EMJMCMC2016: EMJMCMC

#source("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/R/the_mode_jumping_package4.r")


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)>0)) + 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
data.example = as.data.frame(X4)

#specify the initial formula
formula1 = as.formula(paste(colnames(X4)[51],"~ 1 +",paste0(colnames(X4)[-c(51)],collapse = "+")))


#specify tuning parameters of the algorithm for exploring DBRM of interest
#notice that allow_offsprings=3 corresponds to the GMJMCMC runs and
#allow_offsprings=4 -to the RGMJMCMC runs
 res = runemjmcmc(formula = formula1,outgraphs=F,data = X4,estimator = estimate.gamma.cpen,estimator.args =  list(data = data.example),recalc_margin = 249, save.beta = F,interact = T,relations=c("cos","sigmoid","tanh","atan","sin","erf"),relations.prob =c(0.1,0.1,0.1,0.1,0.1,0.1),interact.param=list(allow_offsprings=4,mutation_rate = 250,last.mutation = 1500, 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 = 2000,unique = T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 50,print.freq = 100,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))