Spatial Point Patterns: Methodology and Applications with RModern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on th |
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Table des matières
II EXPLORATORY DATA ANALYSIS | 155 |
III STATISTICAL INFERENCE | 297 |
IV ADDITIONAL STRUCTURE | 635 |
Index of Notation | 745 |
Bibliography | 751 |
791 | |
Back Cover | 811 |
Autres éditions - Tout afficher
Spatial Point Patterns: Methodology and Applications with R Adrian Baddeley,Ege Rubak,Rolf Turner Aucun aperçu disponible - 2015 |
Expressions et termes fréquents
analysis argument cluster process column completely random computed conditional intensity confidence intervals covariate Cox process cumulative distribution function data frame data points default depends distribution edge correction envelopes estimate example fitted model formula Gaussian random field geological faults Gibbs models homogeneous Poisson process hyperframe independent inhomogeneous intensity function K-function kernel left panel linear network logistic regression loglinear lurking variable marks method metres Monte Carlo test multitype point pattern null hypothesis number of points object of class observed p-value package pair correlation function panel of Figure pixel image pixel values point process model points of type Poisson distribution Poisson model Poisson point process Poisson process probability density pseudolikelihood quadrat counts random point region result right panel sampling Section smoothing spatial location spatial point pattern spatial point processes spatially varying spatstat specified stationary Strauss process summary function tessellation trees trend variable vector water striders window