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/legacy.R
> ##
> ## Test that current version of spatstat is compatible with outmoded usage
> ## $Revision: 1.3 $ $Date: 2020/04/29 08:55:17 $
> 
> if(FULLTEST) {
+ local({
+ 
+   ## (1) Old syntax of ppm
+   ppm(cells, ~x)
+   
+   ## (2) Old syntax of MultiStrauss etc.
+   r <- matrix(3, 2, 2)
+   a <- MultiStrauss( , r)
+   a <- MultiStrauss(NULL, r)
+   a <- MultiHard(, r)
+   
+   h <- r/2
+   a <- MultiStraussHard( , r, h)
+ 
+   NULL
+ })
+ }
> #'
> #'    tests/leverinf.R
> #'
> #'   leverage and influence for Gibbs models
> #' 
> #'   $Revision: 1.35 $ $Date: 2022/06/18 10:15:17 $
> #' 
> 
> if(FULLTEST) {
+   Cells <- cells
+   Amacrine <- amacrine
+   Redwood <- redwood
+ } else {
+   ## reduce number of data + dummy points
+   spatstat.options(npixel=32, ndummy.min=16)
+   Cells <- cells[c(FALSE,TRUE)]
+   Redwood <- redwood[c(FALSE, TRUE)]
+   Amacrine <- amacrine[c(FALSE, TRUE)]
+ } 
> 
> local({
+   cat("Running non-sparse algorithm...", fill=TRUE)
+   # original non-sparse algorithm
+   Leverage <- function(...) leverage(..., sparseOK=FALSE)
+   Influence <- function(...) influence(..., sparseOK=FALSE)
+   Dfbetas <- function(...) dfbetas(..., sparseOK=FALSE)
+   if(ALWAYS) {
+     ## Strauss()$delta2
+     fitS <- ppm(Cells ~ x, Strauss(0.12), rbord=0)
+     levS <- Leverage(fitS)
+     infS <- Influence(fitS)
+     dfbS <- Dfbetas(fitS)
+     ## Geyer()$delta2
+     fitG <- ppm(Redwood ~ 1, Geyer(0.1, 2), rbord=0)
+     levG <- Leverage(fitG)
+     infG <- Influence(fitG)
+     ## AreaInter()$delta2
+     fitA <- ppm(Cells ~ 1, AreaInter(0.06), rbord=0, nd=11)
+     levA <- Leverage(fitA)
+     infA <- Influence(fitA)
+     ## pairwise.family$delta2
+     fitD <- ppm(Cells ~ 1, DiggleGatesStibbard(0.12), rbord=0)
+     levD <- Leverage(fitD)
+     infD <- Influence(fitD)
+     ## DiggleGratton() special code
+     fitDG <- ppm(Cells ~ 1, DiggleGratton(0.05, 0.12), rbord=0)
+     levDG <- Leverage(fitDG)
+     infDG <- Influence(fitDG)
+     ## ppmInfluence; offset is present; coefficient vector has length 0
+     fitH <- ppm(Cells ~ 1, Hardcore(0.07))
+     levH <- Leverage(fitH)
+     infH <- Influence(fitH)
+     ## ppmInfluence; hard core
+     fitSH <- ppm(Cells ~ 1, StraussHard(0.07, 0.01))
+     levSH <- Leverage(fitSH)
+     infSH <- Influence(fitSH)
+     ## ppmInfluence; offset is present; coefficient vector has length 1
+     fitHx <- ppm(Cells ~ x, Hardcore(0.07), rbord=0)
+     levHx <- Leverage(fitHx)
+     infHx <- Influence(fitHx)
+     ## multitype 
+     futAm <- ppm(Amacrine ~ x + marks, Strauss(0.07))
+     levAm <- leverage(futAm)
+   }
+ 
+   if(FULLTEST) {
+     ## .........   class support .............................
+     ## other methods for classes leverage.ppm and influence.ppm
+     ## not elsewhere tested
+     cat("Testing class support...", fill=TRUE)
+     w <- domain(levS)
+     w <- Window(infS)
+     vv <- shift(levS, c(1.2, 1.3))
+     vv <- shift(infS, c(1.2, 1.3))
+     A <- quadrats(Window(Cells), 2)
+     a <- integral(levS,domain=A)
+     b <- integral(infS,domain=A)
+     u <- Smooth(levS, sigma=0.07)
+     v <- Smooth(infS, sigma=0.1)
+     ## plot options
+     plot(levS, what="exact")
+     plot(levS, what="nearest")
+     contour(levS, what="nearest")
+     persp(levS, what="nearest")
+     ## plotting for multitype models
+     plot(levAm)
+     contour(levAm)
+     persp(levAm)
+     plot(levAm, multiplot=FALSE)
+     contour(levAm, multiplot=FALSE)
+   }
+ 
+   if(ALWAYS) {
+     ## ..........  compare algorithms .........................
+     ## divide and recombine algorithm
+     cat("Reduce maximum block side to 50,000 ...", fill=TRUE)
+     op <- spatstat.options(maxmatrix=50000)
+     ## non-sparse
+     levSB <- Leverage(fitS)
+     infSB <- Influence(fitS)
+     dfbSB <- Dfbetas(fitS)
+   }
+ 
+   chk <- function(x, y, what,
+                   from="single-block and multi-block",
+                   thresh=1e-12) {
+     if(max(abs(x-y)) > thresh)
+       stop(paste("Different results for", what, "obtained from",
+                  from, "algorithms"),
+            call.=FALSE)
+     invisible(NULL)
+   }
+ 
+   if(ALWAYS) {
+     cat("Compare single-block to multi-block...", fill=TRUE)
+     chk(marks(as.ppp(infS)), marks(as.ppp(infSB)), "influence")
+     chk(as.im(levS),         as.im(levSB),         "leverage")
+     chk(dfbS$val,            dfbSB$val,            "dfbetas$value")
+     chk(dfbS$density,        dfbSB$density,        "dfbetas$density")
+   }
+ 
+   if(FULLTEST) {
+     ## also check case of zero cif
+     cat("Check zero cif cases...", fill=TRUE)
+     levHB <- Leverage(fitH)
+     infHB <- Influence(fitH)
+     dfbHB <- Dfbetas(fitH)
+     levHxB <- Leverage(fitHx)
+     infHxB <- Influence(fitHx)
+     dfbHxB <- Dfbetas(fitHx)
+   }
+ 
+   ## run all code segments
+   Everything <- function(model, ...) { ppmInfluence(model, ..., what="all") }
+     
+   if(FULLTEST) {
+     cat("Run full code on AreaInteraction model...", fill=TRUE)
+     pmiA <- Everything(fitA)
+     
+     ## sparse algorithm, with blocks
+     cat("Run sparse algorithm with blocks...", fill=TRUE)
+     pmiSSB <- Everything(fitS, sparseOK=TRUE)
+     ## also check case of zero cif
+     pmiHSB <- Everything(fitH, sparseOK=TRUE)
+     pmiSHSB <- Everything(fitSH, sparseOK=TRUE)
+     pmiHxSB <- Everything(fitHx, sparseOK=TRUE)
+ 
+     cat("Reinstate maxmatrix...", fill=TRUE)
+     spatstat.options(op)
+   }
+ 
+   if(ALWAYS) {
+     ## sparse algorithm, no blocks
+     cat("Compare sparse and non-sparse results...", fill=TRUE)
+     pmi <- Everything(fitS, sparseOK=TRUE)
+     levSp <- pmi$leverage
+     infSp <- pmi$influence
+     dfbSp <- pmi$dfbetas
+     chks <- function(...) chk(..., from="sparse and non-sparse")
+   
+     chks(marks(as.ppp(infS)), marks(as.ppp(infSp)), "influence")
+     chks(as.im(levS),         as.im(levSp),         "leverage")
+     chks(dfbS$val,            dfbSp$val,            "dfbetas$value")
+     chks(dfbS$density,        dfbSp$density,        "dfbetas$density")
+   }
+ 
+   if(ALWAYS) {
+     #' case of zero cif
+     cat("zero cif...", fill=TRUE)
+     pmiH <- Everything(fitH, sparseOK=TRUE)
+     pmiSH <- Everything(fitSH, sparseOK=TRUE)
+     pmiHx <- Everything(fitHx, sparseOK=TRUE)
+   }
+   if(FULLTEST) {
+     #' other code blocks - check execution only
+     cat("other code blocks...", fill=TRUE)
+     a <- Everything(fitS) 
+     a <- Everything(fitS, method="interpreted") 
+     a <- Everything(fitS, method="interpreted", entrywise=FALSE)
+     a <- Everything(fitS,                       entrywise=FALSE)
+     #' zero cif
+     b <- Everything(fitSH) 
+     b <- Everything(fitSH, method="interpreted") 
+     b <- Everything(fitSH, method="interpreted", entrywise=FALSE)
+     b <- Everything(fitSH,                       entrywise=FALSE)
+   }
+   #' NOTE: code for irregular parameters is tested below, and in 'make bookcheck'
+ 
+   ## ...........  logistic fits .......................
+   cat("Logistic fits...", fill=TRUE)
+   #'  special algorithm for delta2
+   fitSlogi <- ppm(Cells ~ x, Strauss(0.12), rbord=0, method="logi")
+   
+   if(FULLTEST) {
+     pmiSlogi <- Everything(fitSlogi)
+     #'  special algorithm for delta2
+     fitGlogi <- ppm(Redwood ~ 1, Geyer(0.1, 2), rbord=0, method="logi")
+     pmiGlogi <- Everything(fitGlogi)
+     #'  generic algorithm for delta2
+     fitDlogi <- ppm(Cells ~ 1, DiggleGatesStibbard(0.12),
+                     rbord=0, method="logi")
+     pmiDlogi <- Everything(fitDlogi)
+     #'  generic algorithm for delta2 : offset; zero-dimensional 
+     fitHlogi <- ppm(Cells ~ 1, Hardcore(0.07), method="logi")
+     pmiHlogi <- Everything(fitHlogi)
+     #'  generic algorithm for delta2 : offset; 1-dimensional 
+     fitHxlogi <- ppm(Cells ~ x, Hardcore(0.07), rbord=0, method="logi")
+     pmiHxlogi <- Everything(fitHxlogi)
+     #' plotting
+     plot(leverage(fitSlogi))
+     plot(influence(fitSlogi))
+     plot(dfbetas(fitSlogi))
+   }
+ 
+   if(ALWAYS) {
+     #' other code blocks - check execution only
+     cat("Other code blocks...", fill=TRUE)
+     b <- Everything(fitSlogi)  # i.e. full set of results
+     b <- Everything(fitSlogi, method="interpreted") 
+     b <- Everything(fitSlogi, method="interpreted", entrywise=FALSE)
+     b <- Everything(fitSlogi,                       entrywise=FALSE) 
+   }
+   
+   #' irregular parameters
+   cat("Irregular parameters...", fill=TRUE)
+   ytoa <- function(x,y, alpha=1) { y^alpha }
+   lam <- function(x,y,alpha=1) { exp(4 + y^alpha) }
+   set.seed(90210)
+   X <- rpoispp(lam, alpha=2)
+   iScor <- list(alpha=function(x,y,alpha) { alpha * y^(alpha-1) } )
+   iHess <- list(alpha=function(x,y,alpha) { alpha * (alpha-1) * y^(alpha-2) } )
+   gogo <- function(tag, ..., iS=iScor, iH=iHess) {
+     cat(tag, fill=TRUE)
+     #' compute all leverage+influence terms
+     ppmInfluence(..., what="all", iScore=iS, iHessian=iH)
+   }
+   gogogo <- function(hdr, fit) {
+     cat(hdr, fill=TRUE)
+     force(fit)
+     #' try all code options
+     d <- gogo("a", fit)
+     d <- gogo("b", fit, method="interpreted") 
+     d <- gogo("c", fit, method="interpreted", entrywise=FALSE)
+     d <- gogo("d", fit,                       entrywise=FALSE) 
+     invisible(NULL)
+   }
+   gogogo("Offset model...",
+          ippm(X ~ offset(ytoa), start=list(alpha=1), iterlim=40))
+   gogogo("Offset model (logistic) ...", 
+          ippm(X ~ offset(ytoa), start=list(alpha=1),
+               method="logi", iterlim=40))
+   gogogo("Offset+x model...", 
+          ippm(X ~ x + offset(ytoa), start=list(alpha=1), iterlim=40))
+   gogogo("Offset+x model (logistic) ...", 
+          ippm(X ~ x + offset(ytoa), start=list(alpha=1),
+               method="logi", iterlim=40))
+   gogogo("Offset model Strauss ...", 
+          ippm(X ~ offset(ytoa), Strauss(0.07), start=list(alpha=1), iterlim=40))
+   gogogo("Offset model Strauss (logistic) ...", 
+          ippm(X ~ offset(ytoa), Strauss(0.07), start=list(alpha=1),
+               method="logi", iterlim=40))
+   if(FULLTEST) {
+     gogogo("Offset+x model Strauss ...", 
+            ippm(X ~ x + offset(ytoa), Strauss(0.07), start=list(alpha=1),
+                 iterlim=40))
+     gogogo("Offset+x model Strauss (logistic)...", 
+            ippm(X ~ x + offset(ytoa), Strauss(0.07), start=list(alpha=1),
+                 method="logi", iterlim=40))
+   }
+   #'
+   if(FULLTEST) {
+     set.seed(452)
+     foo <- ppm(Cells ~ 1, Strauss(0.15), improve.type="ho", nsim=5)
+     aa <- Everything(foo)
+ 
+     #' Gradient and Hessian obtained by symbolic differentiation
+     f <- deriv(expression((1+x)^a),
+                "a", function.arg=c("x", "y", "a"),
+                hessian=TRUE)
+     #' check they can be extracted
+     fit <- ippm(Cells ~offset(f), start=list(a=0.7))
+     Everything(fit)
+   }
+ })
Running non-sparse algorithm...
Reduce maximum block side to 50,000 ...
Compare single-block to multi-block...
Compare sparse and non-sparse results...
zero cif...
Logistic fits...
Other code blocks...
Irregular parameters...
Offset model...
a
b
c
d
Offset model (logistic) ...
a
b
c
d
Offset+x model...
a
b
c
d
Offset+x model (logistic) ...
a
b
c
d
Offset model Strauss ...
a
Large quadrature scheme split into blocks to avoid memory size limits; 404 dummy points split into 2 blocks, each containing 202 dummy points
b
Large quadrature scheme split into blocks to avoid memory size limits; 404 dummy points split into 2 blocks, each containing 202 dummy points
c
Large quadrature scheme split into blocks to avoid memory size limits; 404 dummy points split into 2 blocks, each containing 202 dummy points
d
Large quadrature scheme split into blocks to avoid memory size limits; 404 dummy points split into 2 blocks, each containing 202 dummy points
Offset model Strauss (logistic) ...
a
Large quadrature scheme split into blocks to avoid memory size limits; 400 dummy points split into 2 blocks, each containing 200 dummy points
b
Large quadrature scheme split into blocks to avoid memory size limits; 400 dummy points split into 2 blocks, each containing 200 dummy points
c
Large quadrature scheme split into blocks to avoid memory size limits; 400 dummy points split into 2 blocks, each containing 200 dummy points
d
Large quadrature scheme split into blocks to avoid memory size limits; 400 dummy points split into 2 blocks, each containing 200 dummy points
There were 11 warnings (use warnings() to see them)
> 
> reset.spatstat.options()
> 
> proc.time()
   user  system elapsed 
  31.06    7.29   38.37