Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns replicated point pattern data using the function mppm. The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Baddeley and R. Spatstat: an R package for analyzing spatial point patterns Journal of Statistical Software 6 Modelling spatial point patterns in R. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan Lecture Notes in Statistics New York: Springer-Verlag ISBN: Baddeley, R.
A model can be fitted to a list of point patterns replicated point pattern data using the function mppm. The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported likelihood ratio test, analysis of deviance, Monte Carlo tests along with basic tools for model selection stepwise , AIC and variable selection sdr.
Spacing Introduction Basic methods Nearest-neighbour function G and empty-space function F Confidence intervals and simulation envelopes Empty-space hazard J -function Inhomogeneous F -, G - and J -functions Anisotropy and the nearest-neighbour orientation Empty-space distance for a spatial pattern Distance from a point pattern to another spatial pattern Theory for edge corrections Palm distribution FAQ.
Poisson Models download pdf Introduction Poisson point process models Fitting Poisson models in spatstat Statistical inference for Poisson models Alternative fitting methods More flexible models Theory Coarse quadrature approximation Fine pixel approximation Conditional logistic regression Approximate Bayesian inference Non-loglinear models Local likelihood FAQ. Hypothesis Tests and Simulation Envelopes Introduction Concepts and terminology Testing for a covariate effect in a parametric model Quadrat counting tests Tests based on the cumulative distribution function Monte Carlo tests Monte Carlo tests based on summary functions Envelopes in spatstat Other presentations of envelope tests Dao-Genton test and envelopes Power of tests based on summary functions FAQ.
Model Validation Overview of validation techniques Relative intensity Residuals for Poisson processes Partial residual plots Added variable plots Validating the independence assumption Leverage and influence Theory for leverage and influence FAQ. Higher-Dimensional Spaces and Marks Introduction Point patterns with numerical or multidimensional marks Three-dimensional point patterns Point patterns with any kinds of marks and coordinates FAQ.
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