Description Usage Arguments Value References Examples
Weigthed Whittaker Smoother
1 2 3 4 5 6 7 8 9 10 11  smooth_wWHIT(
y,
w,
ylu,
nptperyear,
wFUN = wTSM,
iters = 1,
lambda = 15,
second = FALSE,
...
)

y 
Numeric vector, vegetation index timeseries 
w 
(optional) Numeric vector, weights 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 (215 is suitable for 16day 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 
ws
: weights of every iteration
zs
: curve fittings of every iteration
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 Vcurves for optimal smoothing. Stat. Modelling 15, 91–111. https://doi.org/10.1177/1471082X14549288
1 2 3 4 5 6 7  library(phenofit)
data("MOD13A1")
dt < tidy_MOD13.gee(MOD13A1$dt)
d < dt[site == "ATNeu", ]
l < check_input(d$t, d$y, d$w, nptperyear=23)
r_wWHIT < smooth_wWHIT(l$y, l$w, l$ylu, nptperyear = 23, iters = 2)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.