spatstat v1.34-1
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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 1000 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, Dao-Genton) are also supported.
Point process models can be fitted to point pattern data using the functions ppm, kppm, slrm similar to glm. 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 various composite likelihood methods.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported. Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, and leverage and influence diagnostics.
Functions in spatstat
| Name | Description | |
| Extract.tess | Extract or Replace Subset of Tessellation | |
| Hest | Spherical Contact Distribution Function | |
| Extract.splitppp | Extract or Replace Sub-Patterns | |
| Extract.fasp | Extract Subset of Function Array | |
| Gfox | Foxall's Distance Functions | |
| BadGey | Hybrid Geyer Point Process Model | |
| Kcross.inhom | Inhomogeneous Cross K Function | |
| Extract.fv | Extract Subset of Function Values | |
| Extract.im | Extract Subset of Image | |
| Extract.ppp | Extract or Replace Subset of Point Pattern | |
| Concom | The Connected Component Process Model | |
| Fest | Estimate the empty space function F | |
| Gcom | Model Compensator of Nearest Neighbour Function | |
| G3est | Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern | |
| Gmulti | Marked Nearest Neighbour Distance Function | |
| Gres | Residual G Function | |
| Extract.ppx | Extract Subset of Multidimensional Point Pattern | |
| DiggleGatesStibbard | Diggle-Gates-Stibbard Point Process Model | |
| Lcross.inhom | Inhomogeneous Cross Type L Function | |
| DiggleGratton | Diggle-Gratton model | |
| Ldot | Multitype L-function (i-to-any) | |
| PairPiece | The Piecewise Constant Pairwise Interaction Point Process Model | |
| Kcom | Model Compensator of K Function | |
| Pairwise | Generic Pairwise Interaction model | |
| StraussHard | The Strauss / Hard Core Point Process Model | |
| affine.im | Apply Affine Transformation To Pixel Image | |
| Fiksel | The Fiksel Interaction | |
| Emark | Diagnostics for random marking | |
| Gest | Nearest Neighbour Distance Function G | |
| LambertW | Lambert's W Function | |
| Lcross | Multitype L-function (cross-type) | |
| Ginhom | Inhomogeneous Nearest Neighbour Function | |
| allstats | Calculate four standard summary functions of a point pattern. | |
| angles.psp | Orientation Angles of Line Segments | |
| Kdot.inhom | Inhomogeneous Multitype K Dot Function | |
| Kcross | Multitype K Function (Cross-type) | |
| Kest.fft | K-function using FFT | |
| as.lpp | Convert Data to a Point Pattern on a Linear Network | |
| Iest | Estimate the I-function | |
| Kdot | Multitype K Function (i-to-any) | |
| Jinhom | Inhomogeneous J-function | |
| F3est | Empty Space Function of a Three-Dimensional Point Pattern | |
| bdist.pixels | Distance to Boundary of Window | |
| Kest | K-function | |
| crossdist.lpp | Pairwise distances between two point patterns on a linear network | |
| affine.owin | Apply Affine Transformation To Window | |
| as.box3 | Convert Data to Three-Dimensional Box | |
| Kres | Residual K Function | |
| Geyer | Geyer's Saturation Point Process Model | |
| Extract.layered | Extract Subset of a Layered Object | |
| Extract.quad | Subset of Quadrature Scheme | |
| beachcolours | Create Colour Scheme for a Range of Numbers | |
| areaGain | Difference of Disc Areas | |
| as.data.frame.psp | Coerce Line Segment Pattern to a Data Frame | |
| Kmulti | Marked K-Function | |
| crossdist.psp | Pairwise distances between two different line segment patterns | |
| Kscaled | Locally Scaled K-function | |
| Hybrid | Hybrid Interaction Point Process Model | |
| Tstat | Third order summary statistic | |
| anova.slrm | Analysis of Deviance for Spatial Logistic Regression Models | |
| Lest | L-function | |
| as.matrix.im | Convert Pixel Image to Matrix or Array | |
| Jmulti | Marked J Function | |
| Jdot | Multitype J Function (i-to-any) | |
| bw.smoothppp | Cross Validated Bandwidth Selection for Spatial Smoothing | |
| Kmodel | K function of a model | |
| Gcross | Multitype Nearest Neighbour Distance Function (i-to-j) | |
| as.owin | Convert Data To Class owin | |
| amacrine | Hughes' Amacrine Cell Data | |
| cells | Biological Cells Point Pattern | |
| bind.fv | Combine Function Value Tables | |
| Kinhom | Inhomogeneous K-function | |
| Strauss | The Strauss Point Process Model | |
| distfun | Distance Map as a Function | |
| ants | Harkness-Isham ants' nests data | |
| coef.slrm | Coefficients of Fitted Spatial Logistic Regression Model | |
| clarkevans | Clark and Evans Aggregation Index | |
| area.owin | Area of a Window | |
| distcdf | Distribution Function of Interpoint Distance | |
| as.mask | Pixel Image Approximation of a Window | |
| Saturated | Saturated Pairwise Interaction model | |
| as.mask.psp | Convert Line Segment Pattern to Binary Pixel Mask | |
| Poisson | Poisson Point Process Model | |
| as.function.fv | Convert Function Value Table to Function | |
| distmap.owin | Distance Map of Window | |
| Ldot.inhom | Inhomogeneous Multitype L Dot Function | |
| MultiStraussHard | The Multitype/Hard Core Strauss Point Process Model | |
| gordon | People in Gordon Square | |
| affine.lpp | Apply Geometrical Transformations to Point Pattern on a Linear Network | |
| Hardcore | The Hard Core Point Process Model | |
| as.polygonal | Convert a Window to a Polygonal Window | |
| as.hyperframe.ppx | Extract coordinates and marks of multidimensional point pattern | |
| Extract.linnet | Extract Subset of Linear Network | |
| K3est | K-function of a Three-Dimensional Point Pattern | |
| Replace.im | Reset Values in Subset of Image | |
| as.fv | Convert Data To Class fv | |
| box3 | Three-Dimensional Box | |
| contour.listof | Plot a List of Things | |
| clickpoly | Interactively Define a Polygon | |
| bei | Tropical rain forest trees | |
| bw.stoyan | Stoyan's Rule of Thumb for Bandwidth Selection | |
| as.ppm | Extract Fitted Point Process Model | |
| Ord | Generic Ord Interaction model | |
| crossdist.ppp | Pairwise distances between two different point patterns | |
| compatible | Test Whether Objects Are Compatible | |
| bw.diggle | Cross Validated Bandwidth Selection for Kernel Density | |
| compatible.fv | Test Whether Function Objects Are Compatible | |
| OrdThresh | Ord's Interaction model | |
| chop.tess | Subdivide a Window or Tessellation using a Set of Lines | |
| as.data.frame.ppp | Coerce Point Pattern to a Data Frame | |
| applynbd | Apply Function to Every Neighbourhood in a Point Pattern | |
| density.psp | Kernel Smoothing of Line Segment Pattern | |
| bermantest | Berman's Tests for Point Process Model | |
| as.linim | Convert to Pixel Image on Linear Network | |
| dclf.test | Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests | |
| blur | Apply Gaussian Blur to a Pixel Image | |
| diameter.box3 | Geometrical Calculations for Three-Dimensional Box | |
| betacells | Beta Ganglion Cells in Cat Retina | |
| bramblecanes | Hutchings' Bramble Canes data | |
| dummy.ppm | Extract Dummy Points Used to Fit a Point Process Model | |
| addvar | Added Variable Plot for Point Process Model | |
| boxx | Multi-Dimensional Box | |
| dilation | Morphological Dilation | |
| as.psp | Convert Data To Class psp | |
| crossdist.ppx | Pairwise Distances Between Two Different Multi-Dimensional Point Patterns | |
| affine.psp | Apply Affine Transformation To Line Segment Pattern | |
| clip.infline | Intersect Infinite Straight Lines with a Window | |
| inside.owin | Test Whether Points Are Inside A Window | |
| MultiStrauss | The Multitype Strauss Point Process Model | |
| connected.ppp | Connected components of a point pattern | |
| diameter.boxx | Geometrical Calculations for Multi-Dimensional Box | |
| hyperframe | Hyper Data Frame | |
| clmfires | Castilla-La Mancha Forest Fires | |
| convexhull | Convex Hull | |
| delaunay | Delaunay Triangulation of Point Pattern | |
| compatible.fasp | Test Whether Function Arrays Are Compatible | |
| edges2triangles | List Triangles in a Graph | |
| is.multitype.ppm | Test Whether A Point Process Model is Multitype | |
| clickjoin | Interactively join vertices on a plot | |
| dirichlet.weights | Compute Quadrature Weights Based on Dirichlet Tessellation | |
| circumradius | Circumradius and Diameter of a Linear Network | |
| as.rectangle | Window Frame | |
| border | Border Region of a Window | |
| by.ppp | Apply a Function to a Point Pattern Broken Down by Factor | |
| colourtools | Convert and Compare Colours in Different Formats | |
| cauchy.estK | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| distfun.lpp | Distance Map on Linear Network | |
| linearpcfdot | Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| clickppp | Interactively Add Points | |
| eval.fasp | Evaluate Expression Involving Function Arrays | |
| deriv.fv | Calculate Derivative of Function Values | |
| bw.frac | Bandwidth Selection Based on Window Geometry | |
| is.owin | Test Whether An Object Is A Window | |
| Triplets | The Triplet Point Process Model | |
| bw.scott | Scott's Rule for Bandwidth Selection for Kernel Density | |
| layerplotargs | Extract or Replace the Plot Arguments of a Layered Object | |
| influence.ppm | Influence Measure for Spatial Point Process Model | |
| envelope.lpp | Envelope for Point Patterns on Linear Network | |
| affine.linnet | Apply Geometrical Transformations to a Linear Network | |
| beginner | Print Introduction For Beginners | |
| edges2vees | List Dihedral Triples in a Graph | |
| marktable | Tabulate Marks in Neighbourhood of Every Point in a Point Pattern | |
| distmap.ppp | Distance Map of Point Pattern | |
| compatible.im | Test Whether Pixel Images Are Compatible | |
| integral.im | Integral of a Pixel Image | |
| anova.ppm | ANOVA for Fitted Point Process Models | |
| alltypes | Calculate Summary Statistic for All Types in a Multitype Point Pattern | |
| centroid.owin | Centroid of a window | |
| crossdist.default | Pairwise distances between two different sets of points | |
| diameter.owin | Diameter of a Window | |
| diagnose.ppm | Diagnostic Plots for Fitted Point Process Model | |
| cauchy.estpcf | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| infline | Infinite Straight Lines | |
| intensity.lpp | Empirical Intensity of Point Pattern on Linear Network | |
| is.marked.ppp | Test Whether A Point Pattern is Marked | |
| compareFit | Residual Diagnostics for Multiple Fitted Models | |
| leverage.ppm | Leverage Measure for Spatial Point Process Model | |
| adaptive.density | Intensity Estimate of Point Pattern Using Tessellation | |
| dilated.areas | Areas of Morphological Dilations | |
| as.ppp | Convert Data To Class ppp | |
| as.data.frame.im | Convert Pixel Image to Data Frame | |
| dirichlet | Dirichlet Tessellation of Point Pattern | |
| bdist.points | Distance to Boundary of Window | |
| crossdist | Pairwise distances | |
| envelope.pp3 | Simulation Envelopes of Summary Function for 3D Point Pattern | |
| erosion | Morphological Erosion | |
| mincontrast | Method of Minimum Contrast | |
| cbind.hyperframe | Combine Hyperframes by Rows or by Columns | |
| ippm | Optimise Irregular Trend Parameters in Point Process Model | |
| complement.owin | Take Complement of a Window | |
| nbfires | Point Patterns of New Brunswick Forest Fires | |
| envelope | Simulation Envelopes of Summary Function | |
| is.lpp | Test Whether An Object Is A Point Pattern on a Linear Network | |
| connected | Connected components | |
| coords | Extract or Change Coordinates of a Spatial or Spatiotemporal Point Pattern | |
| edge.Ripley | Ripley's Isotropic Edge Correction | |
| cut.im | Convert Pixel Image from Numeric to Factor | |
| distmap | Distance Map | |
| japanesepines | Japanese Pines Point Pattern | |
| linim | Create Pixel Image on Linear Network | |
| is.marked | Test Whether Marks Are Present | |
| concatxy | Concatenate x,y Coordinate Vectors | |
| commonGrid | Determine A Common Spatial Domain And Pixel Resolution | |
| pairdist.psp | Pairwise distances between line segments | |
| linearKinhom | Inhomogeneous Linear K Function | |
| localK | Neighbourhood density function | |
| cut.ppp | Classify Points in a Point Pattern | |
| areaLoss | Difference of Disc Areas | |
| is.rectangle | Determine Type of Window | |
| disc | Circular Window | |
| diameter | Diameter of an Object | |
| interp.im | Interpolate a Pixel Image | |
| linnet | Create a Linear Network | |
| linearKcross | Multitype K Function (Cross-type) for Linear Point Pattern | |
| kaplan.meier | Kaplan-Meier Estimator using Histogram Data | |
| is.multitype | Test whether Object is Multitype | |
| gorillas | Gorilla Nesting Sites | |
| distmap.psp | Distance Map of Line Segment Pattern | |
| linearpcfcross | Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| print.owin | Print Brief Details of a Spatial Window | |
| linearpcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| pairdist.default | Pairwise distances | |
| ewcdf | Weighted Empirical Cumulative Distribution Function | |
| edge.Trans | Translation Edge Correction | |
| demopat | Artificial Data Point Pattern | |
| as.im | Convert to Pixel Image | |
| hamster | Aherne's hamster tumour data | |
| eval.linim | Evaluate Expression Involving Pixel Images on Linear Network | |
| print.ppm | Print a Fitted Point Process Model | |
| is.convex | Test Whether a Window is Convex | |
| pcf.ppp | Pair Correlation Function of Point Pattern | |
| nnmap | K-th Nearest Point Map | |
| pcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-Type) | |
| pixellate | Convert Spatial Object to Pixel Image | |
| eval.fv | Evaluate Expression Involving Functions | |
| effectfun | Compute Fitted Effect of a Spatial Covariate in a Point Process Model | |
| default.expand | Default Expansion Rule for Simulation of Model | |
| longleaf | Longleaf Pines Point Pattern | |
| fitted.slrm | Fitted Probabilities for Spatial Logistic Regression | |
| marks | Marks of a Point Pattern | |
| pixellate.psp | Convert Line Segment Pattern to Pixel Image | |
| endpoints.psp | Endpoints of Line Segment Pattern | |
| fasp.object | Function Arrays for Spatial Patterns | |
| eroded.areas | Areas of Morphological Erosions | |
| lineardisc | Compute Disc of Given Radius in Linear Network | |
| deltametric | Delta Metric | |
| gridcentres | Rectangular grid of points | |
| msr | Signed or Vector-Valued Measure | |
| intensity | Intensity of a Dataset or a Model | |
| hybrid.family | Hybrid Interaction Family | |
| nearestsegment | Find Line Segment Nearest to Each Point | |
| is.multitype.ppp | Test Whether A Point Pattern is Multitype | |
| as.hyperframe | Convert Data to Hyperframe | |
| delaunay.distance | Distance on Delaunay Triangulation | |
| lengths.psp | Lengths of Line Segments | |
| intersect.tess | Intersection of Two Tessellations | |
| convolve.im | Convolution of Pixel Images | |
| nnfun | Nearest Neighbour Map as a Function | |
| is.stationary | Recognise Stationary and Poisson Point Process Models | |
| istat | Point and Click Interface for Exploratory Analysis of Point Pattern | |
| plot.pp3 | Plot a three-dimensional point pattern | |
| plot.kppm | Plot a fitted cluster point process | |
| letterR | Window in Shape of Letter R | |
| pixellate.ppp | Convert Point Pattern to Pixel Image | |
| npfun | Dummy Function Returns Number of Points | |
| multiplicity.ppp | Count Multiplicity of Duplicate Points | |
| density.splitppp | Kernel Smoothed Intensity of Split Point Pattern | |
| interp.colourmap | Interpolate smoothly between specified colours | |
| is.subset.owin | Determine Whether One Window is Contained In Another | |
| intensity.ppp | Empirical Intensity of Point Pattern | |
| plot.ppp | plot a Spatial Point Pattern | |
| bounding.box | Bounding Box of a Window or Point Pattern | |
| finpines | Pine saplings in Finland. | |
| is.im | Test Whether An Object Is A Pixel Image | |
| data.ppm | Extract Original Data from a Fitted Point Process Model | |
| idw | Inverse-distance weighted smoothing of observations at irregular points | |
| discpartarea | Area of Part of Disc | |
| linearmarkconnect | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| flu | Influenza Virus Proteins | |
| exactMPLEstrauss | Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process | |
| lurking | Lurking variable plot | |
| methods.linfun | Methods for Functions on Linear Network | |
| matchingdist | Distance for a Point Pattern Matching | |
| model.depends | Identify Covariates Involved in each Model Term | |
| fitted.ppm | Fitted Conditional Intensity for Point Process Model | |
| fv | Create a Function Value Table | |
| nnmark | Mark of Nearest Neighbour | |
| ponderosa | Ponderosa Pine Tree Point Pattern | |
| pcfdot | Multitype pair correlation function (i-to-any) | |
| pppdist | Distance Between Two Point Patterns | |
| print.im | Print Brief Details of an Image | |
| humberside | Humberside Data on Childhood Leukaemia and Lymphoma | |
| linearKdot.inhom | Inhomogeneous multitype K Function (Dot-type) for Linear Point Pattern | |
| methods.rho2hat | Methods for Intensity Functions of Two Spatial Covariates | |
| redwoodfull | California Redwoods Point Pattern (Entire Dataset) | |
| markstat | Summarise Marks in Every Neighbourhood in a Point Pattern | |
| psp.object | Class of Line Segment Patterns | |
| nndist | Nearest neighbour distances | |
| im.object | Class of Images | |
| ord.family | Ord Interaction Process Family | |
| plot.slrm | Plot a Fitted Spatial Logistic Regression | |
| inforder.family | Infinite Order Interaction Family | |
| plot.leverage.ppm | Plot Leverage Function | |
| pcf.fv | Pair Correlation Function obtained from K Function | |
| quad.ppm | Extract Quadrature Scheme Used to Fit a Point Process Model | |
| pixellate.owin | Convert Window to Pixel Image | |
| linearK | Linear K Function | |
| pcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type) | |
| rmhstart | Determine Initial State for Metropolis-Hastings Simulation. | |
| kstest.ppm | Kolmogorov-Smirnov Test for Point Pattern or Point Process Model | |
| identify.psp | Identify Segments in a Line Segment Pattern | |
| ppp.object | Class of Point Patterns | |
| nncross.lpp | Nearest Neighbours on a Linear Network | |
| rounding | Detect Numerical Rounding | |
| persp.im | Perspective Plot of Pixel Image | |
| plot.owin | Plot a Spatial Window | |
| linearmarkequal | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| predict.ppm | Prediction from a Fitted Point Process Model | |
| model.frame.ppm | Extract the Variables in a Point Process Model | |
| methods.box3 | Methods for Three-Dimensional Box | |
| print.quad | Print a Quadrature Scheme | |
| project2segment | Move Point To Nearest Line | |
| rStraussHard | Perfect Simulation of the Strauss-Hardcore Process | |
| mean.im | Maximum, Minimum, Mean, Median, Range or Sum of Pixel Values in an Image | |
| levelset | Level Set of a Pixel Image | |
| linearpcf | Linear Pair Correlation Function | |
| rPoissonCluster | Simulate Poisson Cluster Process | |
| ppp | Create a Point Pattern | |
| latest.news | Print News About Latest Version of Package | |
| im | Create a Pixel Image Object | |
| model.matrix.slrm | Extract Design Matrix from Spatial Logistic Regression Model | |
| hyytiala | Scots pines and other trees at Hyytiala | |
| plot.plotppm | Plot a plotppm Object Created by plot.ppm | |
| methods.pp3 | Methods for three-dimensional point patterns | |
| nnclean | Nearest Neighbour Clutter Removal | |
| murchison | Murchison gold deposits | |
| print.psp | Print Brief Details of a Line Segment Pattern Dataset | |
| rDiggleGratton | Perfect Simulation of the Diggle-Gratton Process | |
| fv.object | Function Value Table | |
| ppm.object | Class of Fitted Point Process Models | |
| is.marked.ppm | Test Whether A Point Process Model is Marked | |
| lohboot | Bootstrap Confidence Bands for Summary Function | |
| quantile.im | Sample Quantiles of Pixel Image | |
| profilepl | Profile Maximum Pseudolikelihood | |
| identify.ppp | Identify Points in a Point Pattern | |
| nncross | Nearest Neighbours Between Two Patterns | |
| rMaternII | Simulate Matern Model II | |
| rcell | Simulate Baddeley-Silverman Cell Process | |
| rGaussPoisson | Simulate Gauss-Poisson Process | |
| quadrat.test | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts | |
| rescale.psp | Convert Line Segment Pattern to Another Unit of Length | |
| markcorrint | Mark Correlation Integral | |
| nearest.raster.point | Find Pixel Nearest to a Given Point | |
| miplot | Morishita Index Plot | |
| formula.fv | Extract or Change the Plot Formula for a Function Value Table | |
| nncross.pp3 | Nearest Neighbours Between Two Patterns in 3D | |
| km.rs | Kaplan-Meier and Reduced Sample Estimator using Histograms | |
| methods.lpp | Methods for Point Patterns on a Linear Network | |
| fitin.ppm | Extract the Interaction from a Fitted Point Process Model | |
| logLik.ppm | Log Likelihood and AIC for Point Process Model | |
| osteo | Osteocyte Lacunae Data: Replicated Three-Dimensional Point Patterns | |
| rhohat | Smoothing Estimate of Covariate Transformation | |
| eval.im | Evaluate Expression Involving Pixel Images | |
| kppm | Fit Cluster or Cox Point Process Model | |
| density.ppp | Kernel Smoothed Intensity of Point Pattern | |
| nztrees | New Zealand Trees Point Pattern | |
| linearKdot | Multitype K Function (Dot-type) for Linear Point Pattern | |
| nnwhich | Nearest neighbour | |
| plot.psp | plot a Spatial Line Segment Pattern | |
| rVarGamma | Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel | |
| layered | Create List of Plotting Layers | |
| pcf3est | Pair Correlation Function of a Three-Dimensional Point Pattern | |
| raster.x | Cartesian Coordinates for a Pixel Raster | |
| markconnect | Mark Connection Function | |
| nsegments | Number of Line Segments in a Line Segment Pattern | |
| plot.hyperframe | Plot Entries in a Hyperframe | |
| rescale | Convert dataset to another unit of length | |
| owin | Create a Window | |
| iplot | Point and Click Interface for Displaying Spatial Data | |
| update.rmhcontrol | Update Control Parameters of Metropolis-Hastings Algorithm | |
| rmhmodel.list | Define Point Process Model for Metropolis-Hastings Simulation. | |
| spatstat-internal | Internal spatstat functions | |
| qqplot.ppm | Q-Q Plot of Residuals from Fitted Point Process Model | |
| rlinegrid | Generate grid of parallel lines with random displacement | |
| is.hybrid | Test Whether Object is a Hybrid | |
| pairdist.ppx | Pairwise Distances in Any Dimensions | |
| pool | Pool Data | |
| predict.lppm | Predict Point Process Model on Linear Network | |
| rmh | Simulate point patterns using the Metropolis-Hastings algorithm. | |
| lansing | Lansing Woods Point Pattern | |
| shift.ppp | Apply Vector Translation To Point Pattern | |
| quadscheme | Generate a Quadrature Scheme from a Point Pattern | |
| nndist.psp | Nearest neighbour distances between line segments | |
| pairdist.ppp | Pairwise distances | |
| unique.ppp | Extract Unique Points from a Spatial Point Pattern | |
| rHardcore | Perfect Simulation of the Hardcore Process | |
| ppm | Fit Point Process Model to Data | |
| rescale.owin | Convert Window to Another Unit of Length | |
| rStrauss | Perfect Simulation of the Strauss Process | |
| pairdist.lpp | Pairwise shortest-path distances between points on a linear network | |
| residuals.ppm | Residuals for Fitted Point Process Model | |
| plot.im | Plot a Pixel Image | |
| progressreport | Print Progress Reports | |
| linearpcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| rmpoispp | Generate Multitype Poisson Point Pattern | |
| pcf.fasp | Pair Correlation Function obtained from array of K functions | |
| rCauchy | Simulate Neyman-Scott Point Process with Cauchy cluster kernel | |
| rotate.ppp | Rotate a Point Pattern | |
| plot.linnet | Plot a linear network | |
| rmhmodel.ppm | Interpret Fitted Model for Metropolis-Hastings Simulation. | |
| reflect | Reflect In Origin | |
| suffstat | Sufficient Statistic of Point Process Model | |
| rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |
| shift | Apply Vector Translation | |
| is.ppp | Test Whether An Object Is A Point Pattern | |
| rpoisline | Generate Poisson Random Line Process | |
| psst | Pseudoscore Diagnostic For Fitted Model against General Alternative | |
| rshift.psp | Randomly Shift a Line Segment Pattern | |
| reach | Interaction Distance of a Point Process | |
| scalardilate | Apply Scalar Dilation | |
| rthin | Random Thinning | |
| rThomas | Simulate Thomas Process | |
| rjitter | Random Perturbation of a Point Pattern | |
| methods.boxx | Methods for Multi-Dimensional Box | |
| rstrat | Simulate Stratified Random Point Pattern | |
| round.ppp | Apply Numerical Rounding to Spatial Coordinates | |
| rat | Ratio object | |
| lpp | Create Point Pattern on Linear Network | |
| matclust.estpcf | Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation | |
| shift.psp | Apply Vector Translation To Line Segment Pattern | |
| rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |
| vcov.slrm | Variance-Covariance Matrix for a Fitted Spatial Logistic Regression | |
| markvario | Mark Variogram | |
| methods.fii | Methods for Fitted Interactions | |
| whist | Weighted Histogram | |
| methods.funxy | Methods for Spatial Functions | |
| linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |
| psstG | Pseudoscore Diagnostic For Fitted Model against Saturation Alternative | |
| psp | Create a Line Segment Pattern | |
| midpoints.psp | Midpoints of Line Segment Pattern | |
| methods.kppm | Methods for Cluster Point Process Models | |
| will.expand | Test Expansion Rule | |
| rlabel | Random Re-Labelling of Point Pattern | |
| plot.layered | Layered Plot | |
| is.empty | Test Whether An Object Is Empty | |
| split.ppx | Divide Multidimensional Point Pattern into Sub-patterns | |
| scan.test | Spatial Scan Test | |
| runifpoint3 | Generate N Uniform Random Points in Three Dimensions | |
| pairsat.family | Saturated Pairwise Interaction Point Process Family | |
| simdat | Simulated Point Pattern | |
| quadratresample | Resample a Point Pattern by Resampling Quadrats | |
| paracou | Kimboto trees at Paracou, French Guiana | |
| model.matrix.ppm | Extract Design Matrix from Point Process Model | |
| rpoislinetess | Poisson Line Tessellation | |
| model.images | Compute Images of Constructed Covariates | |
| plot.envelope | Plot a Simulation Envelope | |
| matclust.estK | Fit the Matern Cluster Point Process by Minimum Contrast | |
| overlap.owin | Compute Area of Overlap | |
| summary.ppp | Summary of a Point Pattern Dataset | |
| timed | Record the Computation Time | |
| pool.envelope | Pool Data from Several Envelopes | |
| rho2hat | Smoothed Relative Density of Pairs of Covariate Values | |
| rMosaicField | Mosaic Random Field | |
| shift.im | Apply Vector Translation To Pixel Image | |
| rpoint | Generate N Random Points | |
| rmh.ppm | Simulate from a Fitted Point Process Model | |
| rmh.default | Simulate Point Process Models using the Metropolis-Hastings Algorithm. | |
| summary.splitppp | Summary of a Split Point Pattern | |
| vcov.kppm | Variance-Covariance Matrix for a Fitted Cluster Point Process Model | |
| simulate.kppm | Simulate a Fitted Cluster Point Process Model | |
| thomas.estpcf | Fit the Thomas Point Process by Minimum Contrast | |
| setcov | Set Covariance of a Window | |
| rNeymanScott | Simulate Neyman-Scott Process | |
| summary.listof | Summary of a List of Things | |
| summary.ppm | Summarizing a Fitted Point Process Model | |
| smooth.fv | Apply Smoothing to Function Values | |
| transect.im | Pixel Values Along a Transect | |
| vertices | Vertices of a Window | |
| rmhcontrol | Set Control Parameters for Metropolis-Hastings Algorithm. | |
| rsyst | Simulate systematic random point pattern | |
| scaletointerval | Rescale Data to Lie Between Specified Limits | |
| pppmatching | Create a Point Matching | |
| methods.layered | Methods for Layered Objects | |
| localpcf | Local pair correlation function | |
| plot.linim | Plot Pixel Image on Linear Network | |
| summary.psp | Summary of a Line Segment Pattern Dataset | |
| stratrand | Stratified random point pattern | |
| redwood | California Redwoods Point Pattern (Ripley's Subset) | |
| runifdisc | Generate N Uniform Random Points in a Disc | |
| rotate.im | Rotate a Pixel Image | |
| methods.ppx | Methods for Multidimensional Space-Time Point Patterns | |
| smooth.msr | Smooth a Signed or Vector-Valued Measure | |
| rDGS | Perfect Simulation of the Diggle-Gates-Stibbard Process | |
| rshift.splitppp | Randomly Shift a List of Point Patterns | |
| spatstat-deprecated | Deprecated spatstat functions | |
| methods.rhohat | Methods for Intensity Functions of Spatial Covariate | |
| rpoislpp | Poisson Point Process on a Linear Network | |
| superimpose | Superimpose Several Geometric Patterns | |
| rotate | Rotate | |
| pairdist.pp3 | Pairwise distances in Three Dimensions | |
| simulate.slrm | Simulate a Fitted Spatial Logistic Regression Model | |
| runiflpp | Uniform Random Points on a Linear Network | |
| nndist.lpp | Nearest neighbour distances on a linear network | |
| ppx | Multidimensional Space-Time Point Pattern | |
| triplet.family | Triplet Interaction Family | |
| rMaternI | Simulate Matern Model I | |
| opening | Morphological Opening | |
| nnwhich.pp3 | Nearest neighbours in three dimensions | |
| runifpointOnLines | Generate N Uniform Random Points On Line Segments | |
| scanpp | Read Point Pattern From Data File | |
| union.quad | Union of Data and Dummy Points | |
| nncorr | Nearest-Neighbour Correlation Indices of Marked Point Pattern | |
| plot.tess | Plot a tessellation | |
| sharpen | Data Sharpening of Point Pattern | |
| mucosa | Cells in Gastric Mucosa | |
| rotate.owin | Rotate a Window | |
| rshift.ppp | Randomly Shift a Point Pattern | |
| urkiola | Urkiola Woods Point Pattern | |
| summary.owin | Summary of a Spatial Window | |
| pointsOnLines | Place Points Evenly Along Specified Lines | |
| waka | Trees in Waka national park | |
| spatstat.options | Internal Options in Spatstat Package | |
| rSSI | Simulate Simple Sequential Inhibition | |
| sidelengths.owin | Side Lengths of Enclosing Rectangle of a Window | |
| rmhmodel | Define Point Process Model for Metropolis-Hastings Simulation. | |
| spruces | Spruces Point Pattern | |
| selfcrossing.psp | Crossing Points in a Line Segment Pattern | |
| solutionset | Evaluate Logical Expression Involving Pixel Images and Return Region Where Expression is True | |
| tile.areas | Compute Areas of Tiles in a Tessellation | |
| shift.owin | Apply Vector Translation To Window | |
| which.max.im | Identify Pixelwise Maximum of Several Pixel Images | |
| simplify.owin | Approximate a Polygon by a Simpler Polygon | |
| sumouter | Compute Quadratic Forms | |
| with.fv | Evaluate an Expression in a Function Table | |
| vargamma.estK | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| update.ppm | Update a Fitted Point Process Model | |
| spatstat-package | The Spatstat Package | |
| split.ppp | Divide Point Pattern into Sub-patterns | |
| summary.quad | Summarizing a Quadrature Scheme | |
| pcfcross | Multitype pair correlation function (cross-type) | |
| swedishpines | Swedish Pines Point Pattern | |
| vargamma.estpcf | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| rMatClust | Simulate Matern Cluster Process | |
| pcfinhom | Inhomogeneous Pair Correlation Function | |
| pool.fasp | Pool Data from Several Function Arrays | |
| rgbim | Create Colour-Valued Pixel Image | |
| rescale.ppp | Convert Point Pattern to Another Unit of Length | |
| tiles | Extract List of Tiles in a Tessellation | |
| varblock | Estimate Variance of Summary Statistic by Subdivision | |
| runifpoint | Generate N Uniform Random Points | |
| pairs.im | Scatterplot Matrix for Pixel Images | |
| tess | Create a Tessellation | |
| pixelquad | Quadrature Scheme Based on Pixel Grid | |
| plot.listof | Plot a List of Things | |
| pool.quadrattest | Pool Several Quadrat Tests | |
| unmark | Remove Marks | |
| pool.rat | Pool Data from Several Ratio Objects | |
| rMosaicSet | Mosaic Random Set | |
| rotate.psp | Rotate a Line Segment Pattern | |
| smooth.ppp | Spatial smoothing of observations at irregular points | |
| predict.kppm | Prediction from a Fitted Cluster Point Process Model | |
| reduced.sample | Reduced Sample Estimator using Histogram Data | |
| pppmatching.object | Class of Point Matchings | |
| rknn | Theoretical Distribution of Nearest Neighbour Distance | |
| relrisk | Nonparametric Estimate of Spatially-Varying Relative Risk | |
| rmhmodel.default | Build Point Process Model for Metropolis-Hastings Simulation. | |
| summary.im | Summarizing a Pixel Image | |
| vcov.ppm | Variance-Covariance Matrix for a Fitted Point Process Model | |
| Extract.listof | Extract or Replace Subset of a List of Things | |
| Jest | Estimate the J-function | |
| Linhom | L-function | |
| MultiHard | The Multitype Hard Core Point Process Model | |
| Smooth | Spatial smoothing of data | |
| append.psp | Combine Two Line Segment Patterns | |
| bdist.tiles | Distance to Boundary of Window | |
| bw.relrisk | Cross Validated Bandwidth Selection for Relative Risk Estimation | |
| bronzefilter | Bronze gradient filter data | |
| closing | Morphological Closing | |
| dclf.progress | Progress Plot of Test of Spatial Pattern | |
| default.rmhcontrol | Set Default Control Parameters for Metropolis-Hastings Algorithm. | |
| duplicated.ppp | Determine Duplicated Points in a Spatial Point Pattern | |
| intersect.owin | Intersection, Union or Set Subtraction of Two Windows | |
| is.ppm | Test Whether An Object Is A Fitted Point Process Model | |
| localKinhom | Inhomogeneous Neighbourhood Density Function | |
| methods.lppm | Methods for Fitted Point Process Models on a Linear Network | |
| nnwhich.ppx | Nearest Neighbours in Any Dimensions | |
| owin.object | Class owin | |
| plot.bermantest | Plot Result of Berman Test | |
| plot.fv | Plot Function Values | |
| plot.kstest | Plot a Spatial Kolmogorov-Smirnov Test | |
| predict.slrm | Predicted or Fitted Values from Spatial Logistic Regression | |
| quad.object | Class of Quadrature Schemes | |
| simulate.ppm | Simulate a Fitted Gibbs Point Process Model | |
| AreaInter | The Area Interaction Point Process Model | |
| Extract.msr | Extract Subset of Signed or Vector Measure | |
| Extract.psp | Extract Subset of Line Segment Pattern | |
| Kmulti.inhom | Inhomogeneous Marked K-Function | |
| SatPiece | Piecewise Constant Saturated Pairwise Interaction Point Process Model | |
| affine | Apply Affine Transformation | |
| affine.ppp | Apply Affine Transformation To Point Pattern | |
| as.data.frame.hyperframe | Coerce Hyperframe to Data Frame | |
| clarkevans.test | Clark and Evans Test | |
| contour.im | Contour plot of pixel image | |
| crossdist.pp3 | Pairwise distances between two different three-dimensional point patterns | |
| dfbetas.ppm | Parameter influence measure | |
| crossing.psp | Crossing Points of Two Line Segment Patterns | |
| discretise | Safely Convert Point Pattern Window to Binary Mask | |
| envelope.envelope | Recompute Envelopes | |
| ganglia | Beta Ganglion Cells in Cat Retina, Old Version | |
| harmonise.im | Make Pixel Images Compatible | |
| formula.ppm | Model Formulae for Gibbs Point Process Models | |
| gridweights | Compute Quadrature Weights Based on Grid Counts | |
| methods.linnet | Methods for Linear Networks | |
| pairwise.family | Pairwise Interaction Process Family | |
| plot.influence.ppm | Plot Influence Measure | |
| plot.msr | Plot a Signed or Vector-Valued Measure | |
| quadrat.test.splitppp | Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts | |
| rescue.rectangle | Convert Window Back To Rectangle | |
| residualspaper | Data and Code From JRSS Discussion Paper on Residuals | |
| rshift | Random Shift | |
| split.im | Divide Image Into Sub-images | |
| stieltjes | Compute Integral of Function Against Cumulative Distribution | |
| zapsmall.im | Rounding of Pixel Values | |
| Softcore | The Soft Core Point Process Model | |
| anemones | Beadlet Anemones Data | |
| as.interact | Extract Interaction Structure | |
| by.im | Apply Function to Image Broken Down by Factor | |
| default.dummy | Generate a Default Pattern of Dummy Points | |
| expand.owin | Apply Expansion Rule | |
| harmonic | Basis for Harmonic Functions | |
| heather | Diggle's Heather Data | |
| incircle | Find Largest Circle Inside Window | |
| lgcp.estpcf | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| linfun | Function on a Linear Network | |
| logLik.slrm | Loglikelihood of Spatial Logistic Regression | |
| linearKcross.inhom | Inhomogeneous multitype K Function (Cross-type) for Linear Point Pattern | |
| marks.psp | Marks of a Line Segment Pattern | |
| methods.slrm | Methods for Spatial Logistic Regression Models | |
| methods.units | Methods for Units | |
| markcorr | Mark Correlation Function | |
| nndist.ppx | Nearest Neighbour Distances in Any Dimensions | |
| nndist.pp3 | Nearest neighbour distances in three dimensions | |
| nnfun.lpp | Nearest Neighbour Map on Linear Network | |
| periodify | Make Periodic Copies of a Spatial Pattern | |
| plot.ppm | plot a Fitted Point Process Model | |
| plot.splitppp | Plot a List of Point Patterns | |
| quadrats | Divide Region into Quadrats | |
| quadscheme.logi | Generate a Logistic Regression Quadrature Scheme from a Point Pattern | |
| rpoispp | Generate Poisson Point Pattern | |
| slrm | Spatial Logistic Regression | |
| square | Square Window | |
| tweak.colourmap | Change Colour Values in a Colour Map | |
| unitname | Name for Unit of Length | |
| unnormdensity | Weighted kernel smoother | |
| update.kppm | Update a Fitted Cluster Point Process Model | |
| with.hyperframe | Evaluate an Expression in Each Row of a Hyperframe | |
| Extract.lpp | Extract Subset of Point Pattern on Linear Network | |
| Gdot | Multitype Nearest Neighbour Distance Function (i-to-any) | |
| Jcross | Multitype J Function (i-to-j) | |
| LennardJones | The Lennard-Jones Potential | |
| anova.lppm | ANOVA for Fitted Point Process Models on Linear Network | |
| as.matrix.owin | Convert Pixel Image to Matrix | |
| clusterset | Allard-Fraley Estimator of Cluster Feature | |
| convexhull.xy | Convex Hull of Points | |
| copper | Berman-Huntington points and lines data | |
| coef.ppm | Coefficients of Fitted Point Process Model | |
| dummify | Convert Data to Numeric Values by Constructing Dummy Variables | |
| flipxy | Exchange X and Y Coordinates | |
| funxy | Spatial Function Class | |
| intensity.ppm | Intensity of Fitted Point Process Model | |
| lppm | Fit Point Process Model to Point Pattern on Linear Network | |
| lut | Lookup Tables | |
| npoints | Number of Points in a Point Pattern | |
| plot.colourmap | Plot a Colour Map | |
| plot.lppm | Plot a Fitted Point Process Model on a Linear Network | |
| pp3 | Three Dimensional Point Pattern | |
| quadratcount | Quadrat counting for a point pattern | |
| print.ppp | Print Brief Details of a Point Pattern Dataset | |
| rLGCP | Simulate Log-Gaussian Cox Process | |
| rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |
| tilenames | Names of Tiles in a Tessellation | |
| trim.rectangle | Cut margins from rectangle | |
| Extract.owin | Extract Subset of Window | |
| Finhom | Inhomogeneous Empty Space Function | |
| as.tess | Convert Data To Tessellation | |
| Kmeasure | Reduced Second Moment Measure | |
| bounding.box.xy | Convex Hull of Points | |
| bw.ppl | Likelihood Cross Validation Bandwidth Selection for Kernel Density | |
| chicago | Chicago Street Crime Data | |
| chorley | Chorley-Ribble Cancer Data | |
| collapse.fv | Collapse Several Function Tables into One | |
| colourmap | Colour Lookup Tables | |
| corners | Corners of a rectangle | |
| eem | Exponential Energy Marks | |
| fryplot | Fry Plot of Point Pattern | |
| hist.im | Histogram of Pixel Values in an Image | |
| imcov | Spatial Covariance of a Pixel Image | |
| lgcp.estK | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| nnwhich.lpp | Identify Nearest Neighbours on a Linear Network | |
| pairdist | Pairwise distances | |
| pcf | Pair Correlation Function | |
| perimeter | Perimeter Length of Window | |
| parres | Partial Residuals for Point Process Model | |
| plot.fasp | Plot a Function Array | |
| project.ppm | Force Point Process Model to be Valid | |
| psstA | Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative | |
| plot.quad | plot a Spatial Quadrature Scheme | |
| rescale.im | Convert Pixel Image to Another Unit of Length | |
| ripras | Estimate window from points alone | |
| rmhexpand | Specify Simulation Window or Expansion Rule | |
| rmpoint | Generate N Random Multitype Points | |
| runifpointx | Generate N Uniform Random Points in Any Dimensions | |
| sessionLibs | Print Names and Version Numbers of Libraries Loaded | |
| shapley | Galaxies in the Shapley Supercluster | |
| simplenet | Simple Example of Linear Network | |
| spokes | Spokes pattern of dummy points | |
| thomas.estK | Fit the Thomas Point Process by Minimum Contrast | |
| valid.ppm | Check Whether Point Process Model is Valid | |
| volume | Volume of an Object | |
| rcellnumber | Generate Random Numbers of Points for Cell Process | |
| No Results! | ||
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Details
| Date | 2013-11-03 |
| License | GPL (>= 2) |
| URL | http://www.spatstat.org |
| LazyData | true |
| LazyLoad | true |
| ByteCompile | true |
| Packaged | 2013-11-03 05:22:24 UTC; adrian |
| NeedsCompilation | yes |
| Repository | CRAN |
| Date/Publication | 2013-11-03 10:34:29 |
| depends | abind , base (>= 3.0.2) , deldir (>= 0.0-21) , graphics , grDevices , mgcv , polyclip , R (>= 3.0.2) , stats , tensor , utils |
| suggests | gsl , locfit , maptools , RandomFields (>= 2.0.60) , rpanel , scatterplot3d , sm , spatial , tkrplot |
| Contributors | Rolf Turner, Adrian Baddeley |
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