smooth_wWHIT  R Documentation 
Weigthed Whittaker Smoother
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 
scaler or numeric vector, whittaker parameter.

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
Whittaker smoother of the second order difference is used!
Eilers, P.H.C., 2003. A perfect smoother. Anal. Chem. doi: 10.1021/ac034173t
Frasso, G., Eilers, P.H.C., 2015. L and Vcurves for optimal smoothing. Stat. Modelling 15, 91111. doi: 10.1177/1471082X14549288.
lambda_vcurve()
data("MOD13A1") dt < tidy_MOD13(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$ylu, nptperyear = 23, iters = 2) ## Optimize `lambda` by Vcurve theory # (a) optimize manually lambda_vcurve(l$y, l$w, plot = TRUE) # (b) optimize automatically by setting `lambda = NULL` in smooth_wWHIT r_wWHIT2 < smooth_wWHIT(l$y, l$w, l$ylu, nptperyear = 23, iters = 2, lambda = NULL) #
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