R Under development (unstable) (2024-02-01 r85851 ucrt) -- "Unsuffered Consequences"
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> #'
> #'   Header for all (concatenated) test files
> #'
> #'   Require spatstat.model
> #'   Obtain environment variable controlling tests.
> #'
> #'   $Revision: 1.5 $ $Date: 2020/04/30 05:31:37 $
> 
> require(spatstat.model)
Loading required package: spatstat.model
Loading required package: spatstat.data
Loading required package: spatstat.geom
spatstat.geom 3.2-8
Loading required package: spatstat.random
spatstat.random 3.2-2
Loading required package: spatstat.explore
Loading required package: nlme
spatstat.explore 3.2-6
Loading required package: rpart
spatstat.model 3.2-10
> FULLTEST <- (nchar(Sys.getenv("SPATSTAT_TEST", unset="")) > 0)
> ALWAYS   <- TRUE
> cat(paste("--------- Executing",
+           if(FULLTEST) "** ALL **" else "**RESTRICTED** subset of",
+           "test code -----------\n"))
--------- Executing **RESTRICTED** subset of test code -----------
> #
> # tests/rmhExpand.R
> #
> # test decisions about expansion of simulation window
> #
> #  $Revision: 1.9 $  $Date: 2022/10/23 01:17:33 $
> #
> 
> local({
+   if(FULLTEST) {
+     ## check expansion in rmhmodel.ppm
+     fit <- ppm(cells ~x)
+     mod <- rmhmodel(fit)
+     is.expandable(mod)
+     wsim <- as.rectangle(mod$trend)
+     ## work around changes in 'unitname'
+     wcel <- as.owin(cells)
+     unitname(wcel) <- unitname(cells)
+     ## test
+     if(!identical(wsim, wcel))
+       stop("Expansion occurred improperly in rmhmodel.ppm")
+   }
+ })
> 
> 
> 
> #
> # tests/rmhTrend.R
> #
> #  Problems with trend images (rmhmodel.ppm or rmhEngine)
> #
> 
> if(ALWAYS) {
+ local({
+   set.seed(42)
+ 
+   # Bug folder 37 of 8 feb 2011
+   # rmhmodel.ppm -> predict.ppm
+   # + rmhResolveTypes -> is.subset.owin
+ 
+   Z <- rescale(demopat, 7000)
+   X <- unmark(Z)
+   X1 <- split(Z)[[1]]
+   Int  <- density(X,dimyx=200)
+   Lint <- eval.im(log(npoints(X1)*Int/npoints(X)))
+   M    <- as.owin(Int)
+   MR   <- intersect.owin(M,scalardilate(M,0.5,origin="midpoint"))
+   X1 <- X1[MR]
+   Fut  <- ppm(X1~offset(Lint),covariates=list(Lint=Lint),
+               inter=BadGey(r=c(0.03,0.05),sat=3))
+   Y   <- rmh(Fut,control=list(expand=M,nrep=1e3), verbose=FALSE)
+ 
+ })
+ }
> #
> #      tests/rmhmodel.ppm.R
> #
> #    $Revision: 1.10 $  $Date: 2020/05/01 05:29:42 $
> #
> # Case-by-case tests of rmhmodel.ppm
> #
> 
> if(FULLTEST) {
+ local({
+ f <- ppm(cells)
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~x)
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, Strauss(0.1))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, StraussHard(r=0.1,hc=0.05))
+ m <- rmhmodel(f)
+ print(m)
+ 
+ f <- ppm(cells ~1, Hardcore(0.07))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, DiggleGratton(0.05,0.1))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, Softcore(0.5), correction="isotropic")
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, Geyer(0.07,2))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, BadGey(c(0.07,0.1,0.13),2))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, PairPiece(r = c(0.05, 0.1, 0.2)))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~1, AreaInter(r=0.06))
+ m <- rmhmodel(f)
+ print(m)
+ 
+ # multitype
+ 
+ r <- matrix(0.07, 2, 2)
+ f <- ppm(amacrine ~1, MultiStrauss(c("off","on"),r))
+ m <- rmhmodel(f)
+ print(m)
+ 
+ h <- matrix(min(nndist(amacrine))/2, 2, 2)
+ f <- ppm(amacrine ~1, MultiStraussHard(c("off","on"),r, h))
+ m <- rmhmodel(f)
+ 
+ diag(r) <- NA
+ diag(h) <- NA
+ f <- ppm(amacrine ~1, MultiStrauss(c("off","on"),r))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(amacrine ~1, MultiStraussHard(c("off","on"),r, h))
+ m <- rmhmodel(f)
+ 
+ # multitype data, interaction not dependent on type
+ 
+ f <- ppm(amacrine ~marks, Strauss(0.05))
+ m <- rmhmodel(f)
+ print(m)
+ 
+ # trends
+ 
+ f <- ppm(cells ~x, Strauss(0.1))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~y, StraussHard(r=0.1,hc=0.05))
+ m <- rmhmodel(f)
+ 
+ f <- ppm(cells ~x+y, Hardcore(0.07))
+ m <- rmhmodel(f)
+ print(m)
+ 
+ f <- ppm(cells ~polynom(x,y,2), Softcore(0.5), correction="isotropic")
+ m <- rmhmodel(f)
+ 
+ # covariates
+ 
+ Z <- as.im(function(x,y){ x^2+y^2 }, as.owin(cells))
+ f <- ppm(cells ~z, covariates=list(z=Z))
+ m <- rmhmodel(f)
+ m <- rmhmodel(f, control=list(p=1))
+ print(m)
+ 
+ Zim <- as.im(Z, as.owin(cells))
+ f <- ppm(cells ~z, covariates=list(z=Zim))
+ m <- rmhmodel(f)
+ 
+ Z <- as.im(function(x,y){ x^2+y }, as.owin(amacrine))
+ f <- ppm(amacrine ~z + marks, covariates=list(z=Z))
+ m <- rmhmodel(f)
+ print(m)
+ m <- rmhmodel(f, control=list(p=1))
+ m <- rmhmodel(f, control=list(p=1,fixall=TRUE))
+ print(m)
+ 
+ Zim <- as.im(Z, as.owin(amacrine))
+ f <- ppm(amacrine ~z + marks, covariates=list(z=Zim))
+ m <- rmhmodel(f)
+ print(m)
+ 
+ })
+ }
> #
> #    tests/rmhmodelHybrids.R
> #
> #  Test that rmhmodel.ppm and rmhmodel.default
> #  work on Hybrid interaction models
> #
> #   $Revision: 1.6 $  $Date: 2022/10/23 01:17:56 $
> #
> 
> if(ALWAYS) { # involves C code
+ local({
+     
+     ## ......... rmhmodel.ppm .......................
+     fit1 <- ppm(redwood ~1,
+                 Hybrid(A=Strauss(0.02), B=Geyer(0.1, 2), C=Geyer(0.15, 1)))
+     m1 <- rmhmodel(fit1)
+     m1
+     reach(m1)
+ 
+     ## Test of handling 'IsOffset' 
+     fit2 <- ppm(cells ~1, Hybrid(H=Hardcore(0.05), G=Geyer(0.15, 2)))
+     m2 <- rmhmodel(fit2)
+     ## also test C code for hybrid interaction with hard core
+     fakecells <- rmh(fit2, nrep=1e4)
+ 
+     ## Test of handling Poisson components
+     fit3 <- ppm(cells ~1, Hybrid(P=Poisson(), S=Strauss(0.05)))
+     X3 <- rmh(fit3, control=list(nrep=1e3,expand=1), verbose=FALSE)
+ 
+ 
+ })
+ }
Extracting model information...Evaluating trend...done.
Extracting model information...Evaluating trend...done.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
> 
> #
> #  tests/rmh.ppm.R
> #
> #  $Revision: 1.5 $ $Date: 2020/05/01 05:29:42 $
> #
> #  Examples removed from rmh.ppm.Rd
> #  stripped down to minimal tests of validity
> #
> 
> local({
+    op <- spatstat.options()
+    spatstat.options(rmh.nrep=10, npixel=10, ndummy.min=10)
+    spatstat.options(project.fast=TRUE)
+    Nrep <- 10
+ 
+    X <- swedishpines
+    if(FULLTEST) {
+      ## Poisson process
+      fit <- ppm(X ~1, Poisson())
+      Xsim <- rmh(fit)
+    }
+    if(ALWAYS) { # Gibbs model => C code
+      ## Strauss process   
+      fit <- ppm(X ~1, Strauss(r=7))
+      Xsim <- rmh(fit)
+ 
+      ## Strauss process simulated on a larger window
+      ## then clipped to original window
+      Xsim <- rmh(fit, control=list(nrep=Nrep, expand=1.1, periodic=TRUE))
+ 
+      ## Extension of model to another window (thanks to Tuomas Rajala)
+      Xsim <- rmh(fit, w=square(2))
+      Xsim <- simulate(fit, w=square(2))
+    
+      ## Strauss - hard core process
+      ##   fit <- ppm(X ~1, StraussHard(r=7,hc=2))
+      ##   Xsim <- rmh(fit, start=list(n.start=X$n))
+ 
+      ## Geyer saturation process
+      ##   fit <- ppm(X ~1, Geyer(r=7,sat=2))
+      ##   Xsim <- rmh(fit, start=list(n.start=X$n))
+ 
+      ## Area-interaction process
+      fit <- ppm(X ~1, AreaInter(r=7))
+      Xsim <- rmh(fit, start=list(n.start=X$n))
+   
+      ## Penttinen process
+      fit <- ppm(X ~1, Penttinen(r=7))
+      Xsim <- rmh(fit, start=list(n.start=X$n))
+   
+      ## soft core interaction process
+      ##     X <- quadscheme(X, nd=50)
+      ##     fit <- ppm(X ~1, Softcore(kappa=0.1), correction="isotropic")
+      ##     Xsim <- rmh(fit, start=list(n.start=X$n))
+ 
+      ## Diggle-Gratton pairwise interaction model
+      ##     fit <- ppm(cells ~1, DiggleGratton(0.05, 0.1))
+      ##     Xsim <- rmh(fit, start=list(n.start=cells$n))
+      ##     plot(Xsim, main="simulation from fitted Diggle-Gratton model")
+    
+ 
+      ## piecewise-constant pairwise interaction function
+      X <- rSSI(0.05, 100)
+      fit <- ppm(X ~1, PairPiece(seq(0.02, 0.1, by=0.01)))
+      Xsim <- rmh(fit)
+    }
+    
+    ## marked point pattern
+    Y <- amacrine
+ 
+    if(FULLTEST) {
+      #' marked Poisson models
+      fit <- ppm(Y)
+      Ysim <- rmh(fit)
+ 
+      fit <- ppm(Y~marks)
+      Ysim <- rmh(fit)
+ 
+      fit <- ppm(Y~x)
+      Ysim <- rmh(fit)
+      
+      fit <- ppm(Y~marks+x)
+      Ysim <- rmh(fit)
+    }
+ 
+    if(ALWAYS) {
+      #' multitype Strauss
+      typ <- levels(Y$marks)
+      MS <- MultiStrauss(types = typ,
+                         radii=matrix(0.07, ncol=2, nrow=2))
+ 
+      fit <- ppm(Y~marks*x, MS)
+      Ysim <- rmh(fit)
+ 
+      #' multitype Hardcore
+      h0 <- minnndist(unmark(Y)) * 0.95
+      MH <- MultiHard(types = typ,
+                      hradii=matrix(h0, ncol=2, nrow=2))
+      fit <- ppm(Y ~ marks+x, MH)
+      Ysim <- rmh(fit)
+      #' other code blocks
+      Ysim <- rmh(fit, control=list(periodic=TRUE, expand=1))
+      Ysim <- rmh(fit, control=list(periodic=FALSE, expand=1))
+      #' multihard core with invalid initial state
+      Ydouble <- superimpose(Y, rjitter(Y, h0/10))
+      Ysim <- rmh(fit, start=list(x.start=Ydouble))
+ 
+      #' Lennard-Jones
+      fut <- ppm(unmark(longleaf) ~ 1, LennardJones(), rbord=1)
+      Ysim <- rmh(fut)
+      Ysim <- rmh(fut, control=list(periodic=TRUE, expand=1))
+    }
+    
+    spatstat.options(op)
+  })
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Model is invalid - projecting it
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Evaluating trend integral...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Evaluating trend integral...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Evaluating trend integral...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Evaluating trend integral...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Evaluating trend integral...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Starting simulation.
Initial state...Ready to simulate. Generating proposal points...Running Metropolis-Hastings.
> 
> 
> reset.spatstat.options()
> #'
> #'   tests/rmhsnoopy.R
> #'
> #'   Test the rmh interactive debugger
> #' 
> #'   $Revision: 1.11 $  $Date: 2022/10/23 01:19:00 $
> 
> if(ALWAYS) { # may depend on platform
+ local({
+   R <- 0.1
+   ## fit a model and prepare to simulate
+   model <- ppm(amacrine ~ marks + x, Strauss(R))
+   siminfo <- rmh(model, preponly=TRUE)
+   Wsim <- siminfo$control$internal$w.sim
+   Wclip <- siminfo$control$internal$w.clip
+   if(is.null(Wclip)) Wclip <- Window(cells)
+ 
+   ## determine debugger interface panel geometry
+   Xinit <- runifpoint(ex=amacrine)[1:40]
+   P <- rmhsnoop(Wsim=Wsim, Wclip=Wclip, R=R,
+                 xcoords=Xinit$x,
+                 ycoords=Xinit$y,
+                 mlevels=levels(marks(Xinit)),
+                 mcodes=as.integer(marks(Xinit)) - 1L,
+                 irep=3L, itype=1L,
+                 proptype=1, proplocn=c(0.5, 0.5), propmark=0, propindx=0,
+                 numerator=42, denominator=24,
+                 panel.only=TRUE)
+   boxes <- P$boxes
+   clicknames <- names(P$clicks)
+   boxcentres <- do.call(concatxy, lapply(boxes, centroid.owin))
+ 
+   ## design a sequence of clicks
+   actionsequence <- c("Up", "Down", "Left", "Right",
+                       "At Proposal", "Zoom Out", "Zoom In", "Reset",
+                       "Accept", "Reject", "Print Info",
+                       "Next Iteration", "Next Shift", "Next Death",
+                       "Skip 10", "Skip 100", "Skip 1000", "Skip 10,000",
+                       "Skip 100,000", "Exit Debugger")
+   actionsequence <- match(actionsequence, clicknames)
+   actionsequence <- actionsequence[!is.na(actionsequence)]
+   xy <- lapply(boxcentres, "[", actionsequence)
+ 
+   ## queue the click sequence
+   spatstat.utils::queueSpatstatLocator(xy$x,xy$y)
+ 
+   ## go
+   rmh(model, snoop=TRUE)
+ })
+ }
Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Evaluating trend integral...Extracting model information...Evaluating trend...done.
Checking arguments..determining simulation windows...Evaluating trend integral...Starting simulation.
Initial state...
Creating debugger environment..Done.
Ready to simulate. Generating proposal points...Running Metropolis-Hastings.


------------------- 
Iteration 0
Simulation window:
window: rectangle = [0, 1.6012085] x [0, 1] units (one unit = 662 microns)
Clipping window:
window: rectangle = [0, 1.6012085] x [0, 1] units (one unit = 662 microns)
Current state:
Marked planar point pattern: 72 points
Multitype, with levels = off, on 
window: rectangle = [0, 1.6012085] x [0, 1] units (one unit = 662 microns)
Proposal type: Shift
Shift data point 18 from current location (0.290108, 0.0160037, "off") to new 
location (0.620599, 0.378428, "off")
Hastings ratio =  6831.92992943083 / 14545.6615344214 = 0.469688498750194
Fate of proposal: Rejected
Marked planar point pattern: 350 points
Multitype, with levels = off, on 
window: rectangle = [0, 1.6012085] x [0, 1] units (one unit = 662 microns)
Pattern was generated by Metropolis-Hastings simulation.
> 
> proc.time()
   user  system elapsed 
  13.54    1.59   15.14