spatstat: AnRPackage for Analyzing Spatial Point Patterns | Zendy

Adrian Baddeley | Zendy; Rolf Turner | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

spatstat: AnRPackage for Analyzing Spatial Point Patterns

Author(s) -

Adrian Baddeley,

Rolf Turner

Publication year - 2005

Publication title -

journal of statistical software

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.636

H-Index - 145

ISSN - 1548-7660

DOI - 10.18637/jss.v012.i06

Subject(s) - computer science , r package , point (geometry) , covariate , spatial analysis , sampling (signal processing) , exploratory data analysis , data mining , point pattern analysis , point process , statistics , mathematics , common spatial pattern , machine learning , computational science , geometry , computer vision , filter (signal processing)

summary:The pure and modified Bayesian methods are applied to the estimation of parameters of the Neyman-Scott point process. Their performance is compared to the fast, simulation-free methods via extensive simulation study. Our modified Bayesian method is found to be on average 2.8 times more accurate than the fast methods in the relative mean square errors of the point estimates, where the average is taken over all studied cases. The pure Bayesian method is found to be approximately as good as the fast methods. These methods are computationally affordable today

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research