2","cos","sigmoid","tanh","atan","erf"),relations.prob =c(0.4,0.0,0.0,0.0,0.0,0.0,0.0),interact.param=list(allow_offsprings=1,mutation_rate = 250,last.mutation = 15000, max.tree.size = 4, Nvars.max =40,p.allow.replace=0.7,p.allow.tree=0.2,p.nor=0,p.and = 0.9),n.models = 20000,unique = T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 50,print.freq = 1000,advanced.param = list(
    max.N.glob=as.integer(10),
    min.N.glob=as.integer(5),
    max.N=as.integer(3),
    min.N=as.integer(1),
    printable = F))
  
  params <- list(vect)[rep(1,M)]
  
  for(i in 1:M)
  {
    params[[i]]$cpu<-i
    params[[i]]$NM<-1000
    params[[i]]$simlen<-21
  }
  gc()
  print(paste0("begin simulation ",j))
  results<-parall.gmj(X = params,M=1)#increase M if you are not on Windows!

aliaksah/EMJMCMC2016 documentation built on July 27, 2023, 5:48 a.m.