Analysing spatial point patterns in R - CSIRO

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174 Validation of fitted Gibbs models At the time of writing, spatstat does not yet display 2σ significance bands for the lurking variable plots when the fitted model is not Poisson. The interpretation of the lurking variable plots is a little more difficult without the significance bands. One tends to place a little more emphasis on the smoothed residual field. The Pearson residuals should be approximately standardised, so that values which are much greater than 2 (in absolute value) suggest a lack of fit. The four-panel plot above suggests that the model is a reasonable fit. 28.2.3 Q–Q plots As we noted in Section 16.2.6, the four-panel residual plot and the lurking variable plot are useful for detecting misspecification of the trend in a fitted model. They are not very useful for checking misspecification of the interaction in a fitted model. An extreme example is provided by the cells dataset. The residual plots for a uniform Poisson process fitted to the cells data suggest that this is a good model: > data(cells) > fitPois diagnose.ppm(fitPois) cumulative sum of raw residuals 8 6 4 2 0 −2 −4 −6 −8 cumulative sum of raw residuals −8 −6 −4 −2 0 2 4 6 8 0 0.2 0.4 0.6 0.8 1 y coordinate −15 −5 −10 −10 −5 −15 0 10 5 5 −15 −10 −25 −20 −5 0 15 10 0 −10 −5 0 0.2 0.4 0.6 0.8 1 x coordinate However, the K-function shows that the cells dataset is clearly not a Poisson pattern, but has strong inhibition: CopyrightCSIRO 2010
28.2 Residuals for Gibbs processes 175 > par(mfrow = c(1, 2)) > plot(cells) > plot(Kest(cells)) > par(mfrow = c(1, 1)) Kest(cells) cells K(r) 0.00 0.05 0.10 0.15 0.20 iso trans border theo 0.00 0.05 0.10 0.15 0.20 0.25 Interaction between points in a point process corresponds roughly to the distribution of the responses in loglinear regression. To validate the interaction terms in a point process model, we should plot the distribution of the residuals. The appropriate tool is a Q–Q plot. r > qqplot.ppm(fitPois, nsim = 39) data quantile −30 −20 −10 0 10 20 30 40 −30 −20 −10 0 10 20 30 40 Mean quantile of simulations This shows a Q–Q plot of the smoothed residuals for a uniform Poisson model fitted to the cells data, with pointwise 5% critical envelopes from simulations of the fitted model. This indicates that the uniform Poisson model is grossly inappropriate for the cells data. Returning to the model we fitted at the start of this chapter: > qqplot.ppm(fit, nsim = 39) CopyrightCSIRO 2010
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Analysing spatial point patterns in
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CONTENTS 3 Contents PART I. OVERVIE
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CONTENTS 5 PART I. OVERVIEW The fir
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130 1.1 Types of data 7 The mark co
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1.2 Typical scientific questions 9
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1.2 Typical scientific questions 11
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13 We’ll cover both classical and
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2.4 Marks and covariates 15 Data ar
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2.4 Marks and covariates 17 example
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3.3 Contributed libraries for R 19
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4.4 Licence 21 The response will be
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0.005 4.6 Exploratory data analysis
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4.7 Models 25 E K(r) 0 500 1000 150
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4.8 Multitype point patterns 27 4.8
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4.8 Multitype point patterns 29 e.g
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4.9 Installed datasets 31 PART II.
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5.2 Classes in spatstat 33 Point pa
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5.3 Return values 35 density(swedis
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5.3 Return values 37 Many of the fu
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6.2 Creating a ppp object 39 To rea
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6.4 Categorical marks 41 longleaf T
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6.6 Checking data 43 > par(mfrow =
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45 7 Converting from GIS formats Th
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8.1 Making windows by hand 47 8.1.3
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8.2 Converting from GIS formats 49
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8.5 Creating a point pattern in any
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53 9 Manipulating point patterns Be
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9.2 Operations on ppp objects 55 [1
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9.2 Operations on ppp objects 57 ma
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9.3 Example 59 You may also notice
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9.5 List of operations on point pat
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63 10 Pixel images in spatstat An o
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10.2 Inspecting an image 65 > f w
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10.2 Inspecting an image 67 > plot(
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10.3 Manipulating images 69 0 200 6
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71 > W V 3) > U 3, 42, Z)) Other
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11.3 Operations involving a tessell
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12.2 Inhomogeneous intensity 79 12.
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12.2 Inhomogeneous intensity 81 den
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13.3 Relative distribution estimate
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1 1 2 4 4 1 2 13.4 Distance map 85
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13.4 Distance map 87 PART IV. POISS
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14.2 Quadrat counting tests for CSR
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14.4 Kolmogorov-Smirnov test of CSR
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14.5 Using covariate data 93 Becaus
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95 15 Maximum likelihood for Poisso
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15.3 Fitting Poisson processes in s
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15.4 Fitted models 99 > ppm(bei, ~s
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15.4 Fitted models 101 > predict(fi
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15.4 Fitted models 103 effectfun(fi
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15.5 Simulating the fitted model 10
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16.2 Validation using residuals 107
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113 PART V. INTERACTION Part V of t
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115 independent + + + + + + + + + +
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19.2 Empty space distances 117 Anot
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19.2 Empty space distances 119 cell
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19.2 Empty space distances 121 Fest
Page 123 and 124: 19.3 Nearest neighbour distances 12
Page 125 and 126: 19.4 Pairwise distances and the K f
Page 127 and 128: 19.4 Pairwise distances and the K f
Page 129 and 130: 19.6 Manipulating and plotting summ
Page 131 and 132: 19.7 Caveats 131 Kest(X) X K(r) 0.0
Page 133 and 134: 20.1 Envelopes and Monte Carlo test
Page 139 and 140: 139 21 Spatial bootstrap methods Sp
Page 141 and 142: 22.2 Cox processes 141 22.2 Cox pro
Page 143 and 144: 22.4 Sequential models 143 rSSI(0.0
Page 145 and 146: 23.1 Fitting a cluster process 145
Page 147 and 148: 23.4 Generic algorithm for minimum
Page 149 and 150: 149 In data sharpening [27] the poi
Page 151 and 152: 25.2 Inhomogeneous cluster models 1
Page 153 and 154: 25.4 Local scaling 153 > data(bei)
Page 155 and 156: 25.4 Local scaling 155 PART VI. GIB
Page 157 and 158: 26.3 Pairwise interaction models 15
Page 159 and 160: 26.4 Higher-order interactions 159
Page 161 and 162: 26.6 Simulating Gibbs models 161 St
Page 163 and 164: 27.2 Fitting Gibbs models in spatst
Page 165 and 166: 27.3 Interpoint interactions 165 27
Page 167 and 168: 27.4 Fitted point process models 16
Page 169 and 170: 27.6 Dealing with nuisance paramete
Page 171 and 172: 171 Stationary Strauss process Firs
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Page 177 and 178: 28.3 New methods 177 PART VII. MARK
Page 179 and 180: 29.3 Methodological issues 179 A ma
Page 181 and 182: 181 Chorley−Ribble Data For furth
Page 183 and 184: 30.2 Inspecting a marked point patt
Page 185 and 186: 30.3 Manipulating data 185 > data(l
Page 187 and 188: 187 31 Exploratory tools for multit
Page 189 and 190: 31.2 Simple summaries of neighbouri
Page 191 and 192: 31.3 Distance methods and summary f
Page 197 and 198: 31.4 Randomisation tests 197 31.4 R
Page 199 and 200: 31.4 Randomisation tests 199 > plot
Page 201 and 202: 32.1 Numeric marks: distribution an
Page 203 and 204: 32.3 Reverse conditional moments 20
Page 205 and 206: 33.2 Simulation 205 33.2 Simulation
Page 207 and 208: 33.3 Fitting Poisson models 207 33.
Page 209 and 210: 33.3 Fitting Poisson models 209 whe
Page 211 and 212: 34.1 Gibbs models 211 34.1.3 Pairwi
Page 213 and 214: 34.3 Fitting Gibbs models to multit
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Page 217 and 218: 217 There are also the usual method
Page 219 and 220: 219 lty col key label meaning iso 1
Page 221 and 222: 221 38 Replicated data and hyperfra
Page 223 and 224: 223 H H(r) 0.0 0.2 0.4 0.6 0.8 km h
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REFERENCES 225 References [1] F.P.
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REFERENCES 227 [32] P.J. Diggle. Bi
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Index analysis of deviance, 103 are
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INDEX 231 models, 25, 224 Monte Car
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Analysing spatial point patterns in R - CSIRO

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