R Under development (unstable) (2024-03-21 r86166 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 3.2-9
Loading required package: spatstat.random
spatstat.random 3.2-3
Loading required package: spatstat.explore
Loading required package: nlme
spatstat.explore 3.2-7
Loading required package: rpart
spatstat.model 3.2-11
> 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/slrm.R
> #
> # $Revision: 1.3 $ $Date: 2020/05/01 09:59:59 $
> #
> # Test slrm fitting and prediction when there are NA's
> #
> 
> if(ALWAYS) {
+ local({
+   X <- copper$SouthPoints
+   W <- owin(poly=list(x=c(0,35,35,1),y=c(1,1,150,150)))
+   Y <- X[W]
+   fit <- slrm(Y ~ x+y)
+   pred <- predict(fit)
+   extractAIC(fit)
+   fitx <- update(fit, . ~ x)
+   simulate(fitx, seed=42)
+   if(FULLTEST) {
+     unitname(fitx)
+     unitname(fitx) <- "km"
+ 
+     mur <- solapply(murchison,rescale, 1000, "km")
+     mur$dfault <- distfun(mur$faults)
+     fut <- slrm(gold ~ dfault, data=mur, splitby="greenstone")
+     A <- model.images(fut)
+   }
+ })
+ }
> 
> 
> #
> #   tests/step.R
> #
> #   $Revision: 1.5 $  $Date: 2020/05/01 09:59:59 $
> #
> # test for step() operation
> #
> if(FULLTEST) {
+ local({
+   Z <- as.im(function(x,y){ x^3 - y^2 }, nztrees$window)
+   fitP <- ppm(nztrees ~x+y+Z, covariates=list(Z=Z))
+   step(fitP)
+   fitS <- update(fitP, Strauss(7))
+   step(fitS)
+   fitM <- ppm(amacrine ~ marks*(x+y),
+               MultiStrauss(types=levels(marks(amacrine)), radii=matrix(0.04, 2, 2)))
+   step(fitM)
+ })
+ }
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   1.23    0.28    1.46