#'
#' 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)
FULLTEST <- (nchar(Sys.getenv("SPATSTAT_TEST", unset="")) > 0)
ALWAYS <- TRUE
cat(paste("--------- Executing",
if(FULLTEST) "** ALL **" else "**RESTRICTED** subset of",
"test code -----------\n"))
#' 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)
}
})
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