Weigthed Whittaker Smoother

smooth_wWHIT(
  y,
  w,
  ylu,
  nptperyear,
  wFUN = wTSM,
  iters = 1,
  lambda = 15,
  second = FALSE,
  ...
)

Arguments

y	Numeric vector, vegetation index time-series
w	(optional) Numeric vector, weights of `y`. If not specified, weights of all `NA` values will be `wmin`, the others will be 1.0.
ylu	`[low, high]` of time-series y (curve fitting values are constrained in the range of `ylu`.
nptperyear	Integer, number of images per year.
wFUN	weights updating function, can be one of 'wTSM', 'wChen' and 'wBisquare'.
iters	How many times curve fitting is implemented.
lambda	whittaker parameter (2-15 is suitable for 16-day VI). Multiple lambda values also are accept, then a list object return.
second	If true, in every iteration, Whittaker will be implemented twice to make sure curve fitting is smooth. If curve has been smoothed enough, it will not care about the second smooth. If no, the second one is just prepared for this situation. If lambda value has been optimized, second smoothing is unnecessary.
...	Additional parameters are passed to `wFUN`.

Value

ws: weights of every iteration
zs: curve fittings of every iteration

References

Eilers, P.H.C., 2003. A perfect smoother. Anal. Chem. https://doi.org/10.1021/ac034173t
Frasso, G., Eilers, P.H.C., 2015. L- and V-curves for optimal smoothing. Stat. Modelling 15, 91-111. https://doi.org/10.1177/1471082X14549288

Examples

library(phenofit)
#> Error in library(phenofit): there is no package called ‘phenofit’
data("MOD13A1")
#> Warning: data set ‘MOD13A1’ not found
dt <- tidy_MOD13(MOD13A1$dt)
#> Error in tidy_MOD13(MOD13A1$dt): object 'MOD13A1' not found
d <- dt[site == "AT-Neu", ]
#> Error in eval(expr, envir, enclos): object 'site' not found

l <- check_input(d$t, d$y, d$w, nptperyear=23)
#> Error in check_input(d$t, d$y, d$w, nptperyear = 23): object 'd' not found
r_wWHIT <- smooth_wWHIT(l$y, l$w, l$ylu, nptperyear = 23, iters = 2)
#> Error in smooth_wWHIT(l$y, l$w, l$ylu, nptperyear = 23, iters = 2): object 'l' not found