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Clustering of times series of maxima based on the pam package 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.
x a matrix of maxima. For example, number of stations = ncol(x) and time series length = nrow(x) for weekly maxima of precipitation.
number of clusters
number of resampling for which the stations are randomly moved to break the dependence. By default, J=0 means no resampling.
Threshold corresponding to the quantile level for the resampling. The resulting quantile is printed (when J does not take value 0).
A threshold to remove very small values. For example, some raingauges cannot go below 2 mm. By default, max.min=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., A. Guillou, D. Cooley, and J. Diebolt (2009). Modeling pairwise dependence of maxima in space. Biometrika 96(1).
Cooley, D., P. Naveau, and P. Poncet (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.
See the function as pam in the package cluster
data(PrecipFrance)
attach(PrecipFrance)
PAMmado <- PAMfmado(precip,7)
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