515 lines
12 KiB
Plaintext
515 lines
12 KiB
Plaintext
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R Under development (unstable) (2013-02-09 r61878) -- "Unsuffered Consequences"
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Copyright (C) 2013 The R Foundation for Statistical Computing
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ISBN 3-900051-07-0
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Platform: x86_64-unknown-linux-gnu (64-bit)
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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Natural language support but running in an English locale
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> pkgname <- "spatial"
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> source(file.path(R.home("share"), "R", "examples-header.R"))
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> options(warn = 1)
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> library('spatial')
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>
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> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
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> cleanEx()
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> nameEx("Kaver")
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> ### * Kaver
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: Kaver
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> ### Title: Average K-functions from Simulations
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> ### Aliases: Kaver
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> towns <- ppinit("towns.dat")
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> par(pty="s")
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> plot(Kfn(towns, 40), type="b")
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> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
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> for(i in 1:10) lines(Kfn(Psim(69), 10))
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> lims <- Kenvl(10,100,Psim(69))
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> lines(lims$x,lims$lower, lty=2, col="green")
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> lines(lims$x,lims$upper, lty=2, col="green")
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> lines(Kaver(10,25,Strauss(69,0.5,3.5)), col="red")
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>
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>
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>
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> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
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> cleanEx()
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> nameEx("Kenvl")
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> ### * Kenvl
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: Kenvl
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> ### Title: Compute Envelope and Average of Simulations of K-fns
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> ### Aliases: Kenvl
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> towns <- ppinit("towns.dat")
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> par(pty="s")
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> plot(Kfn(towns, 40), type="b")
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> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
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> for(i in 1:10) lines(Kfn(Psim(69), 10))
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> lims <- Kenvl(10,100,Psim(69))
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> lines(lims$x,lims$lower, lty=2, col="green")
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> lines(lims$x,lims$upper, lty=2, col="green")
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> lines(Kaver(10,25,Strauss(69,0.5,3.5)), col="red")
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>
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>
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>
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> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
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> cleanEx()
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> nameEx("Kfn")
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> ### * Kfn
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: Kfn
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> ### Title: Compute K-fn of a Point Pattern
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> ### Aliases: Kfn
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> towns <- ppinit("towns.dat")
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> par(pty="s")
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> plot(Kfn(towns, 10), type="s", xlab="distance", ylab="L(t)")
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>
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>
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>
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> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
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> cleanEx()
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> nameEx("Psim")
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> ### * Psim
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: Psim
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> ### Title: Simulate Binomial Spatial Point Process
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> ### Aliases: Psim
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> towns <- ppinit("towns.dat")
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> par(pty="s")
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> plot(Kfn(towns, 10), type="s", xlab="distance", ylab="L(t)")
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> for(i in 1:10) lines(Kfn(Psim(69), 10))
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>
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>
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>
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> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
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> cleanEx()
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> nameEx("SSI")
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> ### * SSI
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: SSI
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> ### Title: Simulates Sequential Spatial Inhibition Point Process
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> ### Aliases: SSI
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> towns <- ppinit("towns.dat")
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> par(pty = "s")
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> plot(Kfn(towns, 10), type = "b", xlab = "distance", ylab = "L(t)")
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> lines(Kaver(10, 25, SSI(69, 1.2)))
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>
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>
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>
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> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
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> cleanEx()
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> nameEx("Strauss")
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> ### * Strauss
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: Strauss
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> ### Title: Simulates Strauss Spatial Point Process
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> ### Aliases: Strauss
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> towns <- ppinit("towns.dat")
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> par(pty="s")
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> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
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> lines(Kaver(10, 25, Strauss(69,0.5,3.5)))
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>
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>
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>
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> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
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> cleanEx()
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> nameEx("anova.trls")
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> ### * anova.trls
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: anova.trls
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> ### Title: Anova tables for fitted trend surface objects
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> ### Aliases: anova.trls anovalist.trls
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> library(stats)
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> data(topo, package="MASS")
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> topo0 <- surf.ls(0, topo)
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> topo1 <- surf.ls(1, topo)
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> topo2 <- surf.ls(2, topo)
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> topo3 <- surf.ls(3, topo)
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> topo4 <- surf.ls(4, topo)
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> anova(topo0, topo1, topo2, topo3, topo4)
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Analysis of Variance Table
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Model 1: surf.ls(np = 0, x = topo)
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Model 2: surf.ls(np = 1, x = topo)
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Model 3: surf.ls(np = 2, x = topo)
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Model 4: surf.ls(np = 3, x = topo)
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Model 5: surf.ls(np = 4, x = topo)
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Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
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1 51 196030
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2 49 67186 2 128844 46.9843 4.040e-12
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3 46 39958 3 27228 10.4482 2.325e-05
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4 42 21577 4 18381 8.9447 2.558e-05
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5 37 14886 5 6691 3.3265 0.014
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> summary(topo4)
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Analysis of Variance Table
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Model: surf.ls(np = 4, x = topo)
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Sum Sq Df Mean Sq F value Pr(>F)
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Regression 181144.0 14 12938.8567 32.16092 2.2204e-16
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Deviation 14885.7 37 402.3162
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Total 196029.7 51
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Multiple R-Squared: 0.9241, Adjusted R-squared: 0.8953
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AIC: (df = 15) 324.1594
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Fitted:
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Min 1Q Median 3Q Max
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702.1 785.0 836.3 880.5 939.1
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Residuals:
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Min 1Q Median 3Q Max
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-34.077 -12.568 -2.085 14.056 50.161
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>
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>
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>
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> cleanEx()
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> nameEx("correlogram")
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> ### * correlogram
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: correlogram
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> ### Title: Compute Spatial Correlograms
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> ### Aliases: correlogram
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> data(topo, package="MASS")
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> topo.kr <- surf.ls(2, topo)
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> correlogram(topo.kr, 25)
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> d <- seq(0, 7, 0.1)
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> lines(d, expcov(d, 0.7))
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>
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>
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>
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> cleanEx()
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> nameEx("expcov")
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> ### * expcov
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: expcov
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> ### Title: Spatial Covariance Functions
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> ### Aliases: expcov gaucov sphercov
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> data(topo, package="MASS")
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> topo.kr <- surf.ls(2, topo)
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> correlogram(topo.kr, 25)
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> d <- seq(0, 7, 0.1)
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> lines(d, expcov(d, 0.7))
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>
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>
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>
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> cleanEx()
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> nameEx("ppinit")
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> ### * ppinit
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: ppinit
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> ### Title: Read a Point Process Object from a File
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> ### Aliases: ppinit
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> towns <- ppinit("towns.dat")
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> par(pty="s")
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> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
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>
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>
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>
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> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
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> cleanEx()
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> nameEx("pplik")
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> ### * pplik
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: pplik
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> ### Title: Pseudo-likelihood Estimation of a Strauss Spatial Point Process
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> ### Aliases: pplik
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> pines <- ppinit("pines.dat")
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> pplik(pines, 0.7)
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[1] 0.1508756
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>
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>
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>
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> cleanEx()
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> nameEx("predict.trls")
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> ### * predict.trls
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: predict.trls
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> ### Title: Predict method for trend surface fits
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> ### Aliases: predict.trls
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> data(topo, package="MASS")
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> topo2 <- surf.ls(2, topo)
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> topo4 <- surf.ls(4, topo)
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> x <- c(1.78, 2.21)
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> y <- c(6.15, 6.15)
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> z2 <- predict(topo2, x, y)
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> z4 <- predict(topo4, x, y)
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> cat("2nd order predictions:", z2, "\n4th order predictions:", z4, "\n")
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2nd order predictions: 756.0682 747.0624
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4th order predictions: 765.5547 742.3738
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>
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>
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>
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> cleanEx()
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> nameEx("prmat")
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> ### * prmat
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: prmat
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> ### Title: Evaluate Kriging Surface over a Grid
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> ### Aliases: prmat
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> data(topo, package="MASS")
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> topo.kr <- surf.gls(2, expcov, topo, d=0.7)
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> prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
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> contour(prsurf, levels=seq(700, 925, 25))
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>
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>
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>
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> cleanEx()
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> nameEx("semat")
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> ### * semat
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: semat
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> ### Title: Evaluate Kriging Standard Error of Prediction over a Grid
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> ### Aliases: semat
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> data(topo, package="MASS")
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> topo.kr <- surf.gls(2, expcov, topo, d=0.7)
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> prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
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> contour(prsurf, levels=seq(700, 925, 25))
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> sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
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> contour(sesurf, levels=c(22,25))
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>
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>
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>
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> cleanEx()
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> nameEx("surf.gls")
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> ### * surf.gls
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: surf.gls
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> ### Title: Fits a Trend Surface by Generalized Least-squares
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> ### Aliases: surf.gls
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> library(MASS) # for eqscplot
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> data(topo, package="MASS")
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> topo.kr <- surf.gls(2, expcov, topo, d=0.7)
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> trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
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> eqscplot(trsurf, type = "n")
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> contour(trsurf, add = TRUE)
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>
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> prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
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> contour(prsurf, levels=seq(700, 925, 25))
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> sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
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> eqscplot(sesurf, type = "n")
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> contour(sesurf, levels = c(22, 25), add = TRUE)
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>
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>
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>
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> cleanEx()
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detaching ‘package:MASS’
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> nameEx("surf.ls")
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> ### * surf.ls
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: surf.ls
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> ### Title: Fits a Trend Surface by Least-squares
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> ### Aliases: surf.ls
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> library(MASS) # for eqscplot
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> data(topo, package="MASS")
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> topo.kr <- surf.ls(2, topo)
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> trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
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> eqscplot(trsurf, type = "n")
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> contour(trsurf, add = TRUE)
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> points(topo)
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>
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> eqscplot(trsurf, type = "n")
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> contour(trsurf, add = TRUE)
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> plot(topo.kr, add = TRUE)
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> title(xlab= "Circle radius proportional to Cook's influence statistic")
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>
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>
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>
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> cleanEx()
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detaching ‘package:MASS’
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> nameEx("trls.influence")
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> ### * trls.influence
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: trls.influence
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> ### Title: Regression diagnostics for trend surfaces
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> ### Aliases: trls.influence plot.trls
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> library(MASS) # for eqscplot
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> data(topo, package = "MASS")
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> topo2 <- surf.ls(2, topo)
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> infl.topo2 <- trls.influence(topo2)
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> (cand <- as.data.frame(infl.topo2)[abs(infl.topo2$stresid) > 1.5, ])
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r hii stresid Di
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1 61.21889 0.35476783 2.585852 0.61275133
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4 -45.58507 0.13493260 -1.662930 0.07188916
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12 44.71663 0.21022336 1.707234 0.12930392
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31 52.05575 0.07154233 1.833006 0.04314966
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37 54.75944 0.06974770 1.926349 0.04637112
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48 97.75499 0.08574061 3.468809 0.18807312
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50 -63.25149 0.27530059 -2.520972 0.40237779
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> cand.xy <- topo[as.integer(rownames(cand)), c("x", "y")]
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> trsurf <- trmat(topo2, 0, 6.5, 0, 6.5, 50)
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> eqscplot(trsurf, type = "n")
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> contour(trsurf, add = TRUE, col = "grey")
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> plot(topo2, add = TRUE, div = 3)
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> points(cand.xy, pch = 16, col = "orange")
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> text(cand.xy, labels = rownames(cand.xy), pos = 4, offset = 0.5)
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>
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>
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>
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> cleanEx()
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detaching ‘package:MASS’
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> nameEx("trmat")
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> ### * trmat
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: trmat
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> ### Title: Evaluate Trend Surface over a Grid
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> ### Aliases: trmat
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> ### Keywords: spatial
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>
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> ### ** Examples
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>
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> data(topo, package="MASS")
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> topo.kr <- surf.ls(2, topo)
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> trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
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>
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>
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>
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> cleanEx()
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> nameEx("variogram")
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> ### * variogram
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>
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> flush(stderr()); flush(stdout())
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>
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> ### Name: variogram
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> ### Title: Compute Spatial Variogram
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> ### Aliases: variogram
|
||
> ### Keywords: spatial
|
||
>
|
||
> ### ** Examples
|
||
>
|
||
> data(topo, package="MASS")
|
||
> topo.kr <- surf.ls(2, topo)
|
||
> variogram(topo.kr, 25)
|
||
>
|
||
>
|
||
>
|
||
> ### * <FOOTER>
|
||
> ###
|
||
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
|
||
Time elapsed: 0.713 0.036 0.776 0 0
|
||
> grDevices::dev.off()
|
||
null device
|
||
1
|
||
> ###
|
||
> ### Local variables: ***
|
||
> ### mode: outline-minor ***
|
||
> ### outline-regexp: "\\(> \\)?### [*]+" ***
|
||
> ### End: ***
|
||
> quit('no')
|