spatialEco (version 1.1-1)

raster.kendall: Kendall tau trend with continuity correction for raster time-series

Description

Calculates a nonparametric statistic for a monotonic trend based on the Kendall tau statistic and the Theil-Sen slope modification

Usage

raster.kendall(x, tau = FALSE, intercept = FALSE, p.value = FALSE,
  z.value = FALSE, confidence = FALSE, autocorrelation = FALSE, ...)

Arguments

x

A rasterStack object with at least 5 layers

tau

(FALSE/TRUE) return a raster with the pixel wise tau values

intercept

(FALSE/TRUE) return a raster with the pixel wise intercept values

p.value

(FALSE/TRUE) return a raster with the pixel wise p.values

z.value

(FALSE/TRUE) return a raster with the pixel wise z values

confidence

(FALSE/TRUE) return a raster with the pixel wise 95 pct confidence levels

autocorrelation

(FALSE/TRUE) Apply autocorrelation correction using prewhitening

...

Additional arguments passed to the raster overlay function

Value

Depending on arguments, a raster layer or rasterBrick object containing:

• raster layer 1 Theil-Sen slope, always returned

• raster layer 2 Kendall's tau two-sided test, if tau TRUE

• raster layer 3 intercept for trend if intercept TRUE, not if prewhitened

• raster layer 4 p value for trend fit if p.value TRUE

• raster layer 5 Z value for trend fit if z.value TRUE

• raster layer 6 lower confidence level at 95-pct if confidence TRUE, not if prewhitened

• raster layer 7 upper confidence level at 95-pct if confidence TRUE, not if prewhitened

Details

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. Critical values are Z > 1.96 representing a significant increasing trend and a Z < -1.96 a significant decreasing trend (p < 0.05). The null hypothesis can be rejected if Tau = 0. There is also an option for autocorrelation correction using the method proposed in Yue & Wang (2002).

References

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

Yue, S., & Wang, C. Y. (2002). Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. Water Resources Research, 38(6):41-47.

See Also

kendallTrendTest for model details

overlay for available ... arguments

Examples

# NOT RUN {
 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)
 logo.trend <- raster.kendall(r.logo, tau = TRUE, intercept = TRUE,  p.value = TRUE,
                              z.value = TRUE, confidence = TRUE)
   names(logo.trend) <- c("slope","tau", "intercept", "p.value", "z.value", "LCI", "UCI")
     plot(logo.trend)
  	  	  
# }
# NOT RUN {
# }