phenofit (version 0.3.2)

smooth_wWHIT: Weigthed Whittaker Smoother

Description

Weigthed Whittaker Smoother

Usage

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

Value

• ws: weights of every iteration

• zs: curve fittings of every iteration

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.

References

1. Eilers, P.H.C., 2003. A perfect smoother. Anal. Chem. https://doi.org/10.1021/ac034173t
   

2. 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)
data("MOD13A1")
dt <- tidy_MOD13(MOD13A1$dt)
d <- dt[site == "AT-Neu", ]

l <- check_input(d$t, d$y, d$w, nptperyear=23)
r_wWHIT <- smooth_wWHIT(l$y, l$w, l$ylu, nptperyear = 23, iters = 2)