R/read.admbFit.R

In r4ss/r4ss: R Code for Stock Synthesis

#' Read ADMB .par and .cor files.
#'
#' This function will parse the .par and .cor files to provide
#' things like parameter estimates, standard deviations,
#' and correlations. Required for Jim Thorson's Laplace
#' Approximation but likely useful for other purposes.
#' @param file Name of ADMB executable such that files to read will
#' have format file.par and file.cor.
#' @return List of various things from these files.
#' @author James Thorson
#' @seealso [getADMBHessian()], [NegLogInt_Fn()]
#' @export
read.admbFit <- function(file) {
  ret <- list()
  # read par file
  parfile <- as.numeric(scan(paste(file, ".par", sep = ""),
    what = "", n = 16, quiet = TRUE
  )[c(6, 11, 16)])
  ret[["nopar"]] <- as.integer(parfile[1])
  ret[["nloglike"]] <- parfile[2] # objective function value
  ret[["maxgrad"]] <- parfile[3]

  # read cor file
  file <- paste(file, ".cor", sep = "")
  lin <- readLines(file)
  # total parameter including sdreport variables
  ret[["totPar"]] <- length(lin) - 2
  # log of the determinant of the hessian
  ret[["logDetHess"]] <- as.numeric(strsplit(lin[1], "=")[[1]][2])
  sublin <- lapply(strsplit(lin[1:ret[["totPar"]] + 2], " "), function(x) x[x != ""])
  ret[["names"]] <- unlist(lapply(sublin, function(x) x[2]))
  ret[["est"]] <- as.numeric(unlist(lapply(sublin, function(x) x[3])))
  ret[["std"]] <- as.numeric(unlist(lapply(sublin, function(x) x[4])))
  ret[["cor"]] <- matrix(NA, ret[["totPar"]], ret[["totPar"]])
  corvec <- unlist(sapply(seq_along(sublin), function(i) sublin[[i]][5:(4 + i)]))
  ret[["cor"]][upper.tri(ret[["cor"]], diag = TRUE)] <- as.numeric(corvec)
  ret[["cor"]][lower.tri(ret[["cor"]])] <- t(ret[["cor"]])[lower.tri(ret[["cor"]])]
  # covariance matrix
  ret[["cov"]] <- ret[["cor"]] * (ret[["std"]] %o% ret[["std"]])
  return(ret)
}