Test if an increasing or decreasing trend exists in a time series

Test if the series has an increasing or decreasing trend, using a non-parametric Spearman test between the observations and time

trend.test(tseries, R=1)

Arguments

tseries: a univariate or multivariate time series (a 'rts' object in Splus or a 'ts' object in R)
R: The number of time the series is/are resampled for a bootstrap test. If R1 (by default), an usual Spearman test is performed. If R > 1 then a bootstrap test is run

Value

A 'htest' object if R=1, a 'boot' object with an added boot$p.value item otherwise

References

Siegel, S. & N.J. Castellan, 1988. Non-parametric statistics. McGraw-Hill, New York. 399 pp.

Author

Frédéric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean (phgrosjean@sciviews.org)

Note

In both cases (normal test with R=1 and bootstrap test), the p-value can be obtained from obj$p.value (see examples)

Examples

data(marbio)
trend.test(marbio[, 8])
#> Warning: Cannot compute exact p-value with ties
#> 
#> 	Spearman's rank correlation rho
#> 
#> data:  marbio[, 8] and time(marbio[, 8])
#> S = 43853, p-value = 0.1841
#> alternative hypothesis: true rho is not equal to 0
#> sample estimates:
#>       rho 
#> 0.1630113 
#> 
# Run a bootstrap test on the same series
marbio8.trend.test <- trend.test(marbio[, 8], R=99)
# R=999 is a better value... but it is very slow!
marbio8.trend.test  
#> 
#> BLOCK BOOTSTRAP FOR TIME SERIES
#> 
#> Fixed Block Length of 1 
#> 
#> Call:
#> tsboot(tseries = x, statistic = test.trend, R = R, l = 1, sim = "fixed")
#> 
#> 
#> Bootstrap Statistics :
#>      original    bias    std. error
#> t1* 0.1630113 -0.140559   0.1213218
plot(marbio8.trend.test)

marbio8.trend.test$p.value
#> [1] 0.2424242