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SeasonalMannKendall

Mann-Kendall trend test for monthly environmental time series


Description

Hirst et al. (1982) suggested this test for monthly water quality time series. The test is also discussed by Hipel and McLeod (2005).

The score is computed separately for each month.

The purpose of this test is to test for monotonic trend. A common misconception is to look for trends in the individual monthly time series. Usually this does not make a lot of sense, in the context of environmental time series, since if there is a real trend of interest in the series it would not be expected to be greatly changed by seasonality. If indeed one were interested in detecting a trend a particular month then one could use the MannKendall trend test for that particular month or group of months.

Usage

SeasonalMannKendall(x)

Arguments

x

a vector or a time series comprised of consecutive monthly values

Value

A list with class Kendall.

tau

Kendall's tau statistic

sl

two-sided p-value

S

Kendall Score

D

Denominator, tau=S/D

varS

variance of S

Generic function print.Kendall and summary.Kendall are provided.

Note

If you want to use the output from SeasonalMannKendall, save the result as in res<-SeasonalMannKendall(x,y) and then select from the list res the value(s) needed.

Author(s)

A.I. McLeod, aimcleod@uwo.ca

References

Hirsch, R.M., Slack, J.R. and Smith, R.A. (1982), Techniques for trend assessment for monthly water quality data, Water Resources Research 18, 107-121.

Hipel, K.W. and McLeod, A.I., (2005). Time Series Modelling of Water Resources and Environmental Systems. Electronic reprint of our book orginally published in 1994. http://www.stats.uwo.ca/faculty/aim/1994Book/.

See Also

Examples

#test for monotonic trend in monthly average river height data
#for the Rio Negro at Manaus. This data is included in the 
#package boot.
library(boot) 
data(manaus)
SeasonalMannKendall(manaus)

Kendall

Kendall rank correlation and Mann-Kendall trend test

v2.2
GPL (>= 2)
Authors
A.I. McLeod
Initial release
2011-05-16

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