Run PEST++ GLM (Gauss-Levenberg-Marquardt)

Executes the pestpp-glm algorithm for deterministic parameter estimation.

Usage

pesto_glm(
  pst_file,
  exe = NULL,
  noptmax = NULL,
  extra_args = list(),
  working_dir = NULL,
  verbose = TRUE
)

Arguments

pst_file

Character. Path to the .pst control file.

exe

Character. Path to pestpp-glm executable.

noptmax

Integer or NULL. Maximum number of iterations. Written into the control file's * control data section, overriding the value there; PEST++ does not accept it as a ++ option. NULL (the default) leaves the file's own value alone.

extra_args

Named list. Additional PEST++ options, written to the control file as ++key(value) lines. Keys must be PestppOptions keys – PEST++ rejects one it does not recognise. An option the control file already sets is replaced, not duplicated.

working_dir

Character. Working directory.

verbose

Logical. Print output.

Value

A list of class pesto_glm_result.

Examples

# \donttest{
if (nzchar(Sys.which("pestpp-glm"))) {
  pars <- data.table::data.table(
    parnme = c("k1", "k2"), partrans = "log", parchglim = "factor",
    parval1 = c(1.0, 0.5), parlbnd = c(0.01, 0.001),
    parubnd = c(100, 50), pargp = "hydraulic"
  )
  obs <- data.table::data.table(
    obsnme = c("h1", "h2"), obsval = c(1.0, 2.0),
    weight = c(1.0, 1.0), obgnme = "head"
  )
  pst <- create_pest_scenario(pars, obs, model_command = "echo run")
  tf <- tempfile(fileext = ".pst")
  on.exit(unlink(tf), add = TRUE)
  write_pst(pst, tf)
  res <- pesto_glm(tf, noptmax = 1, verbose = FALSE)
  res$exit_code
}
# }