Performs a bootstrapped version of the Mann-Kendall trend test to adjust for serial correlation in annual maximum series data. The BBMK test uses Spearman’s serial correlation test to identify the least insignificant lag, then applies a shuffling procedure to obtain the empirical p-value and confidence bounds for the Mann-Kendall test statistic.
eda_bbmk_test(data, alpha = 0.05, samples = 10000L)A list containing the test results, including:
data: The data argument.
alpha: The significance level as specified in the alpha argument.
null_hypothesis: A string describing the null hypothesis.
alternative_hypothesis: A string describing the alternative hypothesis.
statistic: The Mann-Kendall S-statistic computed on the original series.
bootstrap: Vector of bootstrapped Mann-Kendall test statistics.
p_value: Empirical two-sided p-value derived from the bootstrap distribution.
bounds: Empirical confidence interval bounds from the bootstrap distribution.
reject: If TRUE, the null hypothesis was rejected at significance alpha.
Numeric vector of observed annual maximum series values. Must be strictly positive, finite, and not missing.
Numeric scalar in \([0.01, 0.1]\). The significance level for confidence intervals or hypothesis tests. Default is 0.05.
Integer scalar. The number of bootstrap samples. Default is 10000.
The block size for reshuffling is equal to the least_lag as estimated in
eda_spearman_test(). Bootstrap samples are generated by resampling blocks
of the original data without replacement. This procedure effectively removes
serial correlation from the data.
Bayazit, M., 2015. Nonstationarity of hydrological records and recent trends in trend analysis: a state-of-the-art review. Environmental Processes 2 (3), 527–542. tools:::Rd_expr_doi("10.1007/s40710-015-0081-7")
Khaliq, M.N., Ouarda, T.B.M.J., Gachon, P., Sushama, L., St-Hilaire, A., 2009. Identification of hydrological trends in the presence of serial and cross correlations: a review of selected methods and their application to annual flow regimes of Canadian rivers. Journal Hydrolology 368 (1–4), 117–130. tools:::Rd_expr_doi("10.1016/j.jhydrol.2009.01.035")
Sonali, P., Nagesh Kumar, D., 2013. Review of trend detection methods and their application to detect temperature changes in India. Journal Hydrology 476, 212–227. tools:::Rd_expr_doi("10.1016/j.jhydrol.2012.10.034")
plot_bbmk_test(), eda_mk_test(), eda_spearman_test()
data <- rnorm(n = 100, mean = 100, sd = 10)
eda_bbmk_test(data, samples = 1000L)
Run the code above in your browser using DataLab