spatstat.core

This package is marked for deletion!

The package spatstat.core will be replaced by two packages, spatstat.explore and spatstat.model.

The following information is now out-of-date. This repository will be deleted soon.

Statistical analysis and modelling of spatial data for the spatstat family

The original spatstat package has been split into several sub-packages (See spatstat/spatstat)

This package spatstat.core is one of the sub-packages. It contains all the main user-level functions that perform statistical analysis and modelling of spatial data, with the exception of data on linear networks.

Most of the functionality is for spatial point patterns in two dimensions. There is a very modest amount of functionality for 3D and higher dimensional patterns and space-time patterns.

Overview

spatstat.core supports

• exploratory analysis (quadrat counting test, kernel smoothing, K-function, pair correlation function)
• nonparametric estimation (resource selection function, prospectivity)
• parametric modelling (fitting models to point pattern data, model selection, model prediction)
• formal inference (hypothesis tests, confidence intervals)
• informal validation (model diagnostics)

Detailed contents

For a full list of functions, see the help file for spatstat.core-package.

Exploratory analysis

• Clark-Evans index, Hopkins-Skellam index
• quadrat counting estimates of intensity, quadrat counting test
• Fry plot
• Morisita plot
• scan statistic
• cluster detection (Allard-Fraley cluster set, Byers-Raftery cleaning)

Nonparametric estimation

• kernel estimation of intensity of a point pattern
• kernel smoothing of mark values attached to point locations
• kernel estimation of relative risk
• kernel smoothing of a line segment pattern
• bandwidth selection
• nonparametric estimation of intensity as a function of a covariate
• ROC curve, AUC
• summary functions (K-function, pair correlation function, empty space function, nearest neighbour distance function, J-function, etc) and multi-type versions of these functions
• mark correlation function, mark independence diagnostoc
• local summary functions (LISA)
• simulation envelopes of summary functions
• manipulation of summary functions (plot, evaluate, differentiate, smooth etc)
• spatial bootstrap

Parametric modelling

• fitting Poisson point process models to point pattern data (ppm)
• fitting spatial logistic regression models to point pattern data (slrm)
• fitting Cox point process models to point pattern data (kppm)
• fitting Neyman-Scott cluster process models to point pattern data (kppm)
• fitting Gibbs point process models to point pattern data (ppm)
• class support for fitted models (update, summary, predict, plot, coef, vcov)
• minimum contrast estimation
• simulation of fitted point process models

Formal inference

• hypothesis tests (quadrat test, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test)
• confidence intervals for parameters of a model
• prediction intervals for point counts

Informal validation

• leverage
• influence
• partial residuals
• added variable plot
• diagnostic plots
• pseudoscore residual plots
• model compensators
• Q-Q plots

Data manipulation

• image blurring
• Choi-Hall data sharpening of point locations
• transects of an image along a line or curve
• programming tools