R Under development (unstable) (2023-10-22 r85388 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 3.2-7
Loading required package: spatstat.random
spatstat.random 3.2-1
Loading required package: spatstat.explore
Loading required package: nlme
spatstat.explore 3.2-5
Loading required package: rpart
spatstat.model 3.2-8
> 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/deltasuffstat.R
> #' 
> #'    Explicit tests of 'deltasuffstat'
> #' 
> #' $Revision: 1.4 $ $Date: 2021/01/22 08:08:48 $
> 
> if(!FULLTEST)
+   spatstat.options(npixel=32, ndummy.min=16)
> 
> if(ALWAYS) {  # depends on C code
+ local({
+   
+   disagree <- function(x, y, tol=1e-7) { !is.null(x) && !is.null(y) && max(abs(x-y)) > tol }
+ 
+   flydelta <- function(model, modelname="") {
+     ## Check execution of different algorithms for 'deltasuffstat' 
+     dSS <- deltasuffstat(model, sparseOK=TRUE)
+     dBS <- deltasuffstat(model, sparseOK=TRUE,  use.special=FALSE, force=TRUE)
+     dBF <- deltasuffstat(model, sparseOK=FALSE, use.special=FALSE, force=TRUE)
+     ## Compare results
+     if(disagree(dBS, dSS))
+       stop(paste(modelname, "model: Brute force algorithm disagrees with special algorithm"))
+     if(disagree(dBF, dBS))
+       stop(paste(modelname, "model: Sparse and full versions of brute force algorithm disagree"))
+     return(invisible(NULL))
+   }
+ 
+   modelS <- ppm(cells ~ x, Strauss(0.13), nd=10)
+   flydelta(modelS, "Strauss")
+ 
+   antsub <- ants[c(FALSE,TRUE,FALSE)]
+   rmat <- matrix(c(130, 90, 90, 60), 2, 2)
+   
+   modelM <- ppm(antsub ~ 1, MultiStrauss(rmat), nd=16)
+   flydelta(modelM, "MultiStrauss")
+                 
+   modelA <- ppm(antsub ~ 1, HierStrauss(rmat, archy=c(2,1)), nd=16)
+   flydelta(modelA, "HierStrauss")
+ })
+ 
+ }
> 
> reset.spatstat.options()
> #'
> #'  tests/density.R
> #'
> #'  Test behaviour of density() methods,
> #'                    relrisk(), Smooth()
> #'                    and inhomogeneous summary functions
> #'                    and idw, adaptive.density, intensity
> #'
> #'  $Revision: 1.62 $  $Date: 2022/05/22 11:14:51 $
> #'
> 
> if(!FULLTEST)
+   spatstat.options(npixel=32, ndummy.min=16)
> 
> 
> local({
+   ## likewise 'relrisk.ppm'
+   fit <- ppm(ants ~ x)
+   rants <- function(..., model=fit) {
+     a <- relrisk(model, sigma=100, se=TRUE, ...)
+     return(TRUE)
+   }
+   if(ALWAYS) {
+     rants()
+     rants(diggle=TRUE)
+     rants(edge=FALSE)
+     rants(at="points")
+     rants(casecontrol=FALSE)
+     rants(relative=TRUE)
+   }
+   if(FULLTEST) {
+     rants(diggle=TRUE, at="points")
+     rants(edge=FALSE, at="points")
+     rants(casecontrol=FALSE, relative=TRUE)
+     rants(casecontrol=FALSE,at="points")
+     rants(relative=TRUE,at="points")
+     rants(casecontrol=FALSE, relative=TRUE,at="points")
+     rants(relative=TRUE, control="Cataglyphis", case="Messor")
+     rants(relative=TRUE, control="Cataglyphis", case="Messor", at="points")
+   }
+   ## more than 2 types
+   fut <- ppm(sporophores ~ x)
+   if(ALWAYS) {
+     rants(model=fut)
+   }
+   if(FULLTEST) {
+     rants(model=fut, at="points")
+     rants(model=fut, relative=TRUE, at="points")
+   }
+   if(FULLTEST) {
+     ## cases of 'intensity' etc
+     a <- intensity(ppm(amacrine ~ 1))
+   }
+ })
> 
> reset.spatstat.options()
> 
> #'
> #'      tests/diagnostique.R
> #'
> #'  Diagnostic tools such as diagnose.ppm, qqplot.ppm
> #'
> #'  $Revision: 1.6 $  $Date: 2020/04/28 12:58:26 $
> #'
> 
> if(FULLTEST) {
+ local({
+   fit <- ppm(cells ~ x)
+   diagE <- diagnose.ppm(fit, type="eem")
+   diagI <- diagnose.ppm(fit, type="inverse")
+   diagP <- diagnose.ppm(fit, type="Pearson")
+   plot(diagE, which="all")
+   plot(diagI, which="smooth")
+   plot(diagP, which="x")
+   plot(diagP, which="marks", plot.neg="discrete")
+   plot(diagP, which="marks", plot.neg="contour")
+   plot(diagP, which="smooth", srange=c(-5,5))
+   plot(diagP, which="smooth", plot.smooth="contour")
+   plot(diagP, which="smooth", plot.smooth="image")
+ 
+   fitS <- ppm(cells ~ x, Strauss(0.08))
+   diagES <- diagnose.ppm(fitS, type="eem", clip=FALSE)
+   diagIS <- diagnose.ppm(fitS, type="inverse", clip=FALSE)
+   diagPS <- diagnose.ppm(fitS, type="Pearson", clip=FALSE)
+   plot(diagES, which="marks", plot.neg="imagecontour")
+   plot(diagPS, which="marks", plot.neg="discrete")
+   plot(diagPS, which="marks", plot.neg="contour")
+   plot(diagPS, which="smooth", plot.smooth="image")
+   plot(diagPS, which="smooth", plot.smooth="contour")
+   plot(diagPS, which="smooth", plot.smooth="persp")
+   
+   #' infinite reach, not border-corrected
+   fut <- ppm(cells ~ x, Softcore(0.5), correction="isotropic")
+   diagnose.ppm(fut)
+ 
+   #' 
+   diagPX <- diagnose.ppm(fit, type="Pearson", cumulative=FALSE)
+   plot(diagPX, which="y")
+ 
+   #' simulation based
+   e <- envelope(cells, nsim=4, savepatterns=TRUE, savefuns=TRUE)
+   Plist <- rpoispp(40, nsim=5)
+ 
+   qf <- qqplot.ppm(fit, nsim=4, expr=e, plot.it=FALSE)
+   print(qf)
+   qp <- qqplot.ppm(fit, nsim=5, expr=Plist, fast=FALSE)
+   print(qp)
+   qp <- qqplot.ppm(fit, nsim=5, expr=expression(rpoispp(40)), plot.it=FALSE)
+   print(qp)
+   qg <- qqplot.ppm(fit, nsim=5, style="classical", plot.it=FALSE)
+   print(qg)
+   
+   #' lurking.ppm
+   #' covariate is numeric vector
+   fitx <- ppm(cells ~ x)
+   yvals <- coords(as.ppp(quad.ppm(fitx)))[,"y"]
+   lurking(fitx, yvals)
+   #' covariate is stored but is not used in model
+   Z <- as.im(function(x,y){ x+y }, Window(cells))
+   fitxx <- ppm(cells ~ x, data=solist(Zed=Z), allcovar=TRUE)
+   lurking(fitxx, expression(Zed))
+   #' envelope is a ppplist; length < nsim; glmdata=NULL
+   fit <- ppm(cells ~ 1)
+   stuff <- lurking(fit, expression(x), envelope=Plist, plot.sd=FALSE)
+   #' plot.lurk
+   plot(stuff, shade=NULL)
+ })
+ }
> 
> #'
> #'    tests/deepeepee.R
> #'
> #'    Tests for determinantal point process models
> #' 
> #'    $Revision: 1.9 $ $Date: 2022/04/24 09:14:46 $
> 
> local({
+   if(ALWAYS) {
+     #' simulate.dppm
+     jpines <- residualspaper$Fig1
+     fit <- dppm(jpines ~ 1, dppGauss)
+     set.seed(10981)
+     simulate(fit, W=square(5))
+   }
+   if(FULLTEST) {
+     #' simulate.detpointprocfamily - code blocks
+     model <- dppGauss(lambda=100, alpha=.05, d=2)
+     simulate(model, seed=1999, correction="border")
+     u <- is.stationary(model)
+     #' other methods for dppm
+     kay <- Kmodel(fit)
+     gee <- pcfmodel(fit)
+     lam <- intensity(fit)
+     arr <- reach(fit)
+     pah <- parameters(fit)
+     #' a user bug report - matrix dimension error
+     set.seed(256)
+     dat <- simulate( dppGauss(lambda = 8.5, alpha = 0.1, d = 2), nsim = 1)
+   }
+   if(FULLTEST) {
+     ## cover print.summary.dppm
+     jpines <- japanesepines[c(TRUE,FALSE,FALSE,FALSE)]
+     print(summary(dppm(jpines ~ 1, dppGauss)))
+     print(summary(dppm(jpines ~ 1, dppGauss, method="c")))
+     print(summary(dppm(jpines ~ 1, dppGauss, method="p")))
+     print(summary(dppm(jpines ~ 1, dppGauss, method="a")))
+   }
+   #' dppeigen code blocks
+   if(ALWAYS) {
+     mod <- dppMatern(lambda=2, alpha=0.01, nu=1, d=2)
+     uT <- dppeigen(mod, trunc=1.1,  Wscale=c(1,1), stationary=TRUE)
+   }
+   if(FULLTEST) {
+     uF <- dppeigen(mod, trunc=1.1,  Wscale=c(1,1), stationary=FALSE)
+     vT <- dppeigen(mod, trunc=0.98, Wscale=c(1,1), stationary=TRUE)
+     vF <- dppeigen(mod, trunc=0.98, Wscale=c(1,1), stationary=FALSE)
+   }
+ })
> 
> proc.time()
   user  system elapsed 
   9.81    0.56   10.36