R Under development (unstable) (2024-11-18 r87347 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 3.1-1
Loading required package: spatstat.geom
spatstat.geom 3.3-4
Loading required package: spatstat.random
spatstat.random 3.3-2
Loading required package: spatstat.explore
Loading required package: nlme
spatstat.explore 3.3-3
Loading required package: rpart
spatstat.model 3.3-3
> 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/hobjects.R
> #'
> #'   Validity of methods for ppm(... method="ho")
> #'
> #'   $Revision: 1.4 $ $Date: 2022/06/18 10:14:44 $
> 
> 
> if(FULLTEST) {
+ local({
+   set.seed(42)
+   fit  <- ppm(cells ~1,         Strauss(0.1), improve.type="ho", nsim=10)
+   fitx <- ppm(cells ~offset(x), Strauss(0.1), improve.type="ho", nsim=10)
+ 
+   a  <- AIC(fit)
+   ax <- AIC(fitx)
+ 
+   f  <- fitted(fit)
+   fx <- fitted(fitx)
+ 
+   p  <- predict(fit)
+   px <- predict(fitx)
+ })
+ }
> 
> 
> #'     tests/hypotests.R
> #'     Hypothesis tests
> #' 
> #'  $Revision: 1.10 $ $Date: 2023/07/17 07:30:48 $
> 
> if(FULLTEST) {
+ local({
+ 
+   #' scan test with baseline
+   fit <- ppm(cells ~ x)
+   lam <- predict(fit)
+   rr <- c(0.05, 1)
+   scan.test(cells, rr, nsim=5,
+             method="poisson", baseline=fit, alternative="less")
+   scan.test(cells, rr, nsim=5,
+             method="poisson", baseline=lam, alternative="less")
+ })
+ }
> #'
> #'  tests/interact.R
> #'
> #'  Support for interaction objects
> #'
> #'  $Revision: 1.2 $ $Date: 2020/04/28 12:58:26 $
> 
> if(FULLTEST) {
+ local({
+   #' print.intermaker
+   Strauss
+   Geyer
+   Ord
+   #' intermaker
+   BS <- get("BlankStrauss", envir=environment(Strauss))
+   BD <- function(r) { instantiate.interact(BS, list(r=r)) }
+   BlueDanube <- intermaker(BD, BS) 
+ })
+ }
> 
> #'   tests/ippm.R
> #'   Tests of 'ippm' class
> #'   $Revision: 1.6 $ $Date: 2020/04/28 12:58:26 $
> 
> if(FULLTEST) {
+ local({
+   # .......... set up example from help file .................
+   nd <- 10
+   gamma0 <- 3
+   delta0 <- 5
+   POW <- 3
+   # Terms in intensity
+   Z <- function(x,y) { -2*y }
+   f <- function(x,y,gamma,delta) { 1 + exp(gamma - delta * x^POW) }
+   # True intensity
+   lamb <- function(x,y,gamma,delta) { 200 * exp(Z(x,y)) * f(x,y,gamma,delta) }
+   # Simulate realisation
+   lmax <- max(lamb(0,0,gamma0,delta0), lamb(1,1,gamma0,delta0))
+   set.seed(42)
+   X <- rpoispp(lamb, lmax=lmax, win=owin(), gamma=gamma0, delta=delta0)
+   # Partial derivatives of log f
+   DlogfDgamma <- function(x,y, gamma, delta) {
+     topbit <- exp(gamma - delta * x^POW)
+     topbit/(1 + topbit)
+   }
+   DlogfDdelta <- function(x,y, gamma, delta) {
+     topbit <- exp(gamma - delta * x^POW)
+     - (x^POW) * topbit/(1 + topbit)
+   }
+   # irregular score
+   Dlogf <- list(gamma=DlogfDgamma, delta=DlogfDdelta)
+   # fit model
+   fit <- ippm(X ~Z + offset(log(f)),
+               covariates=list(Z=Z, f=f),
+               iScore=Dlogf,
+               start=list(gamma=1, delta=1),
+               nd=nd)
+   # fit model with logistic likelihood but without iScore
+   fitlo <- ippm(X ~Z + offset(log(f)),
+                 method="logi",
+                 covariates=list(Z=Z, f=f),
+                 start=list(gamma=1, delta=1),
+                 nd=nd)
+ 
+   ## ............. test ippm class support ......................
+   Ar <- model.matrix(fit)
+   Ai <- model.matrix(fit, irregular=TRUE)
+   An <- model.matrix(fit, irregular=TRUE, keepNA=FALSE)
+   AS <- model.matrix(fit, irregular=TRUE, subset=(abs(Z) < 0.5))
+ 
+   Zr <- model.images(fit)
+   Zi <- model.images(fit, irregular=TRUE)
+   ## update.ippm
+   fit2 <- update(fit, . ~ . + I(Z^2))
+   fit0 <- update(fit,
+                  . ~ . - Z,
+                  start=list(gamma=2, delta=4))
+   oldfit <- ippm(X,
+               ~Z + offset(log(f)),
+               covariates=list(Z=Z, f=f),
+               iScore=Dlogf,
+               start=list(gamma=1, delta=1),
+               nd=nd)
+   oldfit2 <- update(oldfit, . ~ . + I(Z^2))
+   oldfit0 <- update(oldfit,
+                     . ~ . - Z,
+                     start=list(gamma=2, delta=4))
+   ## again with logistic
+   fitlo2 <- update(fitlo, . ~ . + I(Z^2))
+   fitlo0 <- update(fitlo,
+                    . ~ . - Z,
+                    start=list(gamma=2, delta=4))
+   oldfitlo <- ippm(X,
+                    ~Z + offset(log(f)),
+                    method="logi",
+                    covariates=list(Z=Z, f=f),
+                    start=list(gamma=1, delta=1),
+                    nd=nd)
+   oldfitlo2 <- update(oldfitlo, . ~ . + I(Z^2))
+   oldfitlo0 <- update(oldfitlo,
+                       . ~ . - Z,
+                       start=list(gamma=2, delta=4))
+   ## anova.ppm including ippm objects
+   fit0 <- update(fit, . ~ Z)
+   fit0lo <- update(fitlo, . ~ Z)
+   A <- anova(fit0, fit)
+   Alo <- anova(fit0lo, fitlo)
+ })
+ }
> 
> proc.time()
   user  system elapsed 
   1.67    0.29    1.96