optim_pheno  R Documentation 
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
)
prior 
A vector of initial values for the parameters for which optimal
values are to be found. 
sFUN 
The name of fine curve fitting functions, can be one of 
y 
Numeric vector, vegetation index timeseries 
t 
Numeric vector, 
tout 
Corresponding doy of prediction. 
method 
The name of optimization method to solve fine fitting, one of

w 
(optional) Numeric vector, weights of 
nptperyear 
Integer, number of images per year, passed to 
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

constrain 
boolean, whether to use parameter constrain 
verbose 
Whether to display intermediate variables? 
... 
other parameters passed to 
use.cpp 
(unstable, not used) boolean, whether to use c++ defined fine
fitting function? If 
A fFIT()
object, with the element of:
tout
: The time of output curve fitting timeseries.
zs
: Smoothed vegetation timeseries of every iteration.
ws
: Weights of every iteration.
par
: Final optimized parameter of fine fitting.
fun
: The name of fine fitting.
fFIT()
, stats::nlminb()
# library(magrittr)
# library(purrr)
# simulate vegetation timeseries
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)
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