spatstat v1.43-0
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Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Comprehensive open-source toolbox 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, space-time point patterns in any number of dimensions, and point patterns on a linear network.
Contains over 2000 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, tessellations, and linear networks.
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, dppm similar to glm. Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal 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.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm. The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise, AIC). 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 | |
| Extract.solist | Extract or Replace Subset of a List of Spatial Objest | |
| Extract.ppx | Extract Subset of Multidimensional Point Pattern | |
| Extract.fasp | Extract Subset of Function Array | |
| Hest | Spherical Contact Distribution Function | |
| DiggleGatesStibbard | Diggle-Gates-Stibbard Point Process Model | |
| AreaInter | The Area Interaction Point Process Model | |
| Concom | The Connected Component Process Model | |
| Gest | Nearest Neighbour Distance Function G | |
| Extract.quad | Subset of Quadrature Scheme | |
| Extract.owin | Extract Subset of Window | |
| Extract.ppp | Extract or Replace Subset of Point Pattern | |
| Frame | Extract or Change the Containing Rectangle of a Spatial Object | |
| Kscaled | Locally Scaled K-function | |
| Kinhom | Inhomogeneous K-function | |
| Extract.anylist | Extract or Replace Subset of a List of Things | |
| Extract.influence.ppm | Extract Subset of Influence Object | |
| Finhom | Inhomogeneous Empty Space Function | |
| Kcross.inhom | Inhomogeneous Cross K Function | |
| Extract.psp | Extract Subset of Line Segment Pattern | |
| PairPiece | The Piecewise Constant Pairwise Interaction Point Process Model | |
| Extract.fv | Extract or Replace Subset of Function Values | |
| SatPiece | Piecewise Constant Saturated Pairwise Interaction Point Process Model | |
| Hardcore | The Hard Core Point Process Model | |
| Gcom | Model Compensator of Nearest Neighbour Function | |
| F3est | Empty Space Function of a Three-Dimensional Point Pattern | |
| Kdot | Multitype K Function (i-to-any) | |
| HierStraussHard | The Hierarchical Strauss Hard Core Point Process Model | |
| Extract.tess | Extract or Replace Subset of Tessellation | |
| Fiksel | The Fiksel Interaction | |
| Gcross | Multitype Nearest Neighbour Distance Function (i-to-j) | |
| Extract.leverage.ppm | Extract Subset of Leverage Object | |
| LambertW | Lambert's W Function | |
| Kmulti.inhom | Inhomogeneous Marked K-Function | |
| Fest | Estimate the Empty Space Function or its Hazard Rate | |
| Poisson | Poisson Point Process Model | |
| K3est | K-function of a Three-Dimensional Point Pattern | |
| affine.owin | Apply Affine Transformation To Window | |
| G3est | Nearest Neighbour Distance Distribution Function of a Three-Dimensional Point Pattern | |
| CDF | Cumulative Distribution Function From Kernel Density Estimate | |
| Extract.splitppp | Extract or Replace Sub-Patterns | |
| affine.im | Apply Affine Transformation To Pixel Image | |
| Linhom | L-function | |
| add.texture | Fill Plot With Texture | |
| OrdThresh | Ord's Interaction model | |
| Kcross | Multitype K Function (Cross-type) | |
| PPversion | Transform a Function into its P-P or Q-Q Version | |
| affine.lpp | Apply Geometrical Transformations to Point Pattern on a Linear Network | |
| Jinhom | Inhomogeneous J-function | |
| adaptive.density | Intensity Estimate of Point Pattern Using Tessellation | |
| Lcross | Multitype L-function (cross-type) | |
| MultiStrauss | The Multitype Strauss Point Process Model | |
| Math.im | S3 Group Generic methods for images | |
| Jcross | Multitype J Function (i-to-j) | |
| affine.linnet | Apply Geometrical Transformations to a Linear Network | |
| Geyer | Geyer's Saturation Point Process Model | |
| Kmark | Mark-Weighted K Function | |
| Extract.msr | Extract Subset of Signed or Vector Measure | |
| Emark | Diagnostics for random marking | |
| Gdot | Multitype Nearest Neighbour Distance Function (i-to-any) | |
| Ginhom | Inhomogeneous Nearest Neighbour Function | |
| allstats | Calculate four standard summary functions of a point pattern. | |
| Gfox | Foxall's Distance Functions | |
| Extract.layered | Extract or Replace Subset of a Layered Object | |
| Iest | Estimate the I-function | |
| Strauss | The Strauss Point Process Model | |
| Kdot.inhom | Inhomogeneous Multitype K Dot Function | |
| Hybrid | Hybrid Interaction Point Process Model | |
| Jest | Estimate the J-function | |
| Extract.im | Extract Subset of Image | |
| Gmulti | Marked Nearest Neighbour Distance Function | |
| Kest.fft | K-function using FFT | |
| Kmodel.kppm | K Function or Pair Correlation Function of Cluster Model or Cox model | |
| Jdot | Multitype J Function (i-to-any) | |
| Kest | K-function | |
| affine | Apply Affine Transformation | |
| Smooth | Spatial smoothing of data | |
| Gres | Residual G Function | |
| Ksector | Sector K-function | |
| Lcross.inhom | Inhomogeneous Cross Type L Function | |
| Lest | L-function | |
| Triplets | The Triplet Point Process Model | |
| Kmodel | K Function or Pair Correlation Function of a Point Process Model | |
| Smooth.msr | Smooth a Signed or Vector-Valued Measure | |
| Extract.listof | Extract or Replace Subset of a List of Things | |
| Kovesi | Colour Sequences with Uniform Perceptual Contrast | |
| Kmulti | Marked K-Function | |
| Penttinen | Penttinen Interaction | |
| MultiStraussHard | The Multitype/Hard Core Strauss Point Process Model | |
| StraussHard | The Strauss / Hard Core Point Process Model | |
| Tstat | Third order summary statistic | |
| MultiHard | The Multitype Hard Core Point Process Model | |
| Pairwise | Generic Pairwise Interaction model | |
| Kmodel.dppm | K-function or Pair Correlation Function of a Determinantal Point Process Model | |
| Ldot.inhom | Inhomogeneous Multitype L Dot Function | |
| Smooth.ppp | Spatial smoothing of observations at irregular points | |
| Kmodel.ppm | K Function or Pair Correlation Function of Gibbs Point Process model | |
| Math.linim | S3 Group Generic Methods for Images on a Linear Network | |
| affine.ppp | Apply Affine Transformation To Point Pattern | |
| WindowOnly | Extract Window of Spatial Object | |
| affine.psp | Apply Affine Transformation To Line Segment Pattern | |
| Smooth.fv | Apply Smoothing to Function Values | |
| LennardJones | The Lennard-Jones Potential | |
| Softcore | The Soft Core Point Process Model | |
| addvar | Added Variable Plot for Point Process Model | |
| Replace.im | Reset Values in Subset of Image | |
| Jmulti | Marked J Function | |
| Saturated | Saturated Pairwise Interaction model | |
| Window | Extract or Change the Window of a Spatial Object | |
| Smoothfun.ppp | Smooth Interpolation of Marks as a Spatial Function | |
| Extract.linnet | Extract Subset of Linear Network | |
| Kcom | Model Compensator of K Function | |
| Ord | Generic Ord Interaction model | |
| DiggleGratton | Diggle-Gratton model | |
| Extract.lpp | Extract Subset of Point Pattern on Linear Network | |
| Ldot | Multitype L-function (i-to-any) | |
| BadGey | Hybrid Geyer Point Process Model | |
| Extract.hyperframe | Extract or Replace Subset of Hyperframe | |
| HierStrauss | The Hierarchical Strauss Point Process Model | |
| HierHard | The Hierarchical Hard Core Point Process Model | |
| Kmeasure | Reduced Second Moment Measure | |
| Kres | Residual K Function | |
| anylist | List of Objects | |
| as.data.frame.psp | Coerce Line Segment Pattern to a Data Frame | |
| as.mask.psp | Convert Line Segment Pattern to Binary Pixel Mask | |
| circumradius | Circumradius of a Window | |
| clmfires | Castilla-La Mancha Forest Fires | |
| append.psp | Combine Two Line Segment Patterns | |
| bw.stoyan | Stoyan's Rule of Thumb for Bandwidth Selection | |
| beachcolours | Create Colour Scheme for a Range of Numbers | |
| as.boxx | Convert Data to Multi-Dimensional Box | |
| berman.test | Berman's Tests for Point Process Model | |
| cells | Biological Cells Point Pattern | |
| chicago | Chicago Street Crime Data | |
| chop.tess | Subdivide a Window or Tessellation using a Set of Lines | |
| cdf.test | Spatial Distribution Test for Point Pattern or Point Process Model | |
| clickppp | Interactively Add Points | |
| as.data.frame.im | Convert Pixel Image to Data Frame | |
| bdspots | Breakdown Spots in Microelectronic Materials | |
| cbind.hyperframe | Combine Hyperframes by Rows or by Columns | |
| as.interact | Extract Interaction Structure | |
| cut.ppp | Classify Points in a Point Pattern | |
| closepairs.pp3 | Close Pairs of Points in 3 Dimensions | |
| as.ppp | Convert Data To Class ppp | |
| edges2vees | List Dihedral Triples in a Graph | |
| amacrine | Hughes' Amacrine Cell Data | |
| endpoints.psp | Endpoints of Line Segment Pattern | |
| envelope.pp3 | Simulation Envelopes of Summary Function for 3D Point Pattern | |
| default.dummy | Generate a Default Pattern of Dummy Points | |
| anova.lppm | ANOVA for Fitted Point Process Models on Linear Network | |
| applynbd | Apply Function to Every Neighbourhood in a Point Pattern | |
| by.im | Apply Function to Image Broken Down by Factor | |
| auc | Area Under ROC Curve | |
| clusterkernel | Extract Cluster Offspring Kernel | |
| fitin.ppm | Extract the Interaction from a Fitted Point Process Model | |
| as.mask | Pixel Image Approximation of a Window | |
| colourtools | Convert and Compare Colours in Different Formats | |
| delaunayDistance | Distance on Delaunay Triangulation | |
| contour.imlist | Array of Contour Plots | |
| as.tess | Convert Data To Tessellation | |
| clusterset | Allard-Fraley Estimator of Cluster Feature | |
| as.rectangle | Window Frame | |
| closing | Morphological Closing | |
| compatible.fasp | Test Whether Function Arrays Are Compatible | |
| anova.mppm | ANOVA for Fitted Multiple Point Process Models | |
| dfbetas.ppm | Parameter influence measure | |
| border | Border Region of a Window | |
| crossdist.ppx | Pairwise Distances Between Two Different Multi-Dimensional Point Patterns | |
| clusterfield | Field of clusters | |
| circdensity | Density Estimation for Circular Data | |
| clusterradius | Compute or Extract Effective Range of Cluster Kernel | |
| dirichletWeights | Compute Quadrature Weights Based on Dirichlet Tessellation | |
| as.layered | Convert Data To Layered Object | |
| diameter.boxx | Geometrical Calculations for Multi-Dimensional Box | |
| as.ppm | Extract Fitted Point Process Model | |
| density.lpp | Kernel Estimate of Intensity on a Linear Network | |
| clickpoly | Interactively Define a Polygon | |
| by.ppp | Apply a Function to a Point Pattern Broken Down by Factor | |
| as.linnet.psp | Convert Line Segment Pattern to Linear Network | |
| as.function.fv | Convert Function Value Table to Function | |
| clickbox | Interactively Define a Rectangle | |
| eval.fv | Evaluate Expression Involving Functions | |
| data.ppm | Extract Original Data from a Fitted Point Process Model | |
| distmap.psp | Distance Map of Line Segment Pattern | |
| cut.im | Convert Pixel Image from Numeric to Factor | |
| deriv.fv | Calculate Derivative of Function Values | |
| blur | Apply Gaussian Blur to a Pixel Image | |
| distmap.ppp | Distance Map of Point Pattern | |
| density.splitppp | Kernel Smoothed Intensity of Split Point Pattern | |
| bw.frac | Bandwidth Selection Based on Window Geometry | |
| dummify | Convert Data to Numeric Values by Constructing Dummy Variables | |
| edit.hyperframe | Invoke Text Editor on Hyperframe | |
| closepairs | Close Pairs of Points | |
| dppparbounds | Parameter Bound for a Determinantal Point Process Model | |
| clarkevans | Clark and Evans Aggregation Index | |
| bind.fv | Combine Function Value Tables | |
| anemones | Beadlet Anemones Data | |
| gridweights | Compute Quadrature Weights Based on Grid Counts | |
| identify.psp | Identify Segments in a Line Segment Pattern | |
| gridcentres | Rectangular grid of points | |
| harmonise.fv | Make Function Tables Compatible | |
| im.object | Class of Images | |
| connected.ppp | Connected components of a point pattern | |
| dg.envelope | Global Envelopes for Dao-Genton Test | |
| chorley | Chorley-Ribble Cancer Data | |
| bw.relrisk | Cross Validated Bandwidth Selection for Relative Risk Estimation | |
| cdf.test.mppm | Spatial Distribution Test for Multiple Point Process Model | |
| colourmap | Colour Lookup Tables | |
| contour.im | Contour plot of pixel image | |
| dg.test | Dao-Genton Adjusted Goodness-Of-Fit Test | |
| emend.ppm | Force Point Process Model to be Valid | |
| duplicated.ppp | Determine Duplicated Points in a Spatial Point Pattern | |
| delaunayNetwork | Linear Network of Delaunay Triangulation or Dirichlet Tessellation | |
| ellipse | Elliptical Window. | |
| copper | Berman-Huntington points and lines data | |
| density.ppp | Kernel Smoothed Intensity of Point Pattern | |
| incircle | Find Largest Circle Inside Window | |
| integral.msr | Integral of a Measure | |
| as.hyperframe.ppx | Extract coordinates and marks of multidimensional point pattern | |
| fixef.mppm | Extract Fixed Effects from Point Process Model | |
| fasp.object | Function Arrays for Spatial Patterns | |
| diameter | Diameter of an Object | |
| edges2triangles | List Triangles in a Graph | |
| crossdist.default | Pairwise distances between two different sets of points | |
| harmonise.im | Make Pixel Images Compatible | |
| as.polygonal | Convert a Window to a Polygonal Window | |
| as.fv | Convert Data To Class fv | |
| intensity.ppp | Empirical Intensity of Point Pattern | |
| dppm | Fit Determinantal Point Process Model | |
| hopskel | Hopkins-Skellam Test | |
| as.owin | Convert Data To Class owin | |
| convexhull | Convex Hull | |
| flipxy | Exchange X and Y Coordinates | |
| affine.tess | Apply Geometrical Transformation To Tessellation | |
| as.data.frame.ppp | Coerce Point Pattern to a Data Frame | |
| clickdist | Interactively Measure Distance | |
| dclf.sigtrace | Significance Trace of Cressie-Loosmore-Ford or Maximum Absolute Deviation Test | |
| as.linim | Convert to Pixel Image on Linear Network | |
| as.psp | Convert Data To Class psp | |
| dendrite | Dendritic Spines Data | |
| dppMatern | Whittle-Matern Determinantal Point Process Model | |
| corners | Corners of a rectangle | |
| eval.fasp | Evaluate Expression Involving Function Arrays | |
| bdist.points | Distance to Boundary of Window | |
| dirichlet | Dirichlet Tessellation of Point Pattern | |
| bdist.tiles | Distance to Boundary of Window | |
| harmonise.owin | Make Windows Compatible | |
| delaunay | Delaunay Triangulation of Point Pattern | |
| intensity | Intensity of a Dataset or a Model | |
| as.im | Convert to Pixel Image | |
| bw.diggle | Cross Validated Bandwidth Selection for Kernel Density | |
| interp.im | Interpolate a Pixel Image | |
| expand.owin | Apply Expansion Rule | |
| integral.linim | Integral on a Linear Network | |
| demopat | Artificial Data Point Pattern | |
| as.lpp | Convert Data to a Point Pattern on a Linear Network | |
| copyExampleFiles | Copy Data Files for Example | |
| is.marked.ppm | Test Whether A Point Process Model is Marked | |
| default.rmhcontrol | Set Default Control Parameters for Metropolis-Hastings Algorithm. | |
| is.empty | Test Whether An Object Is Empty | |
| dppPowerExp | Power Exponential Spectral Determinantal Point Process Model | |
| discs | Union of Discs | |
| fryplot | Fry Plot of Point Pattern | |
| dppeigen | Internal function calculating eig and index | |
| edge.Ripley | Ripley's Isotropic Edge Correction | |
| coords | Extract or Change Coordinates of a Spatial or Spatiotemporal Point Pattern | |
| is.multitype | Test whether Object is Multitype | |
| ewcdf | Weighted Empirical Cumulative Distribution Function | |
| diameter.box3 | Geometrical Calculations for Three-Dimensional Box | |
| intersect.owin | Intersection, Union or Set Subtraction of Windows | |
| hyperframe | Hyper Data Frame | |
| formula.ppm | Model Formulae for Gibbs Point Process Models | |
| dclf.test | Diggle-Cressie-Loosmore-Ford and Maximum Absolute Deviation Tests | |
| alltypes | Calculate Summary Statistic for All Types in a Multitype Point Pattern | |
| as.box3 | Convert Data to Three-Dimensional Box | |
| ants | Harkness-Isham ants' nests data | |
| as.matrix.owin | Convert Pixel Image to Matrix | |
| clickjoin | Interactively join vertices on a plot | |
| angles.psp | Orientation Angles of Line Segments | |
| deletebranch | Delete or Extract a Branch of a Tree | |
| domain | Extract the Domain of any Spatial Object | |
| dg.progress | Progress Plot of Dao-Genton Test of Spatial Pattern | |
| as.hyperframe | Convert Data to Hyperframe | |
| bramblecanes | Hutchings' Bramble Canes data | |
| funxy | Spatial Function Class | |
| hyytiala | Scots pines and other trees at Hyytiala | |
| infline | Infinite Straight Lines | |
| gauss.hermite | Gauss-Hermite Quadrature Approximation to Expectation for Normal Distribution | |
| increment.fv | Increments of a Function | |
| box3 | Three-Dimensional Box | |
| ippm | Fit Point Process Model Involving Irregular Trend Parameters | |
| idw | Inverse-distance weighted smoothing of observations at irregular points | |
| crossdist | Pairwise distances | |
| dmixpois | Mixed Poisson Distribution | |
| deltametric | Delta Metric | |
| hamster | Aherne's hamster tumour data | |
| is.marked | Test Whether Marks Are Present | |
| bronzefilter | Bronze gradient filter data | |
| fitted.ppm | Fitted Conditional Intensity for Point Process Model | |
| bdist.pixels | Distance to Boundary of Window | |
| erosion | Morphological Erosion | |
| harmonise | Make Objects Compatible | |
| clarkevans.test | Clark and Evans Test | |
| boundingbox | Bounding Box of a Window, Image, or Point Pattern | |
| compatible.im | Test Whether Pixel Images Are Compatible | |
| diagnose.ppm | Diagnostic Plots for Fitted Point Process Model | |
| coef.slrm | Coefficients of Fitted Spatial Logistic Regression Model | |
| clip.infline | Intersect Infinite Straight Lines with a Window | |
| boxx | Multi-Dimensional Box | |
| cauchy.estpcf | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| fvnames | Abbreviations for Groups of Columns in Function Value Table | |
| disc | Circular Window | |
| bw.smoothppp | Cross Validated Bandwidth Selection for Spatial Smoothing | |
| envelope | Simulation Envelopes of Summary Function | |
| dg.sigtrace | Significance Trace of Dao-Genton Test | |
| invoke.symbolmap | Plot Data Using Graphics Symbol Map | |
| concatxy | Concatenate x,y Coordinate Vectors | |
| dppspecdenrange | Range of Spectral Density of a Determinantal Point Process Model | |
| fitted.slrm | Fitted Probabilities for Spatial Logistic Regression | |
| gorillas | Gorilla Nesting Sites | |
| dppspecden | Extract Spectral Density from Determinantal Point Process Model Object | |
| dim.detpointprocfamily | Dimension of Determinantal Point Process Model | |
| edges | Extract Boundary Edges of a Window. | |
| compatible | Test Whether Objects Are Compatible | |
| coef.mppm | Coefficients of Point Process Model Fitted to Multiple Point Patterns | |
| influence.ppm | Influence Measure for Spatial Point Process Model | |
| complement.owin | Take Complement of a Window | |
| crossdist.ppp | Pairwise distances between two different point patterns | |
| intensity.quadratcount | Intensity Estimates Using Quadrat Counts | |
| demohyper | Demonstration Example of Hyperframe of Spatial Data | |
| dilation | Morphological Dilation | |
| dirichletAreas | Compute Areas of Tiles in Dirichlet Tessellation | |
| emend | Force Model to be Valid | |
| diameter.linnet | Circumradius and Diameter of a Linear Network | |
| compatible.fv | Test Whether Function Objects Are Compatible | |
| discretise | Safely Convert Point Pattern Window to Binary Mask | |
| connected | Connected components | |
| dppkernel | Extract Kernel from Determinantal Point Process Model Object | |
| dkernel | Kernel distributions and random generation | |
| convolve.im | Convolution of Pixel Images | |
| exactMPLEstrauss | Exact Maximum Pseudolikelihood Estimate for Stationary Strauss Process | |
| default.expand | Default Expansion Rule for Simulation of Model | |
| is.owin | Test Whether An Object Is A Window | |
| iplot | Point and Click Interface for Displaying Spatial Data | |
| dppapproxpcf | Approximate Pair Correlation Function of Determinantal Point Process Model | |
| crossdist.psp | Pairwise distances between two different line segment patterns | |
| distfun.lpp | Distance Map on Linear Network | |
| kernel.factor | Scale factor for density kernel | |
| distcdf | Distribution Function of Interpoint Distance | |
| edge.Trans | Translation Edge Correction | |
| envelope.lpp | Envelope for Point Patterns on Linear Network | |
| fv.object | Function Value Table | |
| discpartarea | Area of Part of Disc | |
| diameter.owin | Diameter of a Window | |
| formula.fv | Extract or Change the Plot Formula for a Function Value Table | |
| distfun | Distance Map as a Function | |
| hist.im | Histogram of Pixel Values in an Image | |
| distmap | Distance Map | |
| harmonic | Basis for Harmonic Functions | |
| intensity.ppm | Intensity of Fitted Point Process Model | |
| dppBessel | Bessel Type Determinantal Point Process Model | |
| inforder.family | Infinite Order Interaction Family | |
| is.marked.ppp | Test Whether A Point Pattern is Marked | |
| edit.ppp | Invoke Text Editor on Spatial Data | |
| hierpair.family | Hierarchical Pairwise Interaction Process Family | |
| istat | Point and Click Interface for Exploratory Analysis of Point Pattern | |
| fv | Create a Function Value Table | |
| is.multitype.ppp | Test Whether A Point Pattern is Multitype | |
| eem | Exponential Energy Marks | |
| effectfun | Compute Fitted Effect of a Spatial Covariate in a Point Process Model | |
| fitted.mppm | Fitted Conditional Intensity for Multiple Point Process Model | |
| ganglia | Beta Ganglion Cells in Cat Retina, Old Version | |
| fourierbasis | Fourier Basis Functions | |
| humberside | Humberside Data on Childhood Leukaemia and Lymphoma | |
| identify.ppp | Identify Points in a Point Pattern | |
| im.apply | Apply Function Pixelwise to List of Images | |
| dppCauchy | Generalized Cauchy Determinantal Point Process Model | |
| dppapproxkernel | Approximate Determinantal Point Process Kernel | |
| improve.kppm | Improve Intensity Estimate of Fitted Cluster Point Process Model | |
| inside.owin | Test Whether Points Are Inside A Window | |
| intersect.tess | Intersection of Two Tessellations | |
| is.hybrid | Test Whether Object is a Hybrid | |
| eval.linim | Evaluate Expression Involving Pixel Images on Linear Network | |
| is.multitype.ppm | Test Whether A Point Process Model is Multitype | |
| foo | Foo is Not a Real Name | |
| imcov | Spatial Covariance of a Pixel Image | |
| hybrid.family | Hybrid Interaction Family | |
| as.data.frame.owin | Convert Window to Data Frame | |
| as.linnet.linim | Extract Linear Network from Data on a Linear Network | |
| as.solist | Convert List of Two-Dimensional Spatial Objects | |
| as.matrix.im | Convert Pixel Image to Matrix or Array | |
| bounding.box.xy | Convex Hull of Points | |
| centroid.owin | Centroid of a window | |
| convexhull.xy | Convex Hull of Points | |
| crossdist.lpp | Pairwise distances between two point patterns on a linear network | |
| distmap.owin | Distance Map of Window | |
| eroded.areas | Areas of Morphological Erosions | |
| flu | Influenza Virus Proteins | |
| fitted.lppm | Fitted Intensity for Point Process on Linear Network | |
| im | Create a Pixel Image Object | |
| intensity.lpp | Empirical Intensity of Point Pattern on Linear Network | |
| is.dppm | Recognise Fitted Determinantal Point Process Models | |
| is.im | Test Whether An Object Is A Pixel Image | |
| anova.slrm | Analysis of Deviance for Spatial Logistic Regression Models | |
| as.function.im | Convert Pixel Image to Function of Coordinates | |
| austates | Australian States and Mainland Territories | |
| beginner | Print Introduction For Beginners | |
| compareFit | Residual Diagnostics for Multiple Fitted Models | |
| detpointprocfamilyfun | Construct a New Determinantal Point Process Model Family Function | |
| envelope.envelope | Recompute Envelopes | |
| gordon | People in Gordon Square | |
| eval.im | Evaluate Expression Involving Pixel Images | |
| hextess | Hexagonal Grid or Tessellation | |
| intensity.dppm | Intensity of Determinantal Point Process Model | |
| methods.units | Methods for Units | |
| area.owin | Area of a Window | |
| areaGain | Difference of Disc Areas | |
| betacells | Beta Ganglion Cells in Cat Retina | |
| bw.scott | Scott's Rule for Bandwidth Selection for Kernel Density | |
| clusterfit | Fit Cluster or Cox Point Process Model via Minimum Contrast | |
| commonGrid | Determine A Common Spatial Domain And Pixel Resolution | |
| crossdist.pp3 | Pairwise distances between two different three-dimensional point patterns | |
| density.psp | Kernel Smoothing of Line Segment Pattern | |
| dilated.areas | Areas of Morphological Dilations | |
| dppGauss | Gaussian Determinantal Point Process Model | |
| dummy.ppm | Extract Dummy Points Used to Fit a Point Process Model | |
| integral.im | Integral of a Pixel Image | |
| finpines | Pine saplings in Finland. | |
| levelset | Level Set of a Pixel Image | |
| intensity.ppx | Intensity of a Multidimensional Space-Time Point Pattern | |
| anova.ppm | ANOVA for Fitted Point Process Models | |
| areaLoss | Difference of Disc Areas | |
| as.data.frame.hyperframe | Coerce Hyperframe to Data Frame | |
| bei | Tropical rain forest trees | |
| begins | Check Start of Character String | |
| branchlabelfun | Tree Branch Membership Labelling Function | |
| bw.ppl | Likelihood Cross Validation Bandwidth Selection for Kernel Density | |
| cauchy.estK | Fit the Neyman-Scott cluster process with Cauchy kernel | |
| coef.ppm | Coefficients of Fitted Point Process Model | |
| collapse.fv | Collapse Several Function Tables into One | |
| crossing.psp | Crossing Points of Two Line Segment Patterns | |
| dclf.progress | Progress Plot of Test of Spatial Pattern | |
| dirichletVertices | Vertices and Edges of Dirichlet Tessellation | |
| fardist | Farthest Distance to Boundary of Window | |
| grow.rectangle | Add margins to rectangle | |
| heather | Diggle's Heather Data | |
| interp.colourmap | Interpolate smoothly between specified colours | |
| is.convex | Test Whether a Window is Convex | |
| is.lpp | Test Whether An Object Is A Point Pattern on a Linear Network | |
| midpoints.psp | Midpoints of Line Segment Pattern | |
| nncross.pp3 | Nearest Neighbours Between Two Patterns in 3D | |
| nnwhich.ppx | Nearest Neighbours in Any Dimensions | |
| pairs.im | Scatterplot Matrix for Pixel Images | |
| linearKcross | Multitype K Function (Cross-type) for Linear Point Pattern | |
| layered | Create List of Plotting Layers | |
| plot.listof | Plot a List of Things | |
| maxnndist | Compute Minimum or Maximum Nearest-Neighbour Distance | |
| lpp | Create Point Pattern on Linear Network | |
| methods.boxx | Methods for Multi-Dimensional Box | |
| plot.yardstick | Plot a Yardstick or Scale Bar | |
| methods.linim | Methods for Images on a Linear Network | |
| logLik.slrm | Loglikelihood of Spatial Logistic Regression | |
| methods.ppx | Methods for Multidimensional Space-Time Point Patterns | |
| logLik.mppm | Log Likelihood for Poisson Point Process Model | |
| methods.slrm | Methods for Spatial Logistic Regression Models | |
| nndist.psp | Nearest neighbour distances between line segments | |
| npoints | Number of Points in a Point Pattern | |
| pool.anylist | Pool Data from a List of Objects | |
| latest.news | Print News About Latest Version of Package | |
| rpoispp3 | Generate Poisson Point Pattern in Three Dimensions | |
| rCauchy | Simulate Neyman-Scott Point Process with Cauchy cluster kernel | |
| pcf3est | Pair Correlation Function of a Three-Dimensional Point Pattern | |
| linearK | Linear K Function | |
| linearpcfcross | Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| pcf.ppp | Pair Correlation Function of Point Pattern | |
| linearpcfinhom | Inhomogeneous Linear Pair Correlation Function | |
| pairdist | Pairwise distances | |
| parameters | Extract Model Parameters in Understandable Form | |
| pcfinhom | Inhomogeneous Pair Correlation Function | |
| localKinhom | Inhomogeneous Neighbourhood Density Function | |
| linearKinhom | Inhomogeneous Linear K Function | |
| plot.texturemap | Plot a Texture Map | |
| perspPoints | Draw Points or Lines on a Surface Viewed in Perspective | |
| localK | Neighbourhood density function | |
| pcf | Pair Correlation Function | |
| sharpen | Data Sharpening of Point Pattern | |
| matchingdist | Distance for a Point Pattern Matching | |
| ponderosa | Ponderosa Pine Tree Point Pattern | |
| persp.im | Perspective Plot of Pixel Image | |
| plot.lpp | Plot Point Pattern on Linear Network | |
| shift | Apply Vector Translation | |
| plot.colourmap | Plot a Colour Map | |
| methods.pp3 | Methods for three-dimensional point patterns | |
| pppmatching | Create a Point Matching | |
| murchison | Murchison gold deposits | |
| linearpcf | Linear Pair Correlation Function | |
| letterR | Window in Shape of Letter R | |
| lohboot | Bootstrap Confidence Bands for Summary Function | |
| logLik.ppm | Log Likelihood and AIC for Point Process Model | |
| lineardisc | Compute Disc of Given Radius in Linear Network | |
| pool | Pool Data | |
| predict.ppm | Prediction from a Fitted Point Process Model | |
| model.matrix.ppm | Extract Design Matrix from Point Process Model | |
| mucosa | Cells in Gastric Mucosa | |
| nndist.ppx | Nearest Neighbour Distances in Any Dimensions | |
| nearest.raster.point | Find Pixel Nearest to a Given Point | |
| project2segment | Move Point To Nearest Line | |
| progressreport | Print Progress Reports | |
| model.depends | Identify Covariates Involved in each Model Term | |
| spatstat-package | The Spatstat Package | |
| nnwhich | Nearest neighbour | |
| nbfires | Point Patterns of New Brunswick Forest Fires | |
| studpermu.test | Studentised Permutation Test | |
| superimpose.lpp | Superimpose Several Point Patterns on Linear Network | |
| kaplan.meier | Kaplan-Meier Estimator using Histogram Data | |
| plot.ppp | plot a Spatial Point Pattern | |
| methods.linfun | Methods for Functions on Linear Network | |
| pairdist.default | Pairwise distances | |
| rMaternI | Simulate Matern Model I | |
| pcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Type-i-To-Any-Type) | |
| owin.object | Class owin | |
| ppx | Multidimensional Space-Time Point Pattern | |
| reflect | Reflect In Origin | |
| linnet | Create a Linear Network | |
| pool.fasp | Pool Data from Several Function Arrays | |
| rVarGamma | Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel | |
| pixelcentres | Extract Pixel Centres as Point Pattern | |
| rmpoint | Generate N Random Multitype Points | |
| rMatClust | Simulate Matern Cluster Process | |
| quasirandom | Quasirandom Patterns | |
| relrisk | Estimate of Spatially-Varying Relative Risk | |
| localpcf | Local pair correlation function | |
| varblock | Estimate Variance of Summary Statistic by Subdivision | |
| rescale.ppp | Convert Point Pattern to Another Unit of Length | |
| linearmarkequal | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| quadratcount | Quadrat counting for a point pattern | |
| plot.layered | Layered Plot | |
| is.stationary | Recognise Stationary and Poisson Point Process Models | |
| runiflpp | Uniform Random Points on a Linear Network | |
| scalardilate | Apply Scalar Dilation | |
| linim | Create Pixel Image on Linear Network | |
| longleaf | Longleaf Pines Point Pattern | |
| lut | Lookup Tables | |
| logLik.kppm | Log Likelihood and AIC for Fitted Cox or Cluster Point Process Model | |
| rSSI | Simulate Simple Sequential Inhibition | |
| ppm | Fit Point Process Model to Data | |
| rshift.ppp | Randomly Shift a Point Pattern | |
| model.images | Compute Images of Constructed Covariates | |
| matclust.estK | Fit the Matern Cluster Point Process by Minimum Contrast | |
| pixellate.owin | Convert Window to Pixel Image | |
| methods.box3 | Methods for Three-Dimensional Box | |
| pixellate.psp | Convert Line Segment Pattern to Pixel Image | |
| is.subset.owin | Determine Whether One Window is Contained In Another | |
| psstA | Pseudoscore Diagnostic For Fitted Model against Area-Interaction Alternative | |
| relrisk.ppp | Nonparametric Estimate of Spatially-Varying Relative Risk | |
| rlinegrid | Generate grid of parallel lines with random displacement | |
| ppp | Create a Point Pattern | |
| markcorr | Mark Correlation Function | |
| methods.objsurf | Methods for Objective Function Surfaces | |
| quad.ppm | Extract Quadrature Scheme Used to Fit a Point Process Model | |
| qqplot.ppm | Q-Q Plot of Residuals from Fitted Point Process Model | |
| methods.lpp | Methods for Point Patterns on a Linear Network | |
| pairorient | Point Pair Orientation Distribution | |
| rpoisline | Generate Poisson Random Line Process | |
| plot.dppm | Plot a fitted determinantal point process | |
| plot.splitppp | Plot a List of Point Patterns | |
| split.ppp | Divide Point Pattern into Sub-patterns | |
| raster.x | Cartesian Coordinates for a Pixel Raster | |
| methods.fii | Methods for Fitted Interactions | |
| rotmean | Rotational Average of a Pixel Image | |
| plot.tess | Plot a tessellation | |
| kppm | Fit Cluster or Cox Point Process Model | |
| is.rectangle | Determine Type of Window | |
| leverage.ppm | Leverage Measure for Spatial Point Process Model | |
| project2set | Find Nearest Point in a Region | |
| plot.textstring | Plot a Text String | |
| lgcp.estK | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| lansing | Lansing Woods Point Pattern | |
| rpoisppx | Generate Poisson Point Pattern in Any Dimensions | |
| linearKdot | Multitype K Function (Dot-type) for Linear Point Pattern | |
| nncross | Nearest Neighbours Between Two Patterns | |
| rlabel | Random Re-Labelling of Point Pattern | |
| pppmatching.object | Class of Point Matchings | |
| selfcut.psp | Cut Line Segments Where They Intersect | |
| pcfdot | Multitype pair correlation function (i-to-any) | |
| run.simplepanel | Run Point-and-Click Interface | |
| linearpcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-type) for Linear Point Pattern | |
| layerplotargs | Extract or Replace the Plot Arguments of a Layered Object | |
| runifpoint3 | Generate N Uniform Random Points in Three Dimensions | |
| linearpcfdot.inhom | Inhomogeneous Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| mincontrast | Method of Minimum Contrast | |
| scaletointerval | Rescale Data to Lie Between Specified Limits | |
| quantile.ewcdf | Quantiles of Weighted Empirical Cumulative Distribution Function | |
| summary.im | Summarizing a Pixel Image | |
| lppm | Fit Point Process Model to Point Pattern on Linear Network | |
| setcov | Set Covariance of a Window | |
| nnmark | Mark of Nearest Neighbour | |
| pcfmulti | Marked pair correlation function | |
| markstat | Summarise Marks in Every Neighbourhood in a Point Pattern | |
| pointsOnLines | Place Points Evenly Along Specified Lines | |
| plot.bermantest | Plot Result of Berman Test | |
| solist | List of Two-Dimensional Spatial Objects | |
| vcov.ppm | Variance-Covariance Matrix for a Fitted Point Process Model | |
| marks.psp | Marks of a Line Segment Pattern | |
| nnorient | Nearest Neighbour Orientation Distribution | |
| split.ppx | Divide Multidimensional Point Pattern into Sub-patterns | |
| urkiola | Urkiola Woods Point Pattern | |
| rmhmodel | Define Point Process Model for Metropolis-Hastings Simulation. | |
| methods.kppm | Methods for Cluster Point Process Models | |
| pairdist.ppp | Pairwise distances | |
| nndist.pp3 | Nearest neighbour distances in three dimensions | |
| npfun | Dummy Function Returns Number of Points | |
| methods.rhohat | Methods for Intensity Functions of Spatial Covariate | |
| msr | Signed or Vector-Valued Measure | |
| objsurf | Objective Function Surface | |
| zapsmall.im | Rounding of Pixel Values | |
| lurking | Lurking variable plot | |
| methods.dppm | Methods for Determinantal Point Process Models | |
| marktable | Tabulate Marks in Neighbourhood of Every Point in a Point Pattern | |
| quadrats | Divide Region into Quadrats | |
| pcf.fv | Pair Correlation Function obtained from K Function | |
| nnclean | Nearest Neighbour Clutter Removal | |
| plot.quadratcount | Plot Quadrat Counts | |
| runifpointOnLines | Generate N Uniform Random Points On Line Segments | |
| plot.kppm | Plot a fitted cluster point process | |
| owin | Create a Window | |
| subfits | Extract List of Individual Point Process Models | |
| print.ppp | Print Brief Details of a Point Pattern Dataset | |
| rotate.psp | Rotate a Line Segment Pattern | |
| pairdist.psp | Pairwise distances between line segments | |
| redwoodfull | California Redwoods Point Pattern (Entire Dataset) | |
| model.frame.ppm | Extract the Variables in a Point Process Model | |
| lengths.psp | Lengths of Line Segments | |
| split.hyperframe | Divide Hyperframe Into Subsets and Reassemble | |
| layout.boxes | Generate a Row or Column Arrangement of Rectangles. | |
| multiplicity.ppp | Count Multiplicity of Duplicate Points | |
| spruces | Spruces Point Pattern | |
| quad.object | Class of Quadrature Schemes | |
| will.expand | Test Expansion Rule | |
| suffstat | Sufficient Statistic of Point Process Model | |
| plot.pp3 | Plot a Three-Dimensional Point Pattern | |
| panel.contour | Panel Plots using Colour Image or Contour Lines | |
| rescale.im | Convert Pixel Image to Another Unit of Length | |
| mean.im | Maximum, Minimum, Mean, Median, Range or Sum of Pixel Values in an Image | |
| rmhstart | Determine Initial State for Metropolis-Hastings Simulation. | |
| print.owin | Print Brief Details of a Spatial Window | |
| quadrat.test.mppm | Chi-Squared Test for Multiple Point Process Model Based on Quadrat Counts | |
| mergeLevels | Merge Levels of a Factor | |
| methods.layered | Methods for Layered Objects | |
| nnwhich.lpp | Identify Nearest Neighbours on a Linear Network | |
| nndist | Nearest neighbour distances | |
| miplot | Morisita Index Plot | |
| model.matrix.slrm | Extract Design Matrix from Spatial Logistic Regression Model | |
| summary.splitppp | Summary of a Split Point Pattern | |
| pcfcross.inhom | Inhomogeneous Multitype Pair Correlation Function (Cross-Type) | |
| unitname | Name for Unit of Length | |
| pairdist.ppx | Pairwise Distances in Any Dimensions | |
| nnmap | K-th Nearest Point Map | |
| rescue.rectangle | Convert Window Back To Rectangle | |
| relrisk.ppm | Parametric Estimate of Spatially-Varying Relative Risk | |
| pairdist.lpp | Pairwise shortest-path distances between points on a linear network | |
| residualspaper | Data and Code From JRSS Discussion Paper on Residuals | |
| quadrat.test | Dispersion Test for Spatial Point Pattern Based on Quadrat Counts | |
| is.ppp | Test Whether An Object Is A Point Pattern | |
| valid.detpointprocfamily | Check Validity of a Determinantal Point Process Model | |
| lgcp.estpcf | Fit a Log-Gaussian Cox Point Process by Minimum Contrast | |
| is.ppm | Test Whether An Object Is A Fitted Point Process Model | |
| moribund | Outdated Functions | |
| marks | Marks of a Point Pattern | |
| rescale.psp | Convert Line Segment Pattern to Another Unit of Length | |
| rNeymanScott | Simulate Neyman-Scott Process | |
| vesicles | Vesicles Data | |
| rshift.psp | Randomly Shift a Line Segment Pattern | |
| update.ppm | Update a Fitted Point Process Model | |
| quantile.density | Quantiles of a Density Estimate | |
| plot.lppm | Plot a Fitted Point Process Model on a Linear Network | |
| rPoissonCluster | Simulate Poisson Cluster Process | |
| rStraussHard | Perfect Simulation of the Strauss-Hardcore Process | |
| nnwhich.pp3 | Nearest neighbours in three dimensions | |
| summary.listof | Summary of a List of Things | |
| nncross.lpp | Nearest Neighbours on a Linear Network | |
| paracou | Kimboto trees at Paracou, French Guiana | |
| psst | Pseudoscore Diagnostic For Fitted Model against General Alternative | |
| rmhmodel.list | Define Point Process Model for Metropolis-Hastings Simulation. | |
| print.quad | Print a Quadrature Scheme | |
| linearmarkconnect | Mark Connection Function for Multitype Point Pattern on Linear Network | |
| pairdist.pp3 | Pairwise distances in Three Dimensions | |
| psp.object | Class of Line Segment Patterns | |
| residuals.dppm | Residuals for Fitted Determinantal Point Process Model | |
| linfun | Function on a Linear Network | |
| osteo | Osteocyte Lacunae Data: Replicated Three-Dimensional Point Patterns | |
| pcf.fasp | Pair Correlation Function obtained from array of K functions | |
| linearpcfdot | Multitype Pair Correlation Function (Dot-type) for Linear Point Pattern | |
| parres | Partial Residuals for Point Process Model | |
| japanesepines | Japanese Pines Point Pattern | |
| opening | Morphological Opening | |
| rmh.default | Simulate Point Process Models using the Metropolis-Hastings Algorithm. | |
| quadrat.test.splitppp | Dispersion Test of CSR for Split Point Pattern Based on Quadrat Counts | |
| quadratresample | Resample a Point Pattern by Resampling Quadrats | |
| markcrosscorr | Mark Cross-Correlation Function | |
| plot.slrm | Plot a Fitted Spatial Logistic Regression | |
| stienen | Stienen Diagram | |
| rmhcontrol | Set Control Parameters for Metropolis-Hastings Algorithm. | |
| reduced.sample | Reduced Sample Estimator using Histogram Data | |
| simdat | Simulated Point Pattern | |
| pcfcross | Multitype pair correlation function (cross-type) | |
| textureplot | Plot Image or Tessellation Using Texture Fill | |
| marks.tess | Marks of a Tessellation | |
| with.msr | Evaluate Expression Involving Components of a Measure | |
| periodify | Make Periodic Copies of a Spatial Pattern | |
| nestsplit | Nested Split | |
| plot.imlist | Plot a List of Images | |
| spokes | Spokes pattern of dummy points | |
| nndist.lpp | Nearest neighbour distances on a linear network | |
| treeprune | Prune Tree to Given Level | |
| reload.or.compute | Compute Unless Previously Saved | |
| pool.fv | Pool Several Functions | |
| pixellate.ppp | Convert Point Pattern to Pixel Image | |
| pairwise.family | Pairwise Interaction Process Family | |
| tile.areas | Compute Areas of Tiles in a Tessellation | |
| nearestsegment | Find Line Segment Nearest to Each Point | |
| update.detpointprocfamily | Set Parameter Values in a Determinantal Point Process Model | |
| roc | Receiver Operating Characteristic | |
| rstrat | Simulate Stratified Random Point Pattern | |
| predict.slrm | Predicted or Fitted Values from Spatial Logistic Regression | |
| rnoise | Random Pixel Noise | |
| ord.family | Ord Interaction Process Family | |
| rotate | Rotate | |
| nnfun | Nearest Neighbour Index Map as a Function | |
| scan.test | Spatial Scan Test | |
| logLik.dppm | Log Likelihood and AIC for Fitted Determinantal Point Process Model | |
| scanpp | Read Point Pattern From Data File | |
| round.ppp | Apply Numerical Rounding to Spatial Coordinates | |
| ppp.object | Class of Point Patterns | |
| simulate.dppm | Simulation of Determinantal Point Process Model | |
| pairsat.family | Saturated Pairwise Interaction Point Process Family | |
| pixelquad | Quadrature Scheme Based on Pixel Grid | |
| plot.hyperframe | Plot Entries in a Hyperframe | |
| simba | Simulated data from a two-group experiment with replication within each group. | |
| relevel.im | Reorder Levels of a Factor-Valued Image or Pattern | |
| rotate.im | Rotate a Pixel Image | |
| tiles | Extract List of Tiles in a Tessellation | |
| rmhmodel.ppm | Interpret Fitted Model for Metropolis-Hastings Simulation. | |
| simulate.lppm | Simulate a Fitted Point Process Model on a Linear Network | |
| nsegments | Number of Line Segments in a Line Segment Pattern | |
| rknn | Theoretical Distribution of Nearest Neighbour Distance | |
| plot.symbolmap | Plot a Graphics Symbol Map | |
| rpoint | Generate N Random Points | |
| summary.psp | Summary of a Line Segment Pattern Dataset | |
| plot.influence.ppm | Plot Influence Measure | |
| rsyst | Simulate systematic random point pattern | |
| plot.owin | Plot a Spatial Window | |
| segregation.test | Test of Spatial Segregation of Types | |
| rjitter | Random Perturbation of a Point Pattern | |
| shift.im | Apply Vector Translation To Pixel Image | |
| mppm | Fit Point Process Model to Several Point Patterns | |
| plot.linnet | Plot a linear network | |
| plot.solist | Plot a List of Spatial Objects | |
| ppm.object | Class of Fitted Point Process Models | |
| triangulate.owin | Decompose Window into Triangles | |
| simulate.slrm | Simulate a Fitted Spatial Logistic Regression Model | |
| nncorr | Nearest-Neighbour Correlation Indices of Marked Point Pattern | |
| rmhexpand | Specify Simulation Window or Expansion Rule | |
| thomas.estK | Fit the Thomas Point Process by Minimum Contrast | |
| spiders | Spider Webs on Mortar Lines of a Brick Wall | |
| plot.im | Plot a Pixel Image | |
| plot.fv | Plot Function Values | |
| nztrees | New Zealand Trees Point Pattern | |
| pool.envelope | Pool Data from Several Envelopes | |
| shapley | Galaxies in the Shapley Supercluster | |
| simplify.owin | Approximate a Polygon by a Simpler Polygon | |
| padimage | Pad the Border of a Pixel Image | |
| print.im | Print Brief Details of an Image | |
| plot.msr | Plot a Signed or Vector-Valued Measure | |
| predict.dppm | Prediction from a Fitted Determinantal Point Process Model | |
| transmat | Convert Pixel Array Between Different Conventions | |
| plot.fasp | Plot a Function Array | |
| plot.mppm | plot a Fitted Multiple Point Process Model | |
| plot.scan.test | Plot Result of Scan Test | |
| plot.leverage.ppm | Plot Leverage Function | |
| subset.hyperframe | Subset of Hyperframe Satisfying A Condition | |
| vargamma.estK | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| summary.anylist | Summary of a List of Things | |
| rHardcore | Perfect Simulation of the Hardcore Process | |
| print.psp | Print Brief Details of a Line Segment Pattern Dataset | |
| simulate.kppm | Simulate a Fitted Cluster Point Process Model | |
| plot.plotppm | Plot a plotppm Object Created by plot.ppm | |
| summary.ppm | Summarizing a Fitted Point Process Model | |
| plot.quadrattest | Display the result of a quadrat counting test. | |
| rpoispp | Generate Poisson Point Pattern | |
| rhohat | Smoothing Estimate of Covariate Transformation | |
| selfcrossing.psp | Crossing Points in a Line Segment Pattern | |
| ppm.ppp | Fit Point Process Model to Point Pattern Data | |
| sessionLibs | Print Names and Version Numbers of Libraries Loaded | |
| pool.quadrattest | Pool Several Quadrat Tests | |
| texturemap | Texture Map | |
| treebranchlabels | Label Vertices of a Tree by Branch Membership | |
| shift.ppp | Apply Vector Translation To Point Pattern | |
| spatstat-deprecated | Deprecated spatstat functions | |
| solutionset | Evaluate Logical Expression Involving Pixel Images and Return Region Where Expression is True | |
| pyramidal | Pyramidal Neurons in Cingulate Cortex | |
| summary.ppp | Summary of a Point Pattern Dataset | |
| pppdist | Distance Between Two Point Patterns | |
| quadscheme | Generate a Quadrature Scheme from a Point Pattern | |
| yardstick | Text, Arrow or Scale Bar in a Diagram | |
| rDGS | Perfect Simulation of the Diggle-Gates-Stibbard Process | |
| spatstat.options | Internal Options in Spatstat Package | |
| unnormdensity | Weighted kernel smoother | |
| rcell | Simulate Baddeley-Silverman Cell Process | |
| rMosaicSet | Mosaic Random Set | |
| transect.im | Pixel Values Along a Transect | |
| swedishpines | Swedish Pines Point Pattern | |
| reach | Interaction Distance of a Point Process | |
| redwood | California Redwoods Point Pattern (Ripley's Subset) | |
| residuals.mppm | Residuals for Point Process Model Fitted to Multiple Point Patterns | |
| spatstat-internal | Internal spatstat functions | |
| summary.owin | Summary of a Spatial Window | |
| rmpoispp | Generate Multitype Poisson Point Pattern | |
| rounding | Detect Numerical Rounding | |
| rMaternII | Simulate Matern Model II | |
| ranef.mppm | Extract Random Effects from Point Process Model | |
| psp | Create a Line Segment Pattern | |
| vcov.kppm | Variance-Covariance Matrix for a Fitted Cluster Point Process Model | |
| rpoisppOnLines | Generate Poisson Point Pattern on Line Segments | |
| sidelengths.owin | Side Lengths of Enclosing Rectangle of a Window | |
| rQuasi | Generate Quasirandom Point Pattern in Given Window | |
| quantile.im | Sample Quantiles of Pixel Image | |
| print.ppm | Print a Fitted Point Process Model | |
| rshift | Random Shift | |
| thomas.estpcf | Fit the Thomas Point Process by Minimum Contrast | |
| quadscheme.logi | Generate a Logistic Regression Quadrature Scheme from a Point Pattern | |
| rThomas | Simulate Thomas Process | |
| spatialcdf | Spatial Cumulative Distribution Function | |
| sporophores | Sporophores Data | |
| rMosaicField | Mosaic Random Field | |
| shift.owin | Apply Vector Translation To Window | |
| square | Square Window | |
| rcellnumber | Generate Random Numbers of Points for Cell Process | |
| residuals.ppm | Residuals for Fitted Point Process Model | |
| valid | Check Whether Point Process Model is Valid | |
| with.fv | Evaluate an Expression in a Function Table | |
| waka | Trees in Waka national park | |
| methods.funxy | Methods for Spatial Functions | |
| perimeter | Perimeter Length of Window | |
| simulate.ppm | Simulate a Fitted Gibbs Point Process Model | |
| rescale.owin | Convert Window to Another Unit of Length | |
| plot.envelope | Plot a Simulation Envelope | |
| rGaussPoisson | Simulate Gauss-Poisson Process | |
| plot.onearrow | Plot an Arrow | |
| rthin | Random Thinning | |
| rDiggleGratton | Perfect Simulation of the Diggle-Gratton Process | |
| timed | Record the Computation Time | |
| tweak.colourmap | Change Colour Values in a Colour Map | |
| sumouter | Compute Quadratic Forms | |
| update.kppm | Update a Fitted Cluster Point Process Model | |
| split.im | Divide Image Into Sub-images | |
| rose | Rose Diagram | |
| plot.ppm | plot a Fitted Point Process Model | |
| rStrauss | Perfect Simulation of the Strauss Process | |
| summary.quad | Summarizing a Quadrature Scheme | |
| rpoislpp | Poisson Point Process on a Linear Network | |
| tilenames | Names of Tiles in a Tessellation | |
| rho2hat | Smoothed Relative Density of Pairs of Covariate Values | |
| unmark | Remove Marks | |
| range.fv | Range of Function Values | |
| rpoislinetess | Poisson Line Tessellation | |
| stratrand | Stratified random point pattern | |
| vertices | Vertices of a Window | |
| scanLRTS | Likelihood Ratio Test Statistic for Scan Test | |
| rescale | Convert dataset to another unit of length | |
| solapply | Apply a Function Over a List and Obtain a List of Objects | |
| union.quad | Union of Data and Dummy Points | |
| plot.psp | plot a Spatial Line Segment Pattern | |
| rgbim | Create Colour-Valued Pixel Image | |
| rLGCP | Simulate Log-Gaussian Cox Process | |
| plot.quad | Plot a Spatial Quadrature Scheme | |
| subset.ppp | Subset of Point Pattern Satisfying A Condition | |
| rmh.ppm | Simulate from a Fitted Point Process Model | |
| residuals.kppm | Residuals for Fitted Cox or Cluster Point Process Model | |
| valid.ppm | Check Whether Point Process Model is Valid | |
| rshift.splitppp | Randomly Shift a List of Point Patterns | |
| triplet.family | Triplet Interaction Family | |
| vargamma.estpcf | Fit the Neyman-Scott Cluster Point Process with Variance Gamma kernel | |
| rotate.ppp | Rotate a Point Pattern | |
| runifpoint | Generate N Uniform Random Points | |
| summary.solist | Summary of a List of Spatial Objects | |
| summary.kppm | Summarizing a Fitted Cox or Cluster Point Process Model | |
| stieltjes | Compute Integral of Function Against Cumulative Distribution | |
| unique.ppp | Extract Unique Points from a Spatial Point Pattern | |
| symbolmap | Graphics Symbol Map | |
| vcov.mppm | Calculate Variance-Covariance Matrix for Fitted Multiple Point Process Model | |
| update.rmhcontrol | Update Control Parameters of Metropolis-Hastings Algorithm | |
| volume | Volume of an Object | |
| km.rs | Kaplan-Meier and Reduced Sample Estimator using Histograms | |
| linearKdot.inhom | Inhomogeneous multitype K Function (Dot-type) for Linear Point Pattern | |
| markconnect | Mark Connection Function | |
| matclust.estpcf | Fit the Matern Cluster Point Process by Minimum Contrast Using Pair Correlation | |
| methods.linnet | Methods for Linear Networks | |
| methods.rho2hat | Methods for Intensity Functions of Two Spatial Covariates | |
| nndensity.ppp | Estimate Intensity of Point Pattern Using Nearest Neighbour Distances | |
| nnfun.lpp | Nearest Neighbour Map on Linear Network | |
| pixellate | Convert Spatial Object to Pixel Image | |
| plot.linim | Plot Pixel Image on Linear Network | |
| predict.kppm | Prediction from a Fitted Cluster Point Process Model | |
| quantess | Quantile Tessellation | |
| rat | Ratio object | |
| rdpp | Simulation of a Determinantal Point Process | |
| reach.dppm | Range of Interaction for a Determinantal Point Process Model | |
| rtemper | Simulated Annealing or Simulated Tempering for Gibbs Point Processes | |
| runifdisc | Generate N Uniform Random Points in a Disc | |
| runifpointx | Generate N Uniform Random Points in Any Dimensions | |
| simplepanel | Simple Point-and-Click Interface Panels | |
| superimpose | Superimpose Several Geometric Patterns | |
| tess | Create a Tessellation | |
| trim.rectangle | Cut margins from rectangle | |
| update.symbolmap | Update a Graphics Symbol Map. | |
| vcov.slrm | Variance-Covariance Matrix for a Fitted Spatial Logistic Regression | |
| with.hyperframe | Evaluate an Expression in Each Row of a Hyperframe | |
| linearKcross.inhom | Inhomogeneous multitype K Function (Cross-type) for Linear Point Pattern | |
| markvario | Mark Variogram | |
| methods.lppm | Methods for Fitted Point Process Models on a Linear Network | |
| overlap.owin | Compute Area of Overlap | |
| plot.anylist | Plot a List of Things | |
| plot.cdftest | Plot a Spatial Distribution Test | |
| pp3 | Three Dimensional Point Pattern | |
| pool.rat | Pool Data from Several Ratio Objects | |
| predict.mppm | Prediction for Fitted Multiple Point Process Model | |
| predict.lppm | Predict Point Process Model on Linear Network | |
| profilepl | Profile Maximum Pseudolikelihood or AIC | |
| psstG | Pseudoscore Diagnostic For Fitted Model against Saturation Alternative | |
| ripras | Estimate window from points alone | |
| rmh | Simulate point patterns using the Metropolis-Hastings algorithm. | |
| rmhmodel.default | Build Point Process Model for Metropolis-Hastings Simulation. | |
| rotate.owin | Rotate a Window | |
| simplenet | Simple Example of Linear Network | |
| shift.psp | Apply Vector Translation To Line Segment Pattern | |
| slrm | Spatial Logistic Regression | |
| spatdim | Spatial Dimension of a Dataset | |
| whist | Weighted Histogram | |
| waterstriders | Waterstriders data. Three independent replications of a point pattern formed by insects. | |
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Details
| Nickname | Mixed Effects |
| Date | 2015-10-07 |
| License | GPL (>= 2) |
| URL | http://www.spatstat.org |
| LazyData | true |
| NeedsCompilation | yes |
| ByteCompile | true |
| BugReports | https://github.com/spatstat/spatstat/issues |
| Packaged | 2015-10-07 06:18:02 UTC; adrian |
| Repository | CRAN |
| Date/Publication | 2015-10-08 13:43:38 |
| imports | abind , deldir (>= 0.0-21) , goftest , Matrix , mgcv , polyclip (>= 1.3-0) , tensor |
| depends | base (>= 3.2.2) , graphics , grDevices , methods , nlme , R (>= 3.2.2) , stats , utils |
| suggests | gsl , locfit , maptools , RandomFields (>= 3.0.0) , rpanel , sm , spatial , tkrplot |
| Contributors | Rolf Turner, Adrian Baddeley, Ege Rubak |
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