Sen's slope
Computes Sen's slope for linear rate of change and corresponding confidence intervalls
sens.slope(x, conf.level = 0.95)
x |
numeric vector or a time series object of class "ts" |
conf.level |
numeric, the level of significance |
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) = (x(j) - x(i)) / (j - i)
for (1 <= i < j <= n), 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 = Median(d(k)).
This function also computes the upper and lower confidence limits for sens slope.
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 |
Current Version is for complete observations only.
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.
data(maxau) sens.slope(maxau[,"s"]) mk.test(maxau[,"s"])
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