| plot.TrendGradient {greenbrown} | R Documentation |
This function plots a gradient of trend slopes (e.g. latitudinal gradient).
## S3 method for class 'TrendGradient'
plot(x, type = "xy", ylab = NULL, xlab = NULL,
col = "black", ylim = NULL, xlim = NULL, add = FALSE, symbolic = TRUE,
symbols = "standard", ...)
x |
Object of class 'TrendGradient' as returned from function |
type |
plotting type: 'xy' = gradient at x axis and slope at y axis, 'yx' = gradient at y axis and slope at x axis. |
ylab |
A title for the y axis |
xlab |
A title for the x axis |
col |
line colors |
ylim |
limits for y axis |
xlim |
limits for x axis |
add |
add to exisiting plot? |
symbolic |
Add p-value as symbols (TRUE) or not (FALSE). If TRUE the p-value of a trend slope is added as symbol to the plot. |
symbols |
Type of symbols for p-values. "standard": *** (p <= 0.001), ** (p <= 0.01), * (p <= 0.05), . (p <= 0.1) and no symbol if p > 0.1.; "simple": * (p <= 0.05), x (p < 0.1) |
... |
Further arguments that can be passed |
Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]
# load a raster dataset of Normalized Difference Vegetation Index data(ndvimap) plot(ndvimap, 8) # 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 # compute gradients with different trend methods gradient.mac <- TrendGradient(ndvimap, start=c(1982, 1), freq=12, method="SeasonalAdjusted", funSeasonalCycle=MeanSeasonalCycle) plot(gradient.mac, col="blue", ylab="NDVI trend (month-1)") # method AAT uses annual time steps, convert years -> months gradient$Slope <- gradient$Slope / 12 gradient$SlopeUncLower <- gradient$SlopeUncLower / 12 gradient$SlopeUncUpper <- gradient$SlopeUncUpper / 12 gradient$SlopeUncMedian <- gradient$SlopeUncMedian / 12 plot(gradient, col="red", add=TRUE)