inst/examples/parall.gmj_example.R

In EMJMCMC: Evolutionary Mode Jumping Markov Chain Monte Carlo Expert Toolbox

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 = FALSE, data = X4,
  estimator = estimate.logic.lm,
  estimator.args = list(data = data.example, n = 100, m = 50),
  recalc_margin = 249, save.beta = FALSE, interact = TRUE,
  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 = TRUE, max.cpu = 4, max.cpu.glob = 4,
  create.table = FALSE, create.hash = TRUE, pseudo.paral = TRUE,
  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 = FALSE
  )
)

params <- list(vect)[rep(1, M)]

for (i in 1:M) {
  params[[i]]$cpu <- i
  params[[i]]$NM <- 1000
  params[[i]]$simlen <- 21
}
\donttest{
  message("begin simulation ", j)
  set.seed(363571)
  results <- parall.gmj(X = params, M = 1)
}