optim_pheno

Interface of optimization functions for double logistics and other parametric curve fitting functions.

optim_pheno(
  prior,
  sFUN,
  y,
  t,
  tout,
  method,
  w,
  nptperyear,
  ylu,
  iters = 2,
  wFUN = wTSM,
  lower = -Inf,
  upper = Inf,
  constrain = TRUE,
  verbose = FALSE,
  ...,
  use.cpp = 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.
sFUN: The name of fine curve fitting functions, can be one of 'FitAG', 'FitDL.Beck', 'FitDL.Elmore', 'FitDL.Gu' and 'FitDL.Klos', 'FitDL.Zhang'.
y: Numeric vector, vegetation index time-series
t: Numeric vector, Date variable
tout: Corresponding doy of prediction.
method: The name of optimization method to solve fine fitting, one of 'BFGS','CG','Nelder-Mead', 'L-BFGS-B', 'nlm', 'nlminb', 'ucminf' and 'spg','Rcgmin','Rvmmin', 'newuoa','bobyqa','nmkb','hjkb'.
w: (optional) Numeric vector, weights of y. If not specified, weights of all NA values will be wmin, the others will be 1.0.
nptperyear: Integer, number of images per year, passed to wFUN. Only wTSM() needs nptperyear. If not specified, nptperyear will be calculated based on t.
ylu: [ymin, ymax], which is used to force ypred in the range of ylu.
iters: How many times curve fitting is implemented.
wFUN: weights updating function, can be one of 'wTSM', 'wChen' and 'wBisquare'.
lower, upper: vectors of lower and upper bounds, replicated to be as long as start. If unspecified, all parameters are assumed to be unconstrained.
constrain: boolean, whether to use parameter constrain
verbose: Whether to display intermediate variables?
...: other parameters passed to I_optim() or I_optimx().
use.cpp: (unstable, not used) boolean, whether to use c++ defined fine fitting function? If FALSE, R version will be used.

Value

A fFIT() object, with the element of:

tout: The time of output curve fitting time-series.
zs : Smoothed vegetation time-series of every iteration.
ws : Weights of every iteration.
par : Final optimized parameter of fine fitting.
fun : The name of fine fitting.

Examples

# library(magrittr)
# library(purrr)

# simulate vegetation time-series
t    <- seq(1, 365, 8)
tout <- seq(1, 365, 1)

FUN = doubleLog_Beck
par  = c( mn  = 0.1 , mx  = 0.7 , sos = 50 , rsp = 0.1 , eos = 250, rau = 0.1)
par0 = c( mn  = 0.15, mx  = 0.65, sos = 100, rsp = 0.12, eos = 200, rau = 0.12)

y <- FUN(par, t)

methods = c("BFGS", "ucminf", "nlm", "nlminb")
opt1 <- I_optim(par0, doubleLog_Beck, y, t, methods) # "BFGS", "ucminf", "nlm",
# opt2 <- I_optimx(prior, fFUN, y, t, tout, )

sFUN   = "doubleLog.Beck" # doubleLog.Beck
r <- optim_pheno(par0, sFUN, y, t, tout, method = methods[4],
                 nptperyear = 46, iters = 2, wFUN = wTSM, verbose = FALSE, use.julia = FALSE)

Arguments

Value

See also

Examples