sens.slope: Sen's slope

In trend: Non-Parametric Trend Tests and Change-Point Detection

Sen's slope

Description

Computes Sen's slope for linear rate of change and corresponding confidence intervalls

Usage

sens.slope(x, conf.level = 0.95)

Arguments

x	numeric vector or a time series object of class "ts"
conf.level	numeric, the level of significance

Details

This test computes both the slope (i.e. linear rate of change) and confidence levels according to Sen's method. First, a set of linear slopes is calculated as follows:

d_{k} = \frac{x_j - x_i}{j - i}

for \left(1 \le i < j \le n \right), where d is the slope, x denotes the variable, n is the number of data, and i, j are indices.

Sen's slope is then calculated as the median from all slopes: b_{Sen} = \textnormal{median}(d_k).

This function also computes the upper and lower confidence limits for sens slope.

Value

A list of class "htest".

estimates	numeric, Sen's slope
data.name	character string that denotes the input data
p.value	the p-value
statistic	the z quantile of the standard normal distribution
null.value	the null hypothesis
conf.int	upper and lower confidence limit
alternative	the alternative hypothesis
method	character string that denotes the test

Note

Current Version is for complete observations only.

References

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

Sen, P.K. (1968), Estimates of the regression coefficient based on Kendall's tau, Journal of the American Statistical Association 63, 1379–1389.

Examples

data(maxau)
sens.slope(maxau[,"s"])
mk.test(maxau[,"s"])

trend documentation built on Oct. 10, 2023, 9:06 a.m.