Nothing
phenonames <- c('TRS2.SOS', 'TRS2.EOS', 'TRS5.SOS', 'TRS5.EOS', 'TRS6.SOS', 'TRS6.EOS',
'DER.SOS', 'DER.POS', 'DER.EOS',
'UD', 'SD', 'DD','RD',
'GreenUp', 'Maturity', 'Senescence', 'Dormancy')
#' Fine curve fitting
#'
#' Curve fit vegetation index (VI) time-series of every growing season using
#' fine curve fitting methods.
#'
#' @param y Vegetation time-series index, numeric vector
#' @param t The corresponding doy of x
#' @param tout The output interpolated time.
#' @param methods Fine curve fitting methods, can be one or more of `c('AG',
#' 'Beck', 'Elmore', 'Gu', 'Klos', 'Zhang')`.
#' @param ... other parameters passed to curve fitting function.
#' @inheritParams optim_pheno
#' @inheritParams init_param
#'
#' @note 'Klos' have too many parameters. It will be slow and not stable.
#'
#' @return fFITs S3 object, see [fFITs()] for details.
#' @seealso [fFITs()]
#'
#' @examples
#' library(phenofit)
#' # simulate vegetation time-series
#' FUN = 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 <- FUN(par, t)
#'
#' methods <- c("AG", "Beck", "Elmore", "Gu", "Zhang") # "Klos" too slow
#' fit <- curvefit(y, t, tout = tout, methods)
#' @export
curvefit <- function(y, t = index(y), tout = t,
methods = c('AG', 'Beck', 'Elmore', 'Gu', 'Klos', 'Zhang'),
w = NULL, ..., type = 1L, use.cpp = FALSE)
{
if (all(is.na(y))) return(NULL)
if (is.null(w)) w = rep(1, length(y))
if (length(methods) == 1 && methods == 'all')
methods <- c('AG', 'Beck', 'Elmore', 'Gu', 'Klos', 'Zhang')
params <- list(y, t, tout, optimFUN = I_optim, ...)
str_DL <- ifelse(use.cpp, "cdoubleLog_", "doubleLog.")
fFITs = lapply(methods %>% set_names(., .), function(meth){
meth_optim = ifelse(meth == "Klos", "BFGS", "nlminb")
# expr = sprintf('do.call(FitDL.%s, c(params, method = "%s"))', meth, meth_optim)
fun_init = get(sprintf("init_%s", meth), mode = "function")
fun_name = paste0(str_DL, meth)
e <- init_param(y, t, w, type = type)
lims <- fun_init(e, type = type)
optim_pheno(lims$prior, fun_name, y, t, tout, meth_optim, w,
lower = lims$lower, upper = lims$upper, ..., use.cpp = use.cpp)
# eval(parse(text = expr))
})
structure(list(data = data.table(y, t), tout = tout, model = fFITs), class = 'fFITs')
}
finefit <- curvefit
## !DEPRECATED
#' curvefit0
#' @keywords internal
#' @export
curvefit0 <- function(y, t = index(y), tout = t,
methods = c("AG", "Beck", "Elmore", "Gu", "Klos", "Zhang"),
w = NULL, ...) {
if (all(is.na(y))) return(NULL)
if (is.null(w)) w <- rep(1, length(y))
if (length(methods) == 1 && methods == "all")
methods <- c("AG", "Beck", "Elmore", "Gu", "Klos", "Zhang")
params <- list(y, t, tout, optimFUN = I_optim, ...)
fFITs <- lapply(methods %>% set_names(., .), function(meth) {
meth_optim <- ifelse(meth == "Klos", "BFGS", "nlminb")
expr = sprintf('do.call(FitDL.%s, c(params, method = "%s"))', meth, meth_optim)
# expr <- sprintf("
# lims <- init_%s(y, t, w)
# optim_pheno(lims$prior, 'doubleLog.%s', y, t, tout, %s, w, lower = lims$lower, upper = lims$upper, ...)
# ", meth, meth, meth_optim)
eval(parse(text = expr))
})
structure(list(data = data.table(y, t), tout = tout, model = fFITs),
class = "fFITs")
}
# if ('spline' %in% methods) fit.spline <- splinefit(y, t, tout)
# if ('AG' %in% methods) fit.AG <- do.call(FitDL.AG, c(params, method = "nlminb")) #nlm
# if ('AG2' %in% methods) fit.AG2 <- do.call(FitDL.AG2, c(params, method = "nlminb")) #nlm
# if ('Beck' %in% methods) fit.Beck <- do.call(FitDL.Beck, c(params, method = "nlminb")) #nlminb
# if ('Elmore' %in% methods) fit.Elmore <- do.call(FitDL.Elmore,c(params, method = "nlminb")) #nlminb
# # best: BFGS, but its speed lower than other function, i.e. nlm
# if ('Gu' %in% methods) fit.Gu <- do.call(FitDL.Gu, c(params, method = "nlminb")) #nlm, ucminf
# if ('Klos' %in% methods) fit.Klos <- do.call(FitDL.Klos, c(params, method = "BFGS")) #BFGS, Nelder-Mead, L-BFGS-B
# if ('Zhang' %in% methods) fit.Zhang <- do.call(FitDL.Zhang, c(params, method = "nlminb")) #nlm
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