spatstat v1.35-0
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Spatial Point Pattern analysis, model-fitting, simulation, tests
A package for analysing spatial data, mainly Spatial Point Patterns, including multitype/marked points and spatial covariates, in any two-dimensional spatial region. Also supports three-dimensional point patterns, and space-time point patterns in any number of dimensions.
Contains over 1500 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.
Data types include point patterns, line segment patterns, spatial windows, pixel images and tessellations.
Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Diggle-Cressie-Loosmore-Ford, Dao-Genton) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm, kppm, slrm similar to glm. Types of models include Poisson, Gibbs, Cox and cluster point processes. Models may involve dependence on covariates, interpoint interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
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). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Functions in spatstat
| Name | Description | |
| affine.im | Apply Affine Transformation To Pixel Image | |
| Fest | Estimate the empty space function F | |
| Kmodel | K function of a model | |
| as.hyperframe | Convert Data to Hyperframe | |
| as.psp | Convert Data To Class psp | |
| Kmeasure | Reduced Second Moment Measure | |
| Jmulti | Marked J Function | |
| Gres | Residual G Function | |
| SatPiece | Piecewise Constant Saturated Pairwise Interaction Point Process Model | |
| Hardcore | The Hard Core Point Process Model | |
| ants | Harkness-Isham ants' nests data | |
| bind.fv | Combine Function Value Tables | |
| as.linim | Convert to Pixel Image on Linear Network | |
| append.psp | Combine Two Line Segment Patterns | |
| complement.owin | Take Complement of a Window | |
| Pairwise | Generic Pairwise Interaction model | |
| Gdot | Multitype Nearest Neighbour Distance Function (i-to-any) | |
| bei | Tropical rain forest trees | |
| by.ppp | Apply a Function to a Point Pattern Broken Down by Factor | |
| Kcross.inhom | Inhomogeneous Cross K Function | |
| delaunay | Delaunay Triangulation of Point Pattern | |
| bounding.box.xy | Convex Hull of Points | |
| Extract.splitppp | Extract or Replace Sub-Patterns | |
| affine.ppp | Apply Affine Transformation To Point Pattern | |
| bw.frac | Bandwidth Selection Based on Window Geometry | |
| Extract.owin | Extract Subset of Window | |
| Extract.fasp | Extract Subset of Function Array | |
| Finhom | Inhomogeneous Empty Space Function | |
| Tstat | Third order summary statistic | |
| Extract.psp | Extract Subset of Line Segment Pattern | |
| Extract.layered | Extract Subset of a Layered Object | |
| Ginhom | Inhomogeneous Nearest Neighbour Function | |
| cbind.hyperframe | Combine Hyperframes by Rows or by Columns | |
| area.owin | Area of a Window | |
| Concom | The Connected Component Process Model | |
| Extract.ppx | Extract Subset of Multidimensional Point Pattern | |
| discpartarea | Area of Part of Disc | |
| angles.psp | Orientation Angles of Line Segments | |
| Extract.im | Extract Subset of Image | |
| Kres | Residual K Function | |
| as.matrix.owin | Convert Pixel Image to Matrix | |
| affine.linnet | Apply Geometrical Transformations to a Linear Network | |
| MultiHard | The Multitype Hard Core Point Process Model | |
| Extract.fv | Extract Subset of Function Values | |
| as.polygonal | Convert a Window to a Polygonal Window | |
| blur | Apply Gaussian Blur to a Pixel Image | |
| coef.mppm | Coefficients of Point Process Model Fitted to Multiple Point Patterns | |
| crossdist.default | Pairwise distances between two different sets of points | |
| PairPiece | The Piecewise Constant Pairwise Interaction Point Process Model | |
| deltametric | Delta Metric | |
| Kdot | Multitype K Function (i-to-any) | |
| areaGain | Difference of Disc Areas | |
| eem | Exponential Energy Marks | |
| gridweights | Compute Quadrature Weights Based on Grid Counts | |
| dfbetas.ppm | Parameter influence measure | |
| as.box3 | Convert Data to Three-Dimensional Box | |
| anova.lppm | ANOVA for Fitted Point Process Models on Linear Network | |
| edge.Ripley | Ripley's Isotropic Edge Correction | |
| allstats | Calculate four standard summary functions of a point pattern. | |
| data.ppm | Extract Original Data from a Fitted Point Process Model | |
| cut.im | Convert Pixel Image from Numeric to Factor | |
| G3est | Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern | |
| envelope | Simulation Envelopes of Summary Function | |
| closing | Morphological Closing | |
| Kinhom | Inhomogeneous K-function | |
| linearpcfcross | Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| hyytiala | Scots pines and other trees at Hyytiala | |
| Extract.listof | Extract or Replace Subset of a List of Things | |
| corners | Corners of a rectangle | |
| Extract.quad | Subset of Quadrature Scheme | |
| as.lpp | Convert Data to a Point Pattern on a Linear Network | |
| commonGrid | Determine A Common Spatial Domain And Pixel Resolution | |
| Fiksel | The Fiksel Interaction | |
| Jinhom | Inhomogeneous J-function | |
| Geyer | Geyer's Saturation Point Process Model | |
| Poisson | Poisson Point Process Model | |
| Lcross.inhom | Inhomogeneous Cross Type L Function | |
| bronzefilter | Bronze gradient filter data | |
| Extract.linnet | Extract Subset of Linear Network | |
| formula.ppm | Model Formulae for Gibbs Point Process Models | |
| ewcdf | Weighted Empirical Cumulative Distribution Function | |
| K3est | K-function of a Three-Dimensional Point Pattern | |
| OrdThresh | Ord's Interaction model | |
| intensity | Intensity of a Dataset or a Model | |
| bdist.tiles | Distance to Boundary of Window | |
| betacells | Beta Ganglion Cells in Cat Retina | |
| LambertW | Lambert's W Function | |
| edge.Trans | Translation Edge Correction | |
| Smooth | Spatial smoothing of data | |
| bw.diggle | Cross Validated Bandwidth Selection for Kernel Density | |
| diameter | Diameter of an Object | |
| anemones | Beadlet Anemones Data | |
| LennardJones | The Lennard-Jones Potential | |
| Lcross | Multitype L-function (cross-type) | |
| lurking | Lurking variable plot | |
| affine.psp | Apply Affine Transformation To Line Segment Pattern | |
| fv.object | Function Value Table | |
| dilation | Morphological Dilation | |
| bdist.points | Distance to Boundary of Window | |
| as.interact | Extract Interaction Structure | |
| bounding.box | Bounding Box of a Window or Point Pattern | |
| envelope.envelope | Recompute Envelopes | |
| AreaInter | The Area Interaction Point Process Model | |
| bw.scott | Scott's Rule for Bandwidth Selection for Kernel Density | |
| by.im | Apply Function to Image Broken Down by Factor | |
| deriv.fv | Calculate Derivative of Function Values | |
| Gcom | Model Compensator of Nearest Neighbour Function | |
| affine.owin | Apply Affine Transformation To Window | |
| Gfox | Foxall's Distance Functions | |
| Extract.tess | Extract or Replace Subset of Tessellation | |
| as.data.frame.hyperframe | Coerce Hyperframe to Data Frame | |
| Kest | K-function | |
| amacrine | Hughes' Amacrine Cell Data | |
| convexhull.xy | Convex Hull of Points | |
| bw.smoothppp | Cross Validated Bandwidth Selection for Spatial Smoothing | |
| Gest | Nearest Neighbour Distance Function G | |
| affine.lpp | Apply Geometrical Transformations to Point Pattern on a Linear Network | |
| ganglia | Beta Ganglion Cells in Cat Retina, Old Version | |
| adaptive.density | Intensity Estimate of Point Pattern Using Tessellation | |
| beginner | Print Introduction For Beginners | |
| convolve.im | Convolution of Pixel Images | |
| disc | Circular Window | |
| Kest.fft | K-function using FFT | |
| anova.ppm | ANOVA for Fitted Point Process Models | |
| edges2vees | List Dihedral Triples in a Graph | |
| distmap.owin | Distance Map of Window | |
| MultiStraussHard | The Multitype/Hard Core Strauss Point Process Model | |
| Hybrid | Hybrid Interaction Point Process Model | |
| Iest | Estimate the I-function | |
| beachcolours | Create Colour Scheme for a Range of Numbers | |
| density.splitppp | Kernel Smoothed Intensity of Split Point Pattern | |
| crossdist.ppp | Pairwise distances between two different point patterns | |
| default.dummy | Generate a Default Pattern of Dummy Points | |
| Ldot | Multitype L-function (i-to-any) | |
| is.owin | Test Whether An Object Is A Window | |
| Kmulti.inhom | Inhomogeneous Marked K-Function | |
| copper | Berman-Huntington points and lines data | |
| boxx | Multi-Dimensional Box | |
| Strauss | The Strauss Point Process Model | |
| as.mask.psp | Convert Line Segment Pattern to Binary Pixel Mask | |
| markconnect | Mark Connection Function | |
| as.data.frame.ppp | Coerce Point Pattern to a Data Frame | |
| gorillas | Gorilla Nesting Sites | |
| inforder.family | Infinite Order Interaction Family | |
| density.ppp | Kernel Smoothed Intensity of Point Pattern | |
| methods.linnet | Methods for Linear Networks | |
| as.mask | Pixel Image Approximation of a Window | |
| diameter.boxx | Geometrical Calculations for Multi-Dimensional Box | |
| Kcross | Multitype K Function (Cross-type) | |
| as.data.frame.psp | Coerce Line Segment Pattern to a Data Frame | |
| convexhull | Convex Hull | |
| dummify | Convert Data to Numeric Values by Constructing Dummy Variables | |
| Jdot | Multitype J Function (i-to-any) | |
| delaunay.distance | Distance on Delaunay Triangulation | |
| harmonic | Basis for Harmonic Functions | |
| DiggleGatesStibbard | Diggle-Gates-Stibbard Point Process Model | |
| hybrid.family | Hybrid Interaction Family | |
| interp.colourmap | Interpolate smoothly between specified colours | |
| Gmulti | Marked Nearest Neighbour Distance Function | |
| box3 | Three-Dimensional Box | |
| eval.fasp | Evaluate Expression Involving Function Arrays | |
| as.ppm | Extract Fitted Point Process Model | |
| methods.fii | Methods for Fitted Interactions | |
| F3est | Empty Space Function of a Three-Dimensional Point Pattern | |
| nndist | Nearest neighbour distances | |
| clmfires | Castilla-La Mancha Forest Fires | |
| linearKcross.inhom | Inhomogeneous multitype K Function (Cross-type) for Linear Point Pattern | |
| methods.rhohat | Methods for Intensity Functions of Spatial Covariate | |
| compareFit | Residual Diagnostics for Multiple Fitted Models | |
| is.multitype | Test whether Object is Multitype | |
| intensity.ppp | Empirical Intensity of Point Pattern | |
| localKinhom | Inhomogeneous Neighbourhood Density Function | |
| crossdist.psp | Pairwise distances between two different line segment patterns | |
| diameter.box3 | Geometrical Calculations for Three-Dimensional Box | |
| circumradius | Circumradius and Diameter of a Linear Network | |
| inside.owin | Test Whether Points Are Inside A Window | |
| crossdist | Pairwise distances | |
| rStrauss | Perfect Simulation of the Strauss Process | |
| as.ppp | Convert Data To Class ppp | |
| BadGey | Hybrid Geyer Point Process Model | |
| eroded.areas | Areas of Morphological Erosions | |
| bermantest | Berman's Tests for Point Process Model | |
| as.fv | Convert Data To Class fv | |
| methods.lppm | Methods for Fitted Point Process Models on a Linear Network | |
| compatible | Test Whether Objects Are Compatible | |
| as.owin | Convert Data To Class owin | |
| addvar | Added Variable Plot for Point Process Model | |
| clickppp | Interactively Add Points | |
| Hest | Spherical Contact Distribution Function | |
| linearmarkconnect | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| rHardcore | Perfect Simulation of the Hardcore Process | |
| Extract.lpp | Extract Subset of Point Pattern on Linear Network | |
| as.tess | Convert Data To Tessellation | |
| colourmap | Colour Lookup Tables | |
| chicago | Chicago Street Crime Data | |
| demohyper | Demonstration Example of Hyperframe of Spatial Data | |
| border | Border Region of a Window | |
| Saturated | Saturated Pairwise Interaction model | |
| clickpoly | Interactively Define a Polygon | |
| layered | Create List of Plotting Layers | |
| connected | Connected components | |
| Jcross | Multitype J Function (i-to-j) | |
| Gcross | Multitype Nearest Neighbour Distance Function (i-to-j) | |
| fasp.object | Function Arrays for Spatial Patterns | |
| model.matrix.ppm | Extract Design Matrix from Point Process Model | |
| clickjoin | Interactively join vertices on a plot | |
| Replace.im | Reset Values in Subset of Image | |
| linearKdot.inhom | Inhomogeneous multitype K Function (Dot-type) for Linear Point Pattern | |
| is.lpp | Test Whether An Object Is A Point Pattern on a Linear Network | |
| linfun | Function on a Linear Network | |
| distmap | Distance Map | |
| distmap.psp | Distance Map of Line Segment Pattern | |
| coef.ppm | Coefficients of Fitted Point Process Model | |
| envelope.lpp | Envelope for Point Patterns on Linear Network | |
| methods.ppx | Methods for Multidimensional Space-Time Point Patterns | |
| plot.listof | Plot a List of Things | |
| anova.mppm | ANOVA for Fitted Multiple Point Process Models | |
| as.matrix.im | Convert Pixel Image to Matrix or Array | |
| kaplan.meier | Kaplan-Meier Estimator using Histogram Data | |
| as.data.frame.im | Convert Pixel Image to Data Frame | |
| endpoints.psp | Endpoints of Line Segment Pattern | |
| idw | Inverse-distance weighted smoothing of observations at irregular points | |
| rcell | Simulate Baddeley-Silverman Cell Process | |
| eval.im | Evaluate Expression Involving Pixel Images | |
| centroid.owin | Centroid of a window | |
| as.im | Convert to Pixel Image | |
| is.ppm | Test Whether An Object Is A Fitted Point Process Model | |
| MultiStrauss | The Multitype Strauss Point Process Model | |
| bramblecanes | Hutchings' Bramble Canes data | |
| compatible.im | Test Whether Pixel Images Are Compatible | |
| distfun.lpp | Distance Map on Linear Network | |
| chorley | Chorley-Ribble Cancer Data | |
| bw.ppl | Likelihood Cross Validation Bandwidth Selection for Kernel Density | |
| fryplot | Fry Plot of Point Pattern | |
| flipxy | Exchange X and Y Coordinates | |
| crossing.psp | Crossing Points of Two Line Segment Patterns | |
| funxy | Spatial Function Class | |
| matchingdist | Distance for a Point Pattern Matching | |
| distcdf | Distribution Function of Interpoint Distance | |
| Ord | Generic Ord Interaction model | |
| layerplotargs | Extract or Replace the Plot Arguments of a Layered Object | |
| plot.linnet | Plot a linear network | |
| linearKdot | Multitype K Function (Dot-type) for Linear Point Pattern | |
| hamster | Aherne's hamster tumour data | |
| concatxy | Concatenate x,y Coordinate Vectors | |
| opening | Morphological Opening | |
| plot.influence.ppm | Plot Influence Measure | |
| linearKcross | Multitype K Function (Cross-type) for Linear Point Pattern | |
| linearmarkequal | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| letterR | Window in Shape of Letter R | |
| fitted.slrm | Fitted Probabilities for Spatial Logistic Regression | |
| matclust.estpcf | Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation | |
| dclf.progress | Progress Plot of Test of Spatial Pattern | |
| Ldot.inhom | Inhomogeneous Multitype L Dot Function | |
| plot.psp | plot a Spatial Line Segment Pattern | |
| km.rs | Kaplan-Meier and Reduced Sample Estimator using Histograms | |
| lpp | Create Point Pattern on Linear Network | |
| localpcf | Local pair correlation function | |
| fitin.ppm | Extract the Interaction from a Fitted Point Process Model | |
| latest.news | Print News About Latest Version of Package | |
| linearpcf | Linear Pair Correlation Function | |
| dclf.test | Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests | |
| midpoints.psp | Midpoints of Line Segment Pattern | |
| chop.tess | Subdivide a Window or Tessellation using a Set of Lines | |
| effectfun | Compute Fitted Effect of a Spatial Covariate in a Point Process Model | |
| lgcp.estpcf | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| finpines | Pine saplings in Finland. | |
| methods.layered | Methods for Layered Objects | |
| pcf.fasp | Pair Correlation Function obtained from array of K functions | |
| im | Create a Pixel Image Object | |
| perimeter | Perimeter Length of Window | |
| model.frame.ppm | Extract the Variables in a Point Process Model | |
| clip.infline | Intersect Infinite Straight Lines with a Window | |
| plot.owin | Plot a Spatial Window | |
| is.stationary | Recognise Stationary and Poisson Point Process Models | |
| logLik.slrm | Loglikelihood of Spatial Logistic Regression | |
| edges2triangles | List Triangles in a Graph | |
| kppm | Fit Cluster or Cox Point Process Model | |
| marks.psp | Marks of a Line Segment Pattern | |
| areaLoss | Difference of Disc Areas | |
| is.rectangle | Determine Type of Window | |
| pppmatching | Create a Point Matching | |
| summary.owin | Summary of a Spatial Window | |
| cauchy.estpcf | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| is.marked.ppp | Test Whether A Point Pattern is Marked | |
| lppm | Fit Point Process Model to Point Pattern on Linear Network | |
| coef.slrm | Coefficients of Fitted Spatial Logistic Regression Model | |
| linearKinhom | Inhomogeneous Linear K Function | |
| Emark | Diagnostics for random marking | |
| fv | Create a Function Value Table | |
| is.im | Test Whether An Object Is A Pixel Image | |
| nnwhich | Nearest neighbour | |
| bdist.pixels | Distance to Boundary of Window | |
| infline | Infinite Straight Lines | |
| pairwise.family | Pairwise Interaction Process Family | |
| methods.boxx | Methods for Multi-Dimensional Box | |
| dilated.areas | Areas of Morphological Dilations | |
| split.ppp | Divide Point Pattern into Sub-patterns | |
| fvnames | Abbreviations for Groups of Columns in Function Value Table | |
| collapse.fv | Collapse Several Function Tables into One | |
| nnwhich.ppx | Nearest Neighbours in Any Dimensions | |
| closepairs | Close Pairs of Points | |
| rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |
| methods.lpp | Methods for Point Patterns on a Linear Network | |
| diameter.owin | Diameter of a Window | |
| plot.colourmap | Plot a Colour Map | |
| dirichlet.weights | Compute Quadrature Weights Based on Dirichlet Tessellation | |
| mppm | Fit Point Process Model to Several Point Patterns | |
| expand.owin | Apply Expansion Rule | |
| Lest | L-function | |
| is.multitype.ppm | Test Whether A Point Process Model is Multitype | |
| incircle | Find Largest Circle Inside Window | |
| markcorrint | Mark Correlation Integral | |
| as.rectangle | Window Frame | |
| cauchy.estK | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| iplot | Point and Click Interface for Displaying Spatial Data | |
| density.psp | Kernel Smoothing of Line Segment Pattern | |
| Triplets | The Triplet Point Process Model | |
| methods.linfun | Methods for Functions on Linear Network | |
| linearpcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| imcov | Spatial Covariance of a Pixel Image | |
| nztrees | New Zealand Trees Point Pattern | |
| dummy.ppm | Extract Dummy Points Used to Fit a Point Process Model | |
| model.matrix.slrm | Extract Design Matrix from Spatial Logistic Regression Model | |
| cut.ppp | Classify Points in a Point Pattern | |
| humberside | Humberside Data on Childhood Leukaemia and Lymphoma | |
| mean.im | Maximum, Minimum, Mean, Median, Range or Sum of Pixel Values in an Image | |
| as.hyperframe.ppx | Extract coordinates and marks of multidimensional point pattern | |
| mincontrast | Method of Minimum Contrast | |
| ppm.object | Class of Fitted Point Process Models | |
| stieltjes | Compute Integral of Function Against Cumulative Distribution | |
| objsurf | Objective Function Surface | |
| nnfun | Nearest Neighbour Index Map as a Function | |
| heather | Diggle's Heather Data | |
| pairdist | Pairwise distances | |
| linearpcfdot | Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| demopat | Artificial Data Point Pattern | |
| default.rmhcontrol | Set Default Control Parameters for Metropolis-Hastings Algorithm. | |
| owin | Create a Window | |
| plot.lppm | Plot a Fitted Point Process Model on a Linear Network | |
| linearpcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| psp.object | Class of Line Segment Patterns | |
| rhohat | Smoothing Estimate of Covariate Transformation | |
| lgcp.estK | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| fitted.ppm | Fitted Conditional Intensity for Point Process Model | |
| Kdot.inhom | Inhomogeneous Multitype K Dot Function | |
| crossdist.lpp | Pairwise distances between two point patterns on a linear network | |
| model.images | Compute Images of Constructed Covariates | |
| coords | Extract or Change Coordinates of a Spatial or Spatiotemporal Point Pattern | |
| gordon | People in Gordon Square | |
| Kmulti | Marked K-Function | |
| marktable | Tabulate Marks in Neighbourhood of Every Point in a Point Pattern | |
| ippm | Optimise Irregular Trend Parameters in Point Process Model | |
| rcellnumber | Generate Random Numbers of Points for Cell Process | |
| is.marked.ppm | Test Whether A Point Process Model is Marked | |
| markvario | Mark Variogram | |
| npfun | Dummy Function Returns Number of Points | |
| lut | Lookup Tables | |
| pairsat.family | Saturated Pairwise Interaction Point Process Family | |
| rmh | Simulate point patterns using the Metropolis-Hastings algorithm. | |
| erosion | Morphological Erosion | |
| envelope.pp3 | Simulation Envelopes of Summary Function for 3D Point Pattern | |
| model.depends | Identify Covariates Involved in each Model Term | |
| eval.linim | Evaluate Expression Involving Pixel Images on Linear Network | |
| dirichlet | Dirichlet Tessellation of Point Pattern | |
| quantile.im | Sample Quantiles of Pixel Image | |
| project2segment | Move Point To Nearest Line | |
| hist.im | Histogram of Pixel Values in an Image | |
| marks | Marks of a Point Pattern | |
| plot.hyperframe | Plot Entries in a Hyperframe | |
| distmap.ppp | Distance Map of Point Pattern | |
| is.empty | Test Whether An Object Is Empty | |
| rMatClust | Simulate Matern Cluster Process | |
| plot.im | Plot a Pixel Image | |
| ppx | Multidimensional Space-Time Point Pattern | |
| rmhmodel.ppm | Interpret Fitted Model for Metropolis-Hastings Simulation. | |
| quad.ppm | Extract Quadrature Scheme Used to Fit a Point Process Model | |
| methods.funxy | Methods for Spatial Functions | |
| ord.family | Ord Interaction Process Family | |
| clusterset | Allard-Fraley Estimator of Cluster Feature | |
| pcf.ppp | Pair Correlation Function of Point Pattern | |
| plot.ppp | plot a Spatial Point Pattern | |
| nndist.lpp | Nearest neighbour distances on a linear network | |
| spruces | Spruces Point Pattern | |
| Jest | Estimate the J-function | |
| plot.layered | Layered Plot | |
| kstest.mppm | Kolmogorov-Smirnov Test for Multiple Point Process Model | |
| is.marked | Test Whether Marks Are Present | |
| npoints | Number of Points in a Point Pattern | |
| Linhom | L-function | |
| runifpoint3 | Generate N Uniform Random Points in Three Dimensions | |
| cells | Biological Cells Point Pattern | |
| rshift.splitppp | Randomly Shift a List of Point Patterns | |
| rStraussHard | Perfect Simulation of the Strauss-Hardcore Process | |
| summary.quad | Summarizing a Quadrature Scheme | |
| nncorr | Nearest-Neighbour Correlation Indices of Marked Point Pattern | |
| intensity.ppm | Intensity of Fitted Point Process Model | |
| clarkevans.test | Clark and Evans Test | |
| swedishpines | Swedish Pines Point Pattern | |
| hyperframe | Hyper Data Frame | |
| alltypes | Calculate Summary Statistic for All Types in a Multitype Point Pattern | |
| colourtools | Convert and Compare Colours in Different Formats | |
| crossdist.ppx | Pairwise Distances Between Two Different Multi-Dimensional Point Patterns | |
| pixellate | Convert Spatial Object to Pixel Image | |
| discretise | Safely Convert Point Pattern Window to Binary Mask | |
| pixellate.ppp | Convert Point Pattern to Pixel Image | |
| periodify | Make Periodic Copies of a Spatial Pattern | |
| formula.fv | Extract or Change the Plot Formula for a Function Value Table | |
| nndist.ppx | Nearest Neighbour Distances in Any Dimensions | |
| contour.im | Contour plot of pixel image | |
| crossdist.pp3 | Pairwise distances between two different three-dimensional point patterns | |
| bw.relrisk | Cross Validated Bandwidth Selection for Relative Risk Estimation | |
| rmhexpand | Specify Simulation Window or Expansion Rule | |
| split.ppx | Divide Multidimensional Point Pattern into Sub-patterns | |
| distfun | Distance Map as a Function | |
| compatible.fv | Test Whether Function Objects Are Compatible | |
| plot.pp3 | Plot a three-dimensional point pattern | |
| harmonise.im | Make Pixel Images Compatible | |
| linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |
| pp3 | Three Dimensional Point Pattern | |
| identify.psp | Identify Segments in a Line Segment Pattern | |
| pairdist.ppx | Pairwise Distances in Any Dimensions | |
| Kcom | Model Compensator of K Function | |
| gridcentres | Rectangular grid of points | |
| integral.im | Integral of a Pixel Image | |
| quadrat.test.mppm | Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts | |
| exactMPLEstrauss | Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process | |
| plot.kppm | Plot a fitted cluster point process | |
| intensity.lpp | Empirical Intensity of Point Pattern on Linear Network | |
| connected.ppp | Connected components of a point pattern | |
| subfits | Extract List of Individual Point Process Models | |
| thomas.estpcf | Fit the Thomas Point Process by Minimum Contrast | |
| pcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type) | |
| plot.bermantest | Plot Result of Berman Test | |
| split.im | Divide Image Into Sub-images | |
| Kscaled | Locally Scaled K-function | |
| is.multitype.ppp | Test Whether A Point Pattern is Multitype | |
| nnfun.lpp | Nearest Neighbour Map on Linear Network | |
| diagnose.ppm | Diagnostic Plots for Fitted Point Process Model | |
| kstest.ppm | Kolmogorov-Smirnov Test for Point Pattern or Point Process Model | |
| mucosa | Cells in Gastric Mucosa | |
| rotate.owin | Rotate a Window | |
| japanesepines | Japanese Pines Point Pattern | |
| psstG | Pseudoscore Diagnostic For Fitted Model against Saturation Alternative | |
| quadrat.test.splitppp | Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts | |
| pcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-Type) | |
| simplenet | Simple Example of Linear Network | |
| murchison | Murchison gold deposits | |
| integral.msr | Integral of a Measure | |
| sidelengths.owin | Side Lengths of Enclosing Rectangle of a Window | |
| lohboot | Bootstrap Confidence Bands for Summary Function | |
| rGaussPoisson | Simulate Gauss-Poisson Process | |
| predict.slrm | Predicted or Fitted Values from Spatial Logistic Regression | |
| plot.kstest | Plot a Spatial Kolmogorov-Smirnov Test | |
| owin.object | Class owin | |
| plot.tess | Plot a tessellation | |
| nnclean | Nearest Neighbour Clutter Removal | |
| identify.ppp | Identify Points in a Point Pattern | |
| bw.stoyan | Stoyan's Rule of Thumb for Bandwidth Selection | |
| unique.ppp | Extract Unique Points from a Spatial Point Pattern | |
| nndist.pp3 | Nearest neighbour distances in three dimensions | |
| localK | Neighbourhood density function | |
| paracou | Kimboto trees at Paracou, French Guiana | |
| pool.rat | Pool Data from Several Ratio Objects | |
| Softcore | The Soft Core Point Process Model | |
| rMosaicField | Mosaic Random Field | |
| logLik.mppm | Log Likelihood for Poisson Point Process Model | |
| update.rmhcontrol | Update Control Parameters of Metropolis-Hastings Algorithm | |
| lineardisc | Compute Disc of Given Radius in Linear Network | |
| rthin | Random Thinning | |
| reduced.sample | Reduced Sample Estimator using Histogram Data | |
| plot.splitppp | Plot a List of Point Patterns | |
| is.ppp | Test Whether An Object Is A Point Pattern | |
| rescale.ppp | Convert Point Pattern to Another Unit of Length | |
| matclust.estK | Fit the Matern Cluster Point Process by Minimum Contrast | |
| reflect | Reflect In Origin | |
| raster.x | Cartesian Coordinates for a Pixel Raster | |
| pcfcross | Multitype pair correlation function (cross-type) | |
| pyramidal | Pyramidal Neurons in Cingulate Cortex | |
| print.ppp | Print Brief Details of a Point Pattern Dataset | |
| markcorr | Mark Correlation Function | |
| eval.fv | Evaluate Expression Involving Functions | |
| rLGCP | Simulate Log-Gaussian Cox Process | |
| nsegments | Number of Line Segments in a Line Segment Pattern | |
| will.expand | Test Expansion Rule | |
| urkiola | Urkiola Woods Point Pattern | |
| shift.psp | Apply Vector Translation To Line Segment Pattern | |
| rknn | Theoretical Distribution of Nearest Neighbour Distance | |
| rmh.default | Simulate Point Process Models using the Metropolis-Hastings Algorithm. | |
| print.ppm | Print a Fitted Point Process Model | |
| pppmatching.object | Class of Point Matchings | |
| simplify.owin | Approximate a Polygon by a Simpler Polygon | |
| nncross | Nearest Neighbours Between Two Patterns | |
| miplot | Morishita Index Plot | |
| flu | Influenza Virus Proteins | |
| intersect.tess | Intersection of Two Tessellations | |
| waterstriders | Waterstriders data. Three independent replications of a point pattern formed by insects. | |
| nndensity.ppp | Estimate Intensity of Point Pattern Using Nearest Neighbour Distances | |
| parres | Partial Residuals for Point Process Model | |
| pixelquad | Quadrature Scheme Based on Pixel Grid | |
| pcfdot | Multitype pair correlation function (i-to-any) | |
| linearK | Linear K Function | |
| quadrat.test | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts | |
| round.ppp | Apply Numerical Rounding to Spatial Coordinates | |
| predict.mppm | Prediction for Fitted Multiple Point Process Model | |
| shift.owin | Apply Vector Translation To Window | |
| methods.kppm | Methods for Cluster Point Process Models | |
| plot.ppm | plot a Fitted Point Process Model | |
| overlap.owin | Compute Area of Overlap | |
| rescale | Convert dataset to another unit of length | |
| pairdist.ppp | Pairwise distances | |
| nbfires | Point Patterns of New Brunswick Forest Fires | |
| pairdist.psp | Pairwise distances between line segments | |
| plot.plotppm | Plot a plotppm Object Created by plot.ppm | |
| markstat | Summarise Marks in Every Neighbourhood in a Point Pattern | |
| interp.im | Interpolate a Pixel Image | |
| istat | Point and Click Interface for Exploratory Analysis of Point Pattern | |
| lengths.psp | Lengths of Line Segments | |
| linnet | Create a Linear Network | |
| is.subset.owin | Determine Whether One Window is Contained In Another | |
| scanpp | Read Point Pattern From Data File | |
| predict.lppm | Predict Point Process Model on Linear Network | |
| longleaf | Longleaf Pines Point Pattern | |
| pool.quadrattest | Pool Several Quadrat Tests | |
| contour.listof | Plot a List of Things | |
| methods.slrm | Methods for Spatial Logistic Regression Models | |
| nnwhich.pp3 | Nearest neighbours in three dimensions | |
| rDGS | Perfect Simulation of the Diggle-Gates-Stibbard Process | |
| Extract.ppp | Extract or Replace Subset of Point Pattern | |
| spatstat-package | The Spatstat Package | |
| is.convex | Test Whether a Window is Convex | |
| methods.pp3 | Methods for three-dimensional point patterns | |
| summary.splitppp | Summary of a Split Point Pattern | |
| pcfinhom | Inhomogeneous Pair Correlation Function | |
| summary.ppp | Summary of a Point Pattern Dataset | |
| rescale.psp | Convert Line Segment Pattern to Another Unit of Length | |
| rotate.psp | Rotate a Line Segment Pattern | |
| spatstat-internal | Internal spatstat functions | |
| pairdist.default | Pairwise distances | |
| rmhstart | Determine Initial State for Metropolis-Hastings Simulation. | |
| ponderosa | Ponderosa Pine Tree Point Pattern | |
| msr | Signed or Vector-Valued Measure | |
| nncross.lpp | Nearest Neighbours on a Linear Network | |
| pairdist.lpp | Pairwise shortest-path distances between points on a linear network | |
| reach | Interaction Distance of a Point Process | |
| rsyst | Simulate systematic random point pattern | |
| project.ppm | Force Point Process Model to be Valid | |
| rgbim | Create Colour-Valued Pixel Image | |
| plot.linim | Plot Pixel Image on Linear Network | |
| DiggleGratton | Diggle-Gratton model | |
| shift | Apply Vector Translation | |
| rpoint | Generate N Random Points | |
| unitname | Name for Unit of Length | |
| print.owin | Print Brief Details of a Spatial Window | |
| slrm | Spatial Logistic Regression | |
| im.object | Class of Images | |
| scan.test | Spatial Scan Test | |
| pcf.fv | Pair Correlation Function obtained from K Function | |
| shapley | Galaxies in the Shapley Supercluster | |
| layout.boxes | Generate a Row or Column Arrangement of Rectangles. | |
| rmhcontrol | Set Control Parameters for Metropolis-Hastings Algorithm. | |
| rNeymanScott | Simulate Neyman-Scott Process | |
| plot.msr | Plot a Signed or Vector-Valued Measure | |
| rThomas | Simulate Thomas Process | |
| quadrats | Divide Region into Quadrats | |
| residuals.mppm | Residuals for Point Process Model Fitted to Multiple Point Patterns | |
| union.quad | Union of Data and Dummy Points | |
| nnmark | Mark of Nearest Neighbour | |
| is.hybrid | Test Whether Object is a Hybrid | |
| selfcrossing.psp | Crossing Points in a Line Segment Pattern | |
| duplicated.ppp | Determine Duplicated Points in a Spatial Point Pattern | |
| tilenames | Names of Tiles in a Tessellation | |
| quadscheme.logi | Generate a Logistic Regression Quadrature Scheme from a Point Pattern | |
| unnormdensity | Weighted kernel smoother | |
| methods.rho2hat | Methods for Intensity Functions of Two Spatial Covariates | |
| trim.rectangle | Cut margins from rectangle | |
| rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |
| unmark | Remove Marks | |
| pixellate.owin | Convert Window to Pixel Image | |
| rounding | Detect Numerical Rounding | |
| spatstat.options | Internal Options in Spatstat Package | |
| nearestsegment | Find Line Segment Nearest to Each Point | |
| leverage.ppm | Leverage Measure for Spatial Point Process Model | |
| runifpoint | Generate N Uniform Random Points | |
| scaletointerval | Rescale Data to Lie Between Specified Limits | |
| linim | Create Pixel Image on Linear Network | |
| print.quad | Print a Quadrature Scheme | |
| rshift.psp | Randomly Shift a Line Segment Pattern | |
| superimpose | Superimpose Several Geometric Patterns | |
| rDiggleGratton | Perfect Simulation of the Diggle-Gratton Process | |
| rMaternII | Simulate Matern Model II | |
| quadratresample | Resample a Point Pattern by Resampling Quadrats | |
| tweak.colourmap | Change Colour Values in a Colour Map | |
| rotate.im | Rotate a Pixel Image | |
| pppdist | Distance Between Two Point Patterns | |
| tile.areas | Compute Areas of Tiles in a Tessellation | |
| profilepl | Profile Maximum Pseudolikelihood | |
| ppp.object | Class of Point Patterns | |
| logLik.ppm | Log Likelihood and AIC for Point Process Model | |
| simulate.kppm | Simulate a Fitted Cluster Point Process Model | |
| methods.box3 | Methods for Three-Dimensional Box | |
| vcov.kppm | Variance-Covariance Matrix for a Fitted Cluster Point Process Model | |
| setcov | Set Covariance of a Window | |
| rstrat | Simulate Stratified Random Point Pattern | |
| progressreport | Print Progress Reports | |
| default.expand | Default Expansion Rule for Simulation of Model | |
| nncross.pp3 | Nearest Neighbours Between Two Patterns in 3D | |
| persp.im | Perspective Plot of Pixel Image | |
| shift.im | Apply Vector Translation To Pixel Image | |
| pixellate.psp | Convert Line Segment Pattern to Pixel Image | |
| pcf3est | Pair Correlation Function of a Three-Dimensional Point Pattern | |
| plot.mppm | plot a Fitted Multiple Point Process Model | |
| rotate.ppp | Rotate a Point Pattern | |
| lansing | Lansing Woods Point Pattern | |
| pairdist.pp3 | Pairwise distances in Three Dimensions | |
| rmpoint | Generate N Random Multitype Points | |
| quad.object | Class of Quadrature Schemes | |
| plot.fv | Plot Function Values | |
| rescale.owin | Convert Window to Another Unit of Length | |
| which.max.im | Identify Pixelwise Maximum of Several Pixel Images | |
| methods.objsurf | Methods for Objective Function Surfaces | |
| pairs.im | Scatterplot Matrix for Pixel Images | |
| rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |
| simulate.ppm | Simulate a Fitted Gibbs Point Process Model | |
| plot.leverage.ppm | Plot Leverage Function | |
| relrisk | Nonparametric Estimate of Spatially-Varying Relative Risk | |
| thomas.estK | Fit the Thomas Point Process by Minimum Contrast | |
| summary.psp | Summary of a Line Segment Pattern Dataset | |
| pool.envelope | Pool Data from Several Envelopes | |
| update.ppm | Update a Fitted Point Process Model | |
| residualspaper | Data and Code From JRSS Discussion Paper on Residuals | |
| residuals.ppm | Residuals for Fitted Point Process Model | |
| pointsOnLines | Place Points Evenly Along Specified Lines | |
| rSSI | Simulate Simple Sequential Inhibition | |
| rmhmodel.default | Build Point Process Model for Metropolis-Hastings Simulation. | |
| run.simplepanel | Run Point-and-Click Interface | |
| pcf | Pair Correlation Function | |
| clarkevans | Clark and Evans Aggregation Index | |
| predict.ppm | Prediction from a Fitted Point Process Model | |
| psst | Pseudoscore Diagnostic For Fitted Model against General Alternative | |
| rpoislinetess | Poisson Line Tessellation | |
| transect.im | Pixel Values Along a Transect | |
| psstA | Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative | |
| nnwhich.lpp | Identify Nearest Neighbours on a Linear Network | |
| nearest.raster.point | Find Pixel Nearest to a Given Point | |
| tess | Create a Tessellation | |
| qqplot.ppm | Q-Q Plot of Residuals from Fitted Point Process Model | |
| spatstat-deprecated | Deprecated spatstat functions | |
| zapsmall.im | Rounding of Pixel Values | |
| rpoisline | Generate Poisson Random Line Process | |
| rMaternI | Simulate Matern Model I | |
| volume | Volume of an Object | |
| fitted.mppm | Fitted Conditional Intensity for Multiple Point Process Model | |
| redwoodfull | California Redwoods Point Pattern (Entire Dataset) | |
| rmpoispp | Generate Multitype Poisson Point Pattern | |
| with.fv | Evaluate an Expression in a Function Table | |
| levelset | Level Set of a Pixel Image | |
| predict.kppm | Prediction from a Fitted Cluster Point Process Model | |
| stratrand | Stratified random point pattern | |
| compatible.fasp | Test Whether Function Arrays Are Compatible | |
| quadratcount | Quadrat counting for a point pattern | |
| sessionLibs | Print Names and Version Numbers of Libraries Loaded | |
| rMosaicSet | Mosaic Random Set | |
| plot.envelope | Plot a Simulation Envelope | |
| rshift.ppp | Randomly Shift a Point Pattern | |
| rat | Ratio object | |
| simdat | Simulated Point Pattern | |
| scalardilate | Apply Scalar Dilation | |
| osteo | Osteocyte Lacunae Data: Replicated Three-Dimensional Point Patterns | |
| shift.ppp | Apply Vector Translation To Point Pattern | |
| sharpen | Data Sharpening of Point Pattern | |
| methods.units | Methods for Units | |
| waka | Trees in Waka national park | |
| applynbd | Apply Function to Every Neighbourhood in a Point Pattern | |
| simulate.slrm | Simulate a Fitted Spatial Logistic Regression Model | |
| plot.fasp | Plot a Function Array | |
| summary.im | Summarizing a Pixel Image | |
| print.psp | Print Brief Details of a Line Segment Pattern Dataset | |
| rmhmodel.list | Define Point Process Model for Metropolis-Hastings Simulation. | |
| pool | Pool Data | |
| pool.fasp | Pool Data from Several Function Arrays | |
| ppm | Fit Point Process Model to Data | |
| vcov.mppm | Calculate Variance-Covariance Matrix for Fitted Multiple Point Process Model | |
| rescue.rectangle | Convert Window Back To Rectangle | |
| rjitter | Random Perturbation of a Point Pattern | |
| rho2hat | Smoothed Relative Density of Pairs of Covariate Values | |
| rshift | Random Shift | |
| vertices | Vertices of a Window | |
| print.im | Print Brief Details of an Image | |
| plot.quad | plot a Spatial Quadrature Scheme | |
| rCauchy | Simulate Neyman-Scott Point Process with Cauchy cluster kernel | |
| spokes | Spokes pattern of dummy points | |
| redwood | California Redwoods Point Pattern (Ripley's Subset) | |
| timed | Record the Computation Time | |
| multiplicity.ppp | Count Multiplicity of Duplicate Points | |
| tiles | Extract List of Tiles in a Tessellation | |
| simba | Simulated data from a two-group experiment with replication within each group. | |
| plot.slrm | Plot a Fitted Spatial Logistic Regression | |
| intersect.owin | Intersection, Union or Set Subtraction of Two Windows | |
| rpoislpp | Poisson Point Process on a Linear Network | |
| nnmap | K-th Nearest Point Map | |
| smooth.msr | Smooth a Signed or Vector-Valued Measure | |
| with.hyperframe | Evaluate an Expression in Each Row of a Hyperframe | |
| rpoispp | Generate Poisson Point Pattern | |
| nndist.psp | Nearest neighbour distances between line segments | |
| suffstat | Sufficient Statistic of Point Process Model | |
| vargamma.estpcf | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| vargamma.estK | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| psp | Create a Line Segment Pattern | |
| runifdisc | Generate N Uniform Random Points in a Disc | |
| sumouter | Compute Quadratic Forms | |
| rPoissonCluster | Simulate Poisson Cluster Process | |
| ripras | Estimate window from points alone | |
| rmhmodel | Define Point Process Model for Metropolis-Hastings Simulation. | |
| rlabel | Random Re-Labelling of Point Pattern | |
| solutionset | Evaluate Logical Expression Involving Pixel Images and Return Region Where Expression is True | |
| runifpointx | Generate N Uniform Random Points in Any Dimensions | |
| summary.listof | Summary of a List of Things | |
| whist | Weighted Histogram | |
| summary.ppm | Summarizing a Fitted Point Process Model | |
| ppp | Create a Point Pattern | |
| vcov.ppm | Variance-Covariance Matrix for a Fitted Point Process Model | |
| varblock | Estimate Variance of Summary Statistic by Subdivision | |
| Extract.msr | Extract Subset of Signed or Vector Measure | |
| rlinegrid | Generate grid of parallel lines with random displacement | |
| as.function.fv | Convert Function Value Table to Function | |
| valid.ppm | Check Whether Point Process Model is Valid | |
| runiflpp | Uniform Random Points on a Linear Network | |
| update.kppm | Update a Fitted Cluster Point Process Model | |
| rVarGamma | Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel | |
| rotate | Rotate | |
| triplet.family | Triplet Interaction Family | |
| vcov.slrm | Variance-Covariance Matrix for a Fitted Spatial Logistic Regression | |
| runifpointOnLines | Generate N Uniform Random Points On Line Segments | |
| quadscheme | Generate a Quadrature Scheme from a Point Pattern | |
| square | Square Window | |
| smooth.ppp | Spatial smoothing of observations at irregular points | |
| simplepanel | Simple Point-and-Click Interface Panels | |
| rmh.ppm | Simulate from a Fitted Point Process Model | |
| rescale.im | Convert Pixel Image to Another Unit of Length | |
| smooth.fv | Apply Smoothing to Function Values | |
| affine | Apply Affine Transformation | |
| StraussHard | The Strauss / Hard Core Point Process Model | |
| anova.slrm | Analysis of Deviance for Spatial Logistic Regression Models | |
| influence.ppm | Influence Measure for Spatial Point Process Model | |
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Details
| Nickname | Multiple Personality |
| Date | 2013-12-12 |
| License | GPL (>= 2) |
| URL | http://www.spatstat.org |
| LazyData | true |
| NeedsCompilation | yes |
| ByteCompile | true |
| Packaged | 2013-12-12 12:12:12 UTC; adrian |
| Repository | CRAN |
| Date/Publication | 2013-12-12 17:47:25 |
| depends | abind , base (>= 3.0.2) , deldir (>= 0.0-21) , graphics , grDevices , mgcv , polyclip (>= 1.2-0) , R (>= 3.0.2) , stats , tensor , utils |
| suggests | gsl , locfit , maptools , RandomFields (>= 3.0.0) , rpanel , scatterplot3d , sm , spatial , tkrplot |
| Contributors | Rolf Turner, Adrian Baddeley |
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