curvefit: Fine curve fitting

In phenofit: Extract Remote Sensing Vegetation Phenology

Fine curve fitting

Description

Curve fit vegetation index (VI) time-series of every growing season using fine curve fitting methods.

Usage

curvefit(
  y,
  t = index(y),
  tout = t,
  methods = c("AG", "Beck", "Elmore", "Gu", "Klos", "Zhang"),
  w = NULL,
  ...,
  type = 1L,
  use.cpp = FALSE
)

Arguments

y	Vegetation time-series index, numeric vector
t	The corresponding doy of x
tout	The output interpolated time.
methods	Fine curve fitting methods, can be one or more of c('AG', 'Beck', 'Elmore', 'Gu', 'Klos', 'Zhang').
w	(optional) Numeric vector, weights of y. If not specified, weights of all NA values will be wmin, the others will be 1.0.
...	other parameters passed to curve fitting function.
type	integer, 1 or -1 1: trough-to-trough curve fitting -1: peak-to-peak curve fitting
use.cpp	(unstable, not used) boolean, whether to use c++ defined fine fitting function? If FALSE, R version will be used.

Value

fFITs S3 object, see fFITs() for details.

Note

'Klos' have too many parameters. It will be slow and not stable.

See Also

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)

phenofit documentation built on Feb. 16, 2023, 6:21 p.m.