R Under development (unstable) (2023-05-14 r84430 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 3.2-1
Loading required package: spatstat.random
spatstat.random 3.1-5
Loading required package: spatstat.explore
Loading required package: nlme
spatstat.explore 3.2-1
Loading required package: rpart
spatstat.model 3.2-4
> 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/marcelino.R
> ##
> ##     $Revision: 1.4 $  $Date: 2020/04/30 02:18:23 $
> ##
> 
> local({
+   if(FULLTEST) {
+     Y <- split(urkiola)
+     B <- Y$birch
+     O <- Y$oak
+     B.lam <- predict (ppm(B ~polynom(x,y,2)), type="trend")
+     O.lam <- predict (ppm(O ~polynom(x,y,2)), type="trend")
+ 
+     Kinhom(B, lambda=B.lam, correction="iso")
+     Kinhom(B, lambda=B.lam, correction="border")
+     
+     Kcross.inhom(urkiola, i="birch", j="oak", B.lam, O.lam)
+     Kcross.inhom(urkiola, i="birch", j="oak", B.lam, O.lam, correction = "iso")
+     Kcross.inhom(urkiola, i="birch", j="oak", B.lam, O.lam, correction = "border")
+   }
+ })
> 
> 
> #' tests/mctests.R
> #' Monte Carlo tests
> #'        (mad.test, dclf.test, envelopeTest, hasenvelope)
> #' $Revision: 1.5 $ $Date: 2022/05/23 04:09:49 $
> 
> 
> local({
+   if(FULLTEST) {
+     fitx <- ppm(redwood~x)
+     ax <- envelopeTest(fitx, exponent=2, nsim=9, savefuns=TRUE)
+     print(ax)
+   }
+ })
> 
> 
> #
> # tests/mppm.R
> #
> # Basic tests of mppm
> #
> # $Revision: 1.23 $ $Date: 2022/07/08 04:55:13 $
> # 
> 
> if(!FULLTEST)
+   spatstat.options(npixel=32, ndummy.min=16)
> 
> local({
+   ## test interaction formulae and subfits
+   fit1 <- mppm(Points ~ group, simba,
+                hyperframe(po=Poisson(), str=Strauss(0.1)),
+                iformula=~ifelse(group=="control", po, str))
+   fit2 <- mppm(Points ~ group, simba,
+                hyperframe(po=Poisson(), str=Strauss(0.1)),
+                iformula=~str/id)
+   fit2w <- mppm(Points ~ group, simba,
+                 hyperframe(po=Poisson(), str=Strauss(0.1)),
+                 iformula=~str/id, weights=runif(nrow(simba)))
+ # currently invalid  
+ #  fit3 <- mppm(Points ~ group, simba,
+ #               hyperframe(po=Poisson(), pie=PairPiece(c(0.05,0.1))),
+ #        iformula=~I((group=="control") * po) + I((group=="treatment") * pie))  
+ 
+   fit1
+   fit2
+   fit2w
+ #  fit3
+   
+   if(FULLTEST) {
+     ## run summary.mppm which currently sits in spatstat-internal.Rd
+     summary(fit1)
+     summary(fit2)
+     summary(fit2w)
+     #  summary(fit3)
+   }    
+ 
+   ## test vcov algorithm
+   vcov(fit1)
+   vcov(fit2)
+ #  vcov(fit3)
+   
+   if(FULLTEST) {
+     fit4 <- mppm(Points ~ group, simba, hyperframe(str=Strauss(0.1)), iformula=~str/group)
+     fit4
+     summary(fit4)
+     vcov(fit4)
+     fit0 <- mppm(Points ~ group, simba)
+     anova(fit0, fit4, test="Chi")
+     ## [bug from Fernando Milesi]
+     fit5 <- mppm(Wat ~ id,
+                data=hyperframe(Wat=waterstriders),
+                interaction=StraussHard(4.5, 1.5),
+                iformula=~Interaction:id)
+     fit5
+     summary(fit5)
+     vcov(fit5)
+   }
+   
+   ## test subfits algorithm
+   if(FULLTEST) {
+     s1 <- subfits(fit1)
+     s2 <- subfits(fit2)
+     #  s3 <- subfits(fit3)
+     s4 <- subfits(fit4)
+     s5 <- subfits(fit5)
+     
+     ## validity of results of subfits()
+     p1 <- solapply(s1, predict)
+     p2 <- solapply(s2, predict)
+     #  p3 <- solapply(s3, predict)
+     p4 <- solapply(s4, predict)
+     p5 <- solapply(s5, predict)
+   }
+ })
> 
> local({
+   if(FULLTEST) {
+     ## cases of predict.mppm
+     W <- solapply(waterstriders, Window)
+     Fakes <- solapply(W, runifpoint, n=30)
+     FakeDist <- solapply(Fakes, distfun)
+     H <- hyperframe(Bugs=waterstriders,
+                     D=FakeDist)
+     fit <- mppm(Bugs ~ D, data=H)
+     p1 <- predict(fit)
+     p2 <- predict(fit, locations=Fakes)
+     p3 <- predict(fit, locations=solapply(W, erosion, r=4))
+     locn <- as.data.frame(do.call(cbind, lapply(Fakes, coords)))
+     df <- data.frame(id=sample(1:3, nrow(locn), replace=TRUE),
+                      D=runif(nrow(locn)))
+     p4 <- predict(fit, locations=locn, newdata=df)
+ 
+     fitG <- mppm(Bugs ~ D, data=H, use.gam=TRUE)
+     p1G <- predict(fitG)
+     p2G <- predict(fitG, locations=Fakes)
+     p3G <- predict(fitG, locations=solapply(W, erosion, r=4))
+     p4G <- predict(fitG, locations=locn, newdata=df)
+   }
+ })
> 
> local({
+   ##  [thanks to Sven Wagner]
+   ## factor covariate, with some levels unused in some rows
+   if(FULLTEST) {
+     set.seed(14921788)
+     H <- hyperframe(X=replicate(3, runifpoint(20), simplify=FALSE),
+                     Z=solist(as.im(function(x,y){x}, owin()),
+                              as.im(function(x,y){y}, owin()),
+                              as.im(function(x,y){x+y}, owin())))
+     H$Z <- solapply(H$Z, cut, breaks=(0:4)/2)
+ 
+     fit6 <- mppm(X ~ Z, H)
+     v6 <- vcov(fit6)
+     s6 <- subfits(fit6)
+     p6 <- solapply(s6, predict)
+ 
+     ## random effects
+     fit7 <- mppm(X ~ Z, H, random=~1|id)
+     v7 <- vcov(fit7)
+     s7 <- subfits(fit7)
+     p7 <- solapply(s7, predict)
+ 
+     fit7a <- mppm(X ~ Z, H, random=~x|id)
+     v7a <- vcov(fit7a)
+     s7a <- subfits(fit7a)
+     p7a <- solapply(s7a, predict)
+     
+     ## multitype: collisions in vcov.ppm, predict.ppm
+     H$X <- lapply(H$X, rlabel, labels=factor(c("a","b")), permute=FALSE)
+     M <- MultiStrauss(matrix(0.1, 2, 2), c("a","b"))
+     fit8 <- mppm(X ~ Z, H, M)
+     v8 <- vcov(fit8, fine=TRUE)
+     s8 <- subfits(fit8)
+     p8 <- lapply(s8, predict)
+     c8 <- lapply(s8, predict, type="cif")
+ 
+     fit9 <- mppm(X ~ Z, H, M, iformula=~Interaction * id)
+     v9 <- vcov(fit9, fine=TRUE)
+     s9 <- subfits(fit9)
+     p9 <- lapply(s9, predict)
+     c9 <- lapply(s9, predict, type="cif")
+ 
+     ## and a simple error in recognising 'marks'
+     fit10 <- mppm(X ~ marks, H)
+   }
+ })
> 
> local({
+   if(FULLTEST) {
+     ## test handling of offsets and zero cif values in mppm
+     H <- hyperframe(Y = waterstriders)
+     (fit1 <- mppm(Y ~ 1,  data=H, Hardcore(1.5)))
+     (fit2 <- mppm(Y ~ 1,  data=H, StraussHard(7, 1.5)))
+     (fit3 <- mppm(Y ~ 1,  data=H, Hybrid(S=Strauss(7), H=Hardcore(1.5))))
+     s1 <- subfits(fit1)
+     s2 <- subfits(fit2)
+     s3 <- subfits(fit3)
+ 
+     ## prediction, in training/testing context
+     ##    (example from Markus Herrmann and Ege Rubak)
+     X <- waterstriders
+     dist <- solapply(waterstriders,
+                      function(z) distfun(runifpoint(1, Window(z))))
+     i <- 3
+     train <- hyperframe(pattern = X[-i], dist = dist[-i])
+     test <- hyperframe(pattern = X[i], dist = dist[i])
+     fit <- mppm(pattern ~ dist, data = train)
+     pred <- predict(fit, type="cif", newdata=test, verbose=TRUE)
+ 
+     ## examples from Robert Aue
+     GH <- Hybrid(G=Geyer(r=0.1, sat=3), H=Hardcore(0.01))
+     res <- mppm(Points ~ 1, interaction = GH, data=demohyper)
+     print(summary(res))
+     sub <- subfits(res, verbose=TRUE)
+     print(sub)
+   }
+ })
> 
> local({
+   if(FULLTEST) {
+     ## test handling of interaction coefficients in multitype case
+     set.seed(42)
+     XX <- as.solist(replicate(3, rthin(amacrine, 0.8), simplify=FALSE))
+     H <- hyperframe(X=XX)
+     M <- MultiStrauss(matrix(0.1, 2, 2), levels(marks(amacrine)))
+     fit <- mppm(X ~ 1, H, M)
+     co <- coef(fit)
+     subco <- sapply(subfits(fit), coef)
+     if(max(abs(subco - co)) > 0.001)
+       stop("Wrong coefficient values in subfits, for multitype interaction")
+   }
+ })
> 
> local({
+   if(FULLTEST) {
+     ## test lurking.mppm
+     ## example from 'mppm'
+     n <- 7
+     H <- hyperframe(V=1:n,
+                     U=runif(n, min=-1, max=1))
+     H$Z <- setcov(square(1))
+     H$U <- with(H, as.im(U, as.rectangle(Z)))
+     H$Y <- with(H, rpoispp(eval.im(exp(2+3*Z))))
+     fit <- mppm(Y ~ Z + U + V, data=H)
+ 
+     lurking(fit, expression(Z), type="P")
+     lurking(fit, expression(V), type="raw") # design covariate
+     lurking(fit, expression(U), type="raw") # image, constant in each row
+     lurking(fit, H$Z,           type="P")   # list of images
+   }
+ })
> 
> local({
+   if(FULLTEST) {
+     ## test anova.mppm code blocks and scoping problem
+     H <- hyperframe(X=waterstriders)
+     mod0 <- mppm(X~1, data=H, Poisson())
+     modxy <- mppm(X~x+y, data=H, Poisson())
+     mod0S <- mppm(X~1, data=H, Strauss(2))
+     modxyS <- mppm(X~x+y, data=H, Strauss(2))
+     anova(mod0, modxy, test="Chi")
+     anova(mod0S, modxyS, test="Chi")
+     anova(modxy, test="Chi")
+     anova(modxyS, test="Chi")
+     #' models with random effects (example from Marcelino de la Cruz)
+     mod0r <- mppm(X~1, data=H, Poisson(), random = ~1|id)
+     modxr <- mppm(X~x, data=H, Poisson(), random = ~1|id)
+     anova(mod0r, modxr, test="Chi")
+   }
+ })
> 
> local({
+   if(FULLTEST) {
+     ## test multitype stuff
+     foo <- flu[1:3,]
+     msh <- MultiStraussHard(iradii=matrix(100, 2, 2),
+                             hradii=matrix(10,2,2),
+                             types=levels(marks(foo$pattern[[1]])))
+     msh0 <- MultiStraussHard(iradii=matrix(100, 2, 2),
+                              hradii=matrix(10,2,2))
+     fit <- mppm(pattern ~ 1, data=foo, interaction=msh0)
+     print(fit)
+     print(summary(fit))
+     v <- vcov(fit)
+   }
+ })
> 
> reset.spatstat.options()
> #'
> #'     tests/msr.R
> #'
> #'     $Revision: 1.5 $ $Date: 2020/11/30 07:27:44 $
> #'
> #'     Tests of code for measures
> #'
> 
> if(FULLTEST) {
+ local({
+     
+   ## cases of 'msr'
+   Q <- quadscheme(cells)
+   nQ <- n.quad(Q)
+   nX <- npoints(cells)
+   A <- matrix(nX * 3, nX, 3)
+   B <- matrix(nQ * 3, nQ, 3)
+ 
+   m <- msr(Q, A, B)
+ 
+   M <- msr(Q, A, 1)
+   M <- msr(Q, 1, B)
+   M <- msr(Q, A, B[,1])
+   M <- msr(Q, A[,1], B)
+   M <- msr(Q, A, B[,1,drop=FALSE])
+   M <- msr(Q, A[,1,drop=FALSE], B)
+ 
+   ## methods
+   a <- summary(m)
+   b <- is.marked(m)
+   w <- as.owin(m)
+   z <- domain(m)
+   ss <- scalardilate(m, 2)
+   tt <- rescale(m, 2)
+   ee <- rotate(m, pi/4)
+   aa <- affine(m, mat=diag(c(1,2)), vec=c(0,1))
+   ff <- flipxy(m)
+   
+   am <- augment.msr(m, sigma=0.08)
+   ua <- update(am)
+   
+   rr <- residuals(ppm(cells ~ x))
+   mm <- residuals(ppm(amacrine ~ x))
+   ss <- residuals(ppm(amacrine ~ x), type="score")
+   gg <- rescale(ss, 1/662, c("micron", "microns"))
+ 
+   plot(mm)
+   plot(mm, multiplot=FALSE)
+   plot(mm, equal.markscale=TRUE, equal.ribbon=TRUE)
+   plot(ss)
+   plot(ss, multiplot=FALSE)
+ })
+ }
> 
> proc.time()
   user  system elapsed 
   4.54    0.40    4.92