Performs clustering of time series of maxima using the pam algorithm
tailored for the F-madogram distance.
PAMfmado(x, K, J = 0, threshold = 0.99, max.min = 0)An object of class "pam" representing the clustering.
See pam.object for details.
A matrix of maxima. For example, for weekly maxima of precipitation,
the number of stations is ncol(x) and the time series length is nrow(x).
Number of clusters.
Number of resamplings for which the stations are randomly moved to break
the dependence. By default, J = 0 means no resampling.
Quantile level used for the resampling threshold.
The corresponding quantile is printed (when J is not 0).
A lower threshold to remove very small values.
For example, some rain gauges cannot go below 2 mm. Default is 0.
Philippe Naveau
Bernard, E., Naveau, P., Vrac, M. and Mestre, O. (2013). Clustering of maxima: Spatial dependencies among heavy rainfall in France. Journal of Climate 26, 7929--7937.
Naveau, P., Guillou, A., Cooley, D. and Diebolt, J. (2009). Modeling pairwise dependence of maxima in space. Biometrika 96(1).
Cooley, D., Naveau, P. and Poncet, P. (2006). Variograms for spatial max-stable random fields. In: Bertail, P., Soulier, P., Doukhan, P. (eds) Dependence in Probability and Statistics. Lecture Notes in Statistics, vol 187. Springer, New York, NY.
Reynolds, A., Richards, G., de la Iglesia, B. and Rayward-Smith, V. (1992). Clustering rules: A comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms 5, 475--504.
data(PrecipFrance)
PAMmado <- PAMfmado(PrecipFrance$precip, 7)
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