Calculates a nonparametric statistic for a monotonic trend based on the Kendall tau statistic and the Theil-Sen slope modification
raster.kendall(
x,
intercept = FALSE,
p.value = FALSE,
z.value = FALSE,
confidence = FALSE,
tau = FALSE,
...
)
Depending on arguments, a raster layer or rasterBrick object containing:
raster layer 1 slope for trend, always returned
raster layer 2 intercept for trend if intercept TRUE
raster layer 3 p value for trend fit if p.value TRUE
raster layer 4 z value for trend fit if z.value TRUE
raster layer 5 lower confidence level at 95 pct, if confidence TRUE
raster layer 6 upper confidence level at 95 pct, if confidence TRUE
raster layer 7 Kendall's tau two-sided test, reject null at 0, if tau TRUE
A rasterStack object with at least 5 layers
(FALSE/TRUE) return a raster with the pixel wise intercept values
(FALSE/TRUE) return a raster with the pixel wise p.values
(FALSE/TRUE) return a raster with the pixel wise z.values
(FALSE/TRUE) return a raster with the pixel wise 95 pct confidence levels
(FALSE/TRUE) return a raster with the pixel wise tau correlation values
Additional arguments passed to the raster overlay function
Jeffrey S. Evans <jeffrey_evans@tnc.org>
This function implements Kendall's nonparametric test for a monotonic trend using the Theil-Sen (Theil 1950; Sen 1968; Siegel 1982) method to estimate the slope and related confidence intervals.
Theil, H. (1950) A rank invariant method for linear and polynomial regression analysis. Nederl. Akad. Wetensch. Proc. Ser. A 53:386-392 (Part I), 53:521-525 (Part II), 53:1397-1412 (Part III).
Sen, P.K. (1968) Estimates of Regression Coefficient Based on Kendall's tau. Journal of the American Statistical Association. 63(324):1379-1389.
Siegel, A.F. (1982) Robust Regression Using Repeated Medians. Biometrika, 69(1):242-244
kendallTrendTest
for model details
overlay
for available ... arguments
# \donttest{
library(raster)
r.logo <- stack(system.file("external/rlogo.grd", package="raster"),
system.file("external/rlogo.grd", package="raster"),
system.file("external/rlogo.grd", package="raster"))
# Calculate trend slope with p-value and confidence level(s)
# ("slope","intercept", "p.value","z.value", "LCI","UCI","tau")
k <- raster.kendall(r.logo, p.value=TRUE, z.value=TRUE,
intercept=TRUE, confidence=TRUE,
tau=TRUE)
plot(k)
# }
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