Description Usage Arguments Details Value Author(s) See Also Examples
This function extracts along a spatial gradient (e.g. along latitude) time series from a raster brick and computes for each position a temporal trend.
1 2 3 4 5 6 7 8 9 10 11 12 | TrendGradient(r, start = c(1982,
1), freq = 12, gradient.r = NULL,
gradient.brks = NULL,
funSpatial = "mean",
cor.area = FALSE,
scalar = 1, method = c("AAT",
"STM", "SeasonalAdjusted",
"RQ"), mosum.pval = 0.05,
h = 0.15, breaks = 0,
funAnnual = mean,
funSeasonalCycle = MeanSeasonalCycle,
percent = FALSE)
|
r |
multi-layer raster object of class |
start |
beginning of the time series (i.e. the time of the first observation). The default is c(1982, 1), i.e. January 1982 which is the usual |
freq |
The frequency of observations. The default is 12 for monthly observations. Use 24 for bi-monthly observations, 365 for daily observations or 1 for annual observations. See |
gradient.r |
raster layer with the variable that has a spatial gradient. If NULL (default) a gradient along latitude will be used. Alternatively, one could provide here for example a raster layer with a gradient along longitude for longitudinal gradients of trends or a raster layer with mean annual temperatures to compute trends along a temperature gradient. |
gradient.brks |
|
funSpatial |
function that should be used for spatial aggregation of grid cells that belong to the same interval. |
cor.area |
If TRUE grid cell values are multiplied by grid cell area to correct for area. |
scalar |
Multiplier to be applied to time series (e.g. for unit conversions). |
method |
|
mosum.pval |
Maximum p-value for the OLS-MOSUM test in order to search for breakpoints. If p = 0.05, breakpoints will be only searched in the time series trend component if the OLS-MOSUM 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 |
funAnnual |
function to aggregate time series to annual values if |
funSeasonalCycle |
a function to estimate the seasonal cycle of the time series if
|
percent |
return trend as percentage change |
The function returns a list of class 'TrendGradient'
The function returns a data.frame of class 'TrendGradient'.
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 19 20 21 22 | # # compute a latitudinal gradient of trends (by default the method 'AAT' is used):
# gradient <- TrendGradient(ndvimap, start=c(1982, 1), freq=12)
# gradient
# plot(gradient)
# # shown is the trend at each latitudinal band, the area represents the 95%
# # confidence interval of the trend (computed with function TrendUncertainty),
# # symbols indicate the p-value of the trend at each latitude
#
# plot(gradient, type="yx") # the gradient can be also plotted in reversed order
#
# # latitudinal gradient with different number of intervals:
# gradient <- TrendGradient(ndvimap, start=c(1982, 1), freq=12,
# gradient.brks=seq(66, 69, length=5))
# plot(gradient)
#
# # example for a longitudinal gradient:
# gradient.r <- raster(ndvimap, 1) # create a raster layer with longitudes:
# gradient.r[] <- xFromCell(gradient.r, 1:ncell(gradient.r))
# plot(gradient.r)
# gradient <- TrendGradient(ndvimap, start=c(1982, 1), freq=12,
# gradient.r=gradient.r)
# plot(gradient, xlab="Longitude (E)")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.