R Under development (unstable) (2024-07-05 r86875 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.univar
spatstat.univar 3.0-0
Loading required package: spatstat.geom
spatstat.geom 3.3-1
Loading required package: spatstat.random
spatstat.random 3.3-0
Loading required package: spatstat.explore
Loading required package: nlme
spatstat.explore 3.3-0
Loading required package: rpart
spatstat.model 3.3-0
> 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/resid.R
> #'
> #'  Stuff related to residuals and residual diagnostics
> #'           including residual summary functions
> #'
> #'   $Revision: 1.7 $  $Date: 2022/05/22 08:43:31 $
> #'
> 
> local({
+   fit <- ppm(cells ~x, Strauss(r=0.15))
+   rr <- residuals(fit, quad=quadscheme(cells, nd=128))
+   diagnose.ppm(fit, cumulative=FALSE, type="pearson")
+ 
+   if(FULLTEST) {
+     diagnose.ppm(fit, cumulative=FALSE)
+ 
+     fitoff <- ppm(cells ~ sin(x) + offset(y))
+     plot(a <- parres(fitoff, "x"))
+     plot(b <- parres(fitoff, "y"))
+     print(a)
+     print(b)
+   
+     d <- diagnose.ppm(fit, which="marks")
+     plot(d, plot.neg="discrete")
+     plot(d, plot.neg="imagecontour")
+ 
+     d <- diagnose.ppm(fit, type="pearson", which="smooth")
+     plot(d, plot.smooth="image")
+     plot(d, plot.smooth="contour")
+     plot(d, plot.smooth="imagecontour")
+   
+     d <- diagnose.ppm(fit, type="pearson", which="x")
+     plot(d)
+     d <- diagnose.ppm(fit, type="pearson", which="y")
+     plot(d)
+   
+     diagnose.ppm(fit, type="pearson", which="x", cumulative=FALSE)
+     diagnose.ppm(fit, type="pearson", which="x", cumulative=FALSE)
+     diagnose.ppm(fit, type="raw", plot.neg="discrete", plot.smooth="image")
+     diagnose.ppm(fit, type="pearson", plot.neg="contour", plot.smooth="contour")
+ 
+     diagnose.ppm(fitoff, type="raw", which="smooth", plot.smooth="persp")
+     diagnose.ppm(fitoff, type="pearson", plot.neg="imagecontour")
+ 
+     plot(Frame(letterR), main="")
+     ploterodewin(letterR, erosion(letterR, 0.05), main="jeans")
+     W <- as.mask(letterR)
+     plot(Frame(W), main="")
+     ploterodewin(W, erosion(W, 0.05), main="JeAnS")
+ 
+     #' entangled terms in model
+     U <- as.im(1, owin())
+     Z <- as.im(function(x,y) x, owin())
+     X <- runifpoint(40)
+     fut <- ppm(X ~ Z:U)
+     a <- parres(fut, "Z")
+     futoff <- ppm(X ~ offset(Z*U))
+     a <- parres(futoff, "Z")
+ 
+     #' residual summary functions
+     pt <- psst(cells, interaction=Strauss(0.1), fun=nndcumfun)
+   }
+ })
> 
> 
> 
> ##
> ## tests/rhohat.R
> ##
> ## Test all combinations of options for rhohatCalc
> ##
> ## $Revision: 1.6 $ $Date: 2022/05/22 08:03:48 $
> 
> local({
+   if(FULLTEST) {
+     X <-  rpoispp(function(x,y){exp(3+3*x)})
+     Z <- as.im(function(x,y) { x }, Window(X))
+     f <- funxy(function(x,y) { y + 1 }, Window(X))
+     
+     
+     ## rhohat.ppm
+     fit <- ppm(X ~x)
+     rhofitA <- rhohat(fit, "x")
+     rhofitB <- rhohat(fit, "x", method="reweight")
+     rhofitC <- rhohat(fit, "x", method="transform")
+     rhofitD <- rhohat(fit, Z)
+     rhofitD <- rhohat(fit, Z, positiveCI=TRUE)
+     lam <- predict(fit)
+ 
+ 
+     ## Horvitz-Thompson
+     rhofitAH <- rhohat(fit, "x", horvitz=TRUE)
+     rhofitBH <- rhohat(fit, "x", method="reweight", horvitz=TRUE)
+     rhofitCH <- rhohat(fit, "x", method="transform", horvitz=TRUE)
+ 
+     r2myx <- rho2hat(fit, "y", "x")
+     r2myxw <- rho2hat(fit, "y", "x", method="reweight")
+     plot(r2myx)
+     plot(r2myxw)
+     print(r2myxw)
+     predict(r2myxw)
+     predict(r2myxw, relative=TRUE)
+   }
+ })
> 
> proc.time()
   user  system elapsed 
   2.07    0.35    2.39