R/bootStateSpace-pb-ssm-fixed-fork-dot.R

In bootStateSpace: Bootstrap for State Space Models

.PBSSMFixedFork <- function(R,
                            path,
                            prefix,
                            n,
                            time,
                            delta_t,
                            mu0,
                            sigma0_l,
                            alpha,
                            beta,
                            psi_l,
                            nu,
                            lambda,
                            theta_l,
                            type,
                            covariates,
                            gamma,
                            kappa,
                            mu0_fixed,
                            sigma0_fixed,
                            optimization_flag,
                            hessian_flag,
                            verbose,
                            weight_flag,
                            debug_flag,
                            perturb_flag,
                            xtol_rel,
                            stopval,
                            ftol_rel,
                            ftol_abs,
                            maxeval,
                            maxtime,
                            ncores,
                            seed) {
  if (ncores > R) {
    ncores <- R
  }
  if (!is.null(seed)) {
    set.seed(seed)
  }
  prep <- .PBSSMFixedPrepDynr(
    mu0 = mu0,
    sigma0_l = sigma0_l,
    alpha = alpha,
    beta = beta,
    psi_l = psi_l,
    nu = nu,
    lambda = lambda,
    theta_l = theta_l,
    mu0_fixed = mu0_fixed,
    sigma0_fixed = sigma0_fixed
  )
  if (interactive()) {
    message(
      "\nGenerating data...\n"
    )
  }
  parallel::mclapply(
    X = seq_len(R),
    FUN = .PBSSMFixedData,
    path = path,
    prefix = prefix,
    n = n,
    time = time,
    delta_t = delta_t,
    mu0 = mu0,
    sigma0_l = sigma0_l,
    alpha = alpha,
    beta = beta,
    psi_l = psi_l,
    nu = nu,
    lambda = lambda,
    theta_l = theta_l,
    type = type,
    covariates = covariates,
    gamma = gamma,
    kappa = kappa,
    mc.cores = ncores
  )
  if (interactive()) {
    message(
      "Model fitting...\n"
    )
  }
  parallel::mclapply(
    X = seq_len(R),
    FUN = .PBFitDynr,
    path = path,
    prefix = prefix,
    dynr_initial = prep$dynr_initial,
    dynr_measurement = prep$dynr_measurement,
    dynr_noise = prep$dynr_noise,
    dynr_dynamics = prep$dynr_dynamics,
    optimization_flag = optimization_flag,
    hessian_flag = hessian_flag,
    verbose = verbose,
    weight_flag = weight_flag,
    debug_flag = debug_flag,
    perturb_flag = perturb_flag,
    xtol_rel = xtol_rel,
    stopval = stopval,
    ftol_rel = ftol_rel,
    ftol_abs = ftol_abs,
    maxeval = maxeval,
    maxtime = maxtime,
    mc.cores = ncores
  )
  thetahatstar <- parallel::mclapply(
    X = seq_len(R),
    FUN = .PBCoefDynr,
    path = path,
    prefix = prefix,
    mc.cores = ncores
  )
  list(
    prep = prep,
    thetahatstar = thetahatstar
  )
}