By Vondracek Z.
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Extra info for A Characterization of Brownian Motion in a Lipschitz Domain by Its Killing Distributions
Xn } where xi ∈ W are the individual points of the process, and the total number of points n ≥ 0 is not ﬁxed. 1 Formulation of Models The point process models ﬁtted in spatstat are Gibbs point processes, cf. [5, 47, 51]. e. we are not restricted to pairwise interactions), and dependence on marks. Each model will be speciﬁed in terms of its conditional intensity rather than its likelihood. This turns out to be an intuitively appealing way to formulate point process models, as well as being necessary for technical reasons.
Suppose, for example, that we have data for a point pattern observed in the rectangle [0, 10] × [0, 4]. Assume the Cartesian coordinates of the points are stored in R as vectors x and y. Then the command X <- ppp(x, y, c(0,10), c(0,4)) creates a point pattern object containing this information. 2 Initial Inspection of Data Chatﬁeld  emphasises the importance of careful initial inspection of data. The same principles apply to point pattern data. g. categorical or continuous); inconsistency with plots of the same data in the original source publication; coarse rounding of the Cartesian coordinates; use of values such as 99 or −1 to indicate a missing value; incorrect software translation of the levels of a factor; and duplicated points.
Journal of Applied Probability, 21:575–582, 1984. R. Mecke and D. Stoyan. Morphological characterization of point patterns. Biometrical Journal, 47, 2005.  J. P. Waagepetersen. Statistical Inference and Simulation for Spatial Point Processes. London: Chapman and Hall, 2004. D. Ripley. Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society B, 39:172–212, 1977. D. Ripley. Spatial Statistics, John Wiley & Sons, New York, 1981. D. Ripley. Statistical Inference for Spatial Processes, Cambridge University Press, Cambridge, 1988.