TrendSeasonalAdjusted {greenbrown}  R Documentation 
The function computes and substracts the seasonal cycle from a time series. Then a trend is estimated on the seasonaladjusted time series. The function can be applied to gridded (raster) data using the function TrendRaster
. A detailed description of this method can be found in Forkel et al. (2013).
TrendSeasonalAdjusted(Yt, mosum.pval = 0.05, h = 0.15, breaks = NULL, funSeasonalCycle = MeanSeasonalCycle)
Yt 
univariate time series of class 
mosum.pval 
Maximum pvalue for the OLSMOSUM test in order to search for breakpoints. If p = 0.05, breakpoints will be only searched in the time series trend component if the OLSMOSUM test indicates a significant structural change in the time series. If p = 1 breakpoints will be always searched regardless if there is a significant structural change in the time series or not. See 
h 
minimal segment size either given as fraction relative to the sample size or as an integer giving the minimal number of observations in each segment. See 
breaks 
maximal number of breaks to be calculated (integer number). By default the maximal number allowed by h is used. See 
funSeasonalCycle 
a function to estimate the seasonal cycle of the time series. A own function can be defined to estimate the seasonal cycle which has to return the seasonal cycle as a time series of class "ts". Currently two approaches are part of this package:

The function returns a list of class "Trend".
Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]
Forkel, M., N. Carvalhais, J. Verbesselt, M. Mahecha, C. Neigh and M. Reichstein (2013): Trend Change Detection in NDVI Time Series: Effects of InterAnnual Variability and Methodology.  Remote Sensing 5.
Trend
, TrendRaster
, MeanSeasonalCycle
, SSASeasonalCycle
# load a time series of NDVI (normalized difference vegetation index) data(ndvi) plot(ndvi) # calculate trend on time series with removed mean seasonal cycle MACtrend < TrendSeasonalAdjusted(ndvi, funSeasonalCycle=MeanSeasonalCycle) MACtrend plot(MACtrend) # plot the seasonaladjusted time series plot(ndvi) lines(MACtrend$adjusted, col="orange") # calculate trend on time series with removed mean seasonal cycle # but with limited number of breakpoints MACtrend < TrendSeasonalAdjusted(ndvi, breaks=1, funSeasonalCycle=MeanSeasonalCycle) MACtrend plot(MACtrend) ## calculate trend on time series with removed seasonal cycle but seasonal cycle computed based ## on singular spectrum analysis #SSAtrend < TrendSeasonalAdjusted(ndvi, funSeasonalCycle=SSASeasonalCycle) #SSAtrend #plot(SSAtrend) #lines(SSAtrend$adjusted, col="orange")