R version 2.11.0 alpha (2010-03-28 r51461)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> pkgname <- "spatial"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('spatial')
> 
> assign(".oldSearch", search(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("Kaver")
> ### * Kaver
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Kaver
> ### Title: Average K-functions from Simulations
> ### Aliases: Kaver
> ### Keywords: spatial
> 
> ### ** Examples
> 
> towns <- ppinit("towns.dat")
> par(pty="s")
> plot(Kfn(towns, 40), type="b")
> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
> for(i in 1:10) lines(Kfn(Psim(69), 10))
> lims <- Kenvl(10,100,Psim(69))
> lines(lims$x,lims$lower, lty=2, col="green")
> lines(lims$x,lims$upper, lty=2, col="green")
> lines(Kaver(10,25,Strauss(69,0.5,3.5)),  col="red")
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("Kenvl")
> ### * Kenvl
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Kenvl
> ### Title: Compute Envelope and Average of Simulations of K-fns
> ### Aliases: Kenvl
> ### Keywords: spatial
> 
> ### ** Examples
> 
> towns <- ppinit("towns.dat")
> par(pty="s")
> plot(Kfn(towns, 40), type="b")
> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
> for(i in 1:10) lines(Kfn(Psim(69), 10))
> lims <- Kenvl(10,100,Psim(69))
> lines(lims$x,lims$lower, lty=2, col="green")
> lines(lims$x,lims$upper, lty=2, col="green")
> lines(Kaver(10,25,Strauss(69,0.5,3.5)), col="red")
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("Kfn")
> ### * Kfn
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Kfn
> ### Title: Compute K-fn of a Point Pattern
> ### Aliases: Kfn
> ### Keywords: spatial
> 
> ### ** Examples
> 
> towns <- ppinit("towns.dat")
> par(pty="s")
> plot(Kfn(towns, 10), type="s", xlab="distance", ylab="L(t)")
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("Psim")
> ### * Psim
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Psim
> ### Title: Simulate Binomial Spatial Point Process
> ### Aliases: Psim
> ### Keywords: spatial
> 
> ### ** Examples
> 
> towns <- ppinit("towns.dat")
> par(pty="s")
> plot(Kfn(towns, 10), type="s", xlab="distance", ylab="L(t)")
> for(i in 1:10) lines(Kfn(Psim(69), 10))
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("SSI")
> ### * SSI
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: SSI
> ### Title: Simulates Sequential Spatial Inhibition Point Process
> ### Aliases: SSI
> ### Keywords: spatial
> 
> ### ** Examples
> 
> towns <- ppinit("towns.dat")
> par(pty = "s")
> plot(Kfn(towns, 10), type = "b", xlab = "distance", ylab = "L(t)")
> lines(Kaver(10, 25, SSI(69, 1.2)))
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("Strauss")
> ### * Strauss
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Strauss
> ### Title: Simulates Strauss Spatial Point Process
> ### Aliases: Strauss
> ### Keywords: spatial
> 
> ### ** Examples
> 
> towns <- ppinit("towns.dat")
> par(pty="s")
> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
> lines(Kaver(10, 25, Strauss(69,0.5,3.5)))
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("anova.trls")
> ### * anova.trls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: anova.trls
> ### Title: Anova tables for fitted trend surface objects
> ### Aliases: anova.trls anovalist.trls
> ### Keywords: spatial
> 
> ### ** Examples
> 
> library(stats)
> data(topo, package="MASS")
> topo0 <- surf.ls(0, topo)
> topo1 <- surf.ls(1, topo)
> topo2 <- surf.ls(2, topo)
> topo3 <- surf.ls(3, topo)
> topo4 <- surf.ls(4, topo)
> anova(topo0, topo1, topo2, topo3, topo4)
Analysis of Variance Table

Model 1: surf.ls(np = 0, x = topo)
Model 2: surf.ls(np = 1, x = topo)
Model 3: surf.ls(np = 2, x = topo)
Model 4: surf.ls(np = 3, x = topo)
Model 5: surf.ls(np = 4, x = topo)
  Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)
1     51     196030                            
2     49      67186  2 128844 46.9843 4.040e-12
3     46      39958  3  27228 10.4482 2.325e-05
4     42      21577  4  18381  8.9447 2.558e-05
5     37      14886  5   6691  3.3265     0.014
> summary(topo4)
Analysis of Variance Table
 Model: surf.ls(np = 4, x = topo)
              Sum Sq Df    Mean Sq  F value     Pr(>F)
Regression 181143.99 14 12938.8567 32.16092 2.2204e-16
Deviation   14885.70 37   402.3162                    
Total      196029.69 51                               
Multiple R-Squared: 0.924,	Adjusted R-squared: 0.8953 
AIC: (df = 15) 324.1594
Fitted:
   Min     1Q Median     3Q    Max 
 702.1  785.0  836.3  880.5  939.1 
Residuals:
    Min      1Q  Median      3Q     Max 
-34.077 -12.568  -2.085  14.056  50.161 
> 
> 
> 
> cleanEx()
> nameEx("correlogram")
> ### * correlogram
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: correlogram
> ### Title: Compute Spatial Correlograms
> ### Aliases: correlogram
> ### Keywords: spatial
> 
> ### ** Examples
> 
> data(topo, package="MASS")
> topo.kr <- surf.ls(2, topo)
> correlogram(topo.kr, 25)
> d <- seq(0, 7, 0.1)
> lines(d, expcov(d, 0.7))
> 
> 
> 
> cleanEx()
> nameEx("expcov")
> ### * expcov
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: expcov
> ### Title: Spatial Covariance Functions
> ### Aliases: expcov gaucov sphercov
> ### Keywords: spatial
> 
> ### ** Examples
> 
> data(topo, package="MASS")
> topo.kr <- surf.ls(2, topo)
> correlogram(topo.kr, 25)
> d <- seq(0, 7, 0.1)
> lines(d, expcov(d, 0.7))
> 
> 
> 
> cleanEx()
> nameEx("ppinit")
> ### * ppinit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ppinit
> ### Title: Read a Point Process Object from a File
> ### Aliases: ppinit
> ### Keywords: spatial
> 
> ### ** Examples
> 
> towns <- ppinit("towns.dat")
> par(pty="s")
> plot(Kfn(towns, 10), type="b", xlab="distance", ylab="L(t)")
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("pplik")
> ### * pplik
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pplik
> ### Title: Pseudo-likelihood Estimation of a Strauss Spatial Point Process
> ### Aliases: pplik
> ### Keywords: spatial
> 
> ### ** Examples
> 
> pines <- ppinit("pines.dat")
> pplik(pines, 0.7)
[1] 0.1508756
> 
> 
> 
> cleanEx()
> nameEx("predict.trls")
> ### * predict.trls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.trls
> ### Title: Predict method for trend surface fits
> ### Aliases: predict.trls
> ### Keywords: spatial
> 
> ### ** Examples
> 
> data(topo, package="MASS")
> topo2 <- surf.ls(2, topo)
> topo4 <- surf.ls(4, topo)
> x <- c(1.78, 2.21)
> y <- c(6.15, 6.15)
> z2 <- predict(topo2, x, y)
> z4 <- predict(topo4, x, y)
> cat("2nd order predictions:", z2, "\n4th order predictions:", z4, "\n")
2nd order predictions: 756.0682 747.0624 
4th order predictions: 765.5547 742.3738 
> 
> 
> 
> cleanEx()
> nameEx("prmat")
> ### * prmat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: prmat
> ### Title: Evaluate Kriging Surface over a Grid
> ### Aliases: prmat
> ### Keywords: spatial
> 
> ### ** Examples
> 
> data(topo, package="MASS")
> topo.kr <- surf.gls(2, expcov, topo, d=0.7)
> prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
> contour(prsurf, levels=seq(700, 925, 25))
> 
> 
> 
> cleanEx()
> nameEx("semat")
> ### * semat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: semat
> ### Title: Evaluate Kriging Standard Error of Prediction over a Grid
> ### Aliases: semat
> ### Keywords: spatial
> 
> ### ** Examples
> 
> data(topo, package="MASS")
> topo.kr <- surf.gls(2, expcov, topo, d=0.7)
> prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
> contour(prsurf, levels=seq(700, 925, 25))
> sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
> contour(sesurf, levels=c(22,25))
> 
> 
> 
> cleanEx()
> nameEx("surf.gls")
> ### * surf.gls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: surf.gls
> ### Title: Fits a Trend Surface by Generalized Least-squares
> ### Aliases: surf.gls
> ### Keywords: spatial
> 
> ### ** Examples
> 
> library(MASS)  # for eqscplot
> data(topo, package="MASS")
> topo.kr <- surf.gls(2, expcov, topo, d=0.7)
> trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
> eqscplot(trsurf, type = "n")
> contour(trsurf, add = TRUE)
> 
> prsurf <- prmat(topo.kr, 0, 6.5, 0, 6.5, 50)
> contour(prsurf, levels=seq(700, 925, 25))
> sesurf <- semat(topo.kr, 0, 6.5, 0, 6.5, 30)
> eqscplot(sesurf, type = "n")
> contour(sesurf, levels = c(22, 25), add = TRUE)
> 
> 
> 
> cleanEx()

detaching ‘package:MASS’

> nameEx("surf.ls")
> ### * surf.ls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: surf.ls
> ### Title: Fits a Trend Surface by Least-squares
> ### Aliases: surf.ls
> ### Keywords: spatial
> 
> ### ** Examples
> 
> library(MASS)  # for eqscplot
> data(topo, package="MASS")
> topo.kr <- surf.ls(2, topo)
> trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
> eqscplot(trsurf, type = "n")
> contour(trsurf, add = TRUE)
> points(topo)
> 
> eqscplot(trsurf, type = "n")
> contour(trsurf, add = TRUE)
> plot(topo.kr, add = TRUE)
> title(xlab= "Circle radius proportional to Cook's influence statistic")
> 
> 
> 
> cleanEx()

detaching ‘package:MASS’

> nameEx("trls.influence")
> ### * trls.influence
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: trls.influence
> ### Title: Regression diagnostics for trend surfaces
> ### Aliases: trls.influence plot.trls
> ### Keywords: spatial
> 
> ### ** Examples
> 
> library(MASS)  # for eqscplot
> data(topo, package = "MASS")
> topo2 <- surf.ls(2, topo)
> infl.topo2 <- trls.influence(topo2)
> (cand <- as.data.frame(infl.topo2)[abs(infl.topo2$stresid) > 1.5, ])
           r        hii   stresid         Di
1   61.21889 0.35476783  2.585852 0.61275133
4  -45.58507 0.13493260 -1.662930 0.07188916
12  44.71663 0.21022336  1.707234 0.12930392
31  52.05575 0.07154233  1.833006 0.04314966
37  54.75944 0.06974770  1.926349 0.04637112
48  97.75499 0.08574061  3.468809 0.18807312
50 -63.25149 0.27530059 -2.520972 0.40237779
> cand.xy <- topo[as.integer(rownames(cand)), c("x", "y")]
> trsurf <- trmat(topo2, 0, 6.5, 0, 6.5, 50)
> eqscplot(trsurf, type = "n")
> contour(trsurf, add = TRUE, col = "grey")
> plot(topo2, add = TRUE, div = 3)
> points(cand.xy, pch = 16, col = "orange")
> text(cand.xy, labels = rownames(cand.xy), pos = 4, offset = 0.5)
> 
> 
> 
> cleanEx()

detaching ‘package:MASS’

> nameEx("trmat")
> ### * trmat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: trmat
> ### Title: Evaluate Trend Surface over a Grid
> ### Aliases: trmat
> ### Keywords: spatial
> 
> ### ** Examples
> 
> data(topo, package="MASS")
> topo.kr <- surf.ls(2, topo)
> trsurf <- trmat(topo.kr, 0, 6.5, 0, 6.5, 50)
> 
> 
> 
> cleanEx()
> nameEx("variogram")
> ### * variogram
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: variogram
> ### Title: Compute Spatial Variogram
> ### Aliases: variogram
> ### Keywords: spatial
> 
> ### ** Examples
> 
> data(topo, package="MASS")
> topo.kr <- surf.ls(2, topo)
> variogram(topo.kr, 25)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  0.601 0.012 0.615 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')