Kendall correlation and trend tests.

Description

Compute Kendall's rank correlation and various trend tests.

Details

If you just want to compute Kendall's tau or its signficance level, the base function cor and cor.test are recommended. The purpose of this package is to implement the Mann-Kendall test, the seasonal Mann-Kendall trend test as well as computing the Kendall score. See documentation for Mann-Kendall on using the block bootstrap to deal with autocorrelation.

Author(s)

A. I. McLeod

Maintainer: aimcleod@uwo.ca

References

Hipel, K.W. and McLeod, A.I., (1994). Time Series Modelling of Water Resources and Environmental Systems.

See Also

cor, cor.test, print.Kendall, summary.Kendall, MannKendall, SeasonalMannKendall

Examples

library(boot)
data(PrecipGL)
MKtau<-function(z) MannKendall(z)$tau
tsboot(PrecipGL, MKtau, R=500, l=5, sim="fixed")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Kendall)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Kendall/Kendall-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Kendall-package
> ### Title: Kendall correlation and trend tests.
> ### Aliases: Kendall-package
> ### Keywords: ts package
> 
> ### ** Examples
> 
> library(boot)
> data(PrecipGL)
> MKtau<-function(z) MannKendall(z)$tau
> tsboot(PrecipGL, MKtau, R=500, l=5, sim="fixed")

BLOCK BOOTSTRAP FOR TIME SERIES

Fixed Block Length of 5 

Call:
tsboot(tseries = PrecipGL, statistic = MKtau, R = 500, l = 5, 
    sim = "fixed")


Bootstrap Statistics :
     original     bias    std. error
t1* 0.2645801 -0.2717591  0.08312208
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>