Description Usage Arguments Value Author(s) See Also Examples
This function can be used for pre-processing of time series before the analyzing phenology or trends. The pre-processing involves the following steps:
Step 1. Filling of permanent gaps. Values that are missing in each year will be filled using the function FillPermanentGaps
.
Step 2. Temporal smoothing, gap filling and interpolation. The time series will be smoothed and remaining gaps will be filled. Optionally, time series will be interpolated to daily values.
1 2 3 4 5 6 7 8 | TsPP(Yt, fpg = FillPermanentGaps,
tsgf = TSGFspline,
interpolate = FALSE,
min.gapfrac = 0.2,
lower = TRUE, fillval = NA,
fun = min, backup = NULL,
check.seasonality = 1:3,
...)
|
Yt |
univariate time series of class |
fpg |
Filling of permanent gaps: If NULL, permanent gaps will be not filled, else the function |
tsgf |
Temporal smoothing and gap filling: Function to be used for temporal smoothing, gap filling and interpolation of the time series. If NULL, this step will be not applied. Otherwise a function needs to be specified. Exisiting functions that can be applied are |
interpolate |
Should the smoothed and gap filled time series be interpolated to daily values? |
min.gapfrac |
How often has an observation to be NA to be considered as a permanent gap? (fraction of time series length) Example: If the month January is 5 times NA in a 10 year time series (= 0.5), then the month January is considered as permanent gap if |
lower |
For filling of permanent gaps: fill |
fillval |
For filling of permanent gaps: constant fill values for gaps. If NA the fill value will be estimated from the data using |
fun |
For filling of permanent gaps: function to be used to compute fill values. By default, minimum. |
backup |
Which |
check.seasonality |
Which methods in |
... |
further arguments (currently not used) |
pre-processed time series
Matthias Forkel <matthias.forkel@tu-dresden.de> [aut, cre]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # introduce systematic gaps in winter and random gaps
gaps <- ndvi
gaps[runif(50, 1, length(ndvi))] <- NA
gaps[cycle(ndvi) == 1 | cycle(ndvi) == 2 | cycle(ndvi) == 12] <- NA
plot(gaps)
# perform pre-processing of time series using different methods
pp.lin <- TsPP(gaps, tsgf=TSGFlinear) # linear interpolation + running median
pp.spl <- TsPP(gaps, tsgf=TSGFspline) # smoothing splines
plot(gaps)
cols <- rainbow(5)
lines(pp.lin, col=cols[1])
lines(pp.spl, col=cols[2])
data.df <- ts.union(time(gaps), orig=ndvi, pp.lin, pp.spl)
plot(data.df)
cor(na.omit(data.df[is.na(gaps),]))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.