phenofit (version 0.2.5-2)

I_optim: Interface of unified optimization functions.

Description

Caution that optimx speed is not so satisfied. So I_optim is present.

Usage

I_optim(prior, FUN, y, t, tout, method = "BFGS", ...)
I_optimx(prior, FUN, y, t, tout, method, verbose = FALSE, ...)

Arguments

prior

A vector of initial values for the parameters for which optimal values are to be found. prior is suggested giving a column name.

FUN

Fine curve fitting function for goal function f_goal().

y

Numeric vector, vegetation index time-series

t

Numeric vector, Date variable

tout

Corresponding doy of prediction.

method

method can be one of 'BFGS','CG','Nelder-Mead', 'L-BFGS-B', 'nlm', 'nlminb', 'ucminf'. For I_optimx, other methods are also supported, e.g. 'spg','Rcgmin','Rvmmin', 'newuoa','bobyqa','nmkb','hjkb'.

...

other parameters passed to I_optim() or I_optimx().

verbose

If TRUE, all optimization methods in optimx::optimx() are used, and print optimization information of all methods.

Value

• convcode: An integer code. 0 indicates successful convergence. Various methods may or may not return sufficient information to allow all the codes to be specified. An incomplete list of codes includes

  • 1: indicates that the iteration limit maxit had been reached.

  • 20: indicates that the initial set of parameters is inadmissible, that is, that the function cannot be computed or returns an infinite, NULL, or NA value.

  • 21: indicates that an intermediate set of parameters is inadmissible.

  • 10: indicates degeneracy of the Nelder--Mead simplex.

  • 51: indicates a warning from the "L-BFGS-B" method; see component message for further details.

  • 52: indicates an error from the "L-BFGS-B" method; see component message for further details.

  • 9999: error

• value: The value of fn corresponding to par

• par: The best parameter found

• nitns: the number of iterations

• fevals: The number of calls to objective.

See Also

stats::optim(), stats::nlminb(), stats::nlm(), optimx::optimx(), ucminf::ucminf()

Examples

# NOT RUN {
library(ggplot2)
library(magrittr)
library(purrr)

# simulate vegetation time-series
fFUN = doubleLog.Beck
par = c(
  mn  = 0.1,
  mx  = 0.7,
  sos = 50,
  rsp = 0.1,
  eos = 250,
  rau = 0.1)
t    <- seq(1, 365, 8)
tout <- seq(1, 365, 1)
y <- fFUN(par, t)

# initial parameter
par0 <- c(
  mn  = 0.15,
  mx  = 0.65,
  sos = 100,
  rsp = 0.12,
  eos = 200,
  rau = 0.12)

objective <- f_goal # goal function
optFUNs   <- c("opt_ucminf", "opt_nlminb", "opt_nlm", "opt_optim") %>% set_names(., .)
prior <- as.matrix(par0) %>% t() %>% rbind(., .)

opt1 <- I_optim(prior, fFUN, y, t, tout, c("BFGS", "ucminf", "nlm", "nlminb"))
opt2 <- I_optimx(prior, fFUN, y, t, tout, c("BFGS", "ucminf", "nlm", "nlminb"))

# microbenchmark::microbenchmark(
#     I_optim(prior, fFUN, y, t, tout, c("BFGS", "ucminf", "nlm", "nlminb")),
#     I_optimx(prior, fFUN, y, t, tout, c("BFGS", "ucminf", "nlm", "nlminb")),
#     times = 2
# )
# }