## use canned possum model
shorePossums <- predictDsurface(possum.model.Ds)
par(mar = c(1,1,1,6))
plot(shorePossums, plottype = "shaded", polycol = "blue", border = 100)
plot(traps(possumCH), detpar = list(col = "black"), add = TRUE)
par(mar = c(5,4,4,2) + 0.1) ## reset to default
## extract and summarise
summary(covariates(shorePossums))
## Not run: ------------------------------------
#
# ## extrapolate to a new mask; add covariate needed by model; plot
# regionmask <- make.mask(traps(possumCH), buffer = 1000, spacing = 10,
# poly = possumremovalarea)
# dts <- distancetotrap(regionmask, possumarea)
# covariates(regionmask) <- data.frame(d.to.shore = dts)
# regionPossums <- predictDsurface(possum.model.Ds, regionmask,
# se.D = TRUE, cl.D = TRUE)
# par(mfrow = c(1,2), mar = c(1,1,1,6))
# plot(regionPossums, plottype = "shaded", mesh = NA, breaks = 20)
# plot(regionPossums, plottype = "contour", add = TRUE)
# plot(regionPossums, covariate = "SE", plottype = "shaded",
# mesh = NA, breaks = 20)
# plot(regionPossums, covariate = "SE", plottype = "contour",
# add = TRUE)
#
# ## confidence surfaces
# plot(regionPossums, covariate = "lcl", breaks = seq(0,3,0.2),
# plottype = "shaded")
# plot(regionPossums, covariate = "lcl", plottype = "contour",
# add = TRUE, levels = seq(0,2.7,0.2))
# title("lower 95% surface")
# plot(regionPossums, covariate = "ucl", breaks=seq(0,3,0.2),
# plottype = "shaded")
# plot(regionPossums, covariate = "ucl", plottype = "contour",
# add = TRUE, levels = seq(0,2.7,0.2))
# title("upper 95% surface")
#
# ## annotate with CI
# par(mfrow = c(1,1))
# plot(regionPossums, plottype = "shaded", mesh = NA, breaks = 20)
# plot(traps(possumCH), add = TRUE, detpar = list(col = "black"))
# spotHeight(regionPossums, dec = 1, pre = c("lcl","ucl"), cex = 0.8)
#
# ## perspective plot
# pm <- plot(regionPossums, plottype = "persp", box = FALSE, zlim =
# c(0,3), phi=30, d = 5, col = "green", shade = 0.75, border = NA)
# lines(trans3d (possumremovalarea$x, possumremovalarea$y,
# rep(1,nrow(possumremovalarea)), pmat = pm))
#
# par(mfrow = c(1,1), mar = c(5, 4, 4, 2) + 0.1) ## reset to default
#
# ## compare estimates of region N
# ## grid cell area is 0.01 ha
# sum(covariates(regionPossums)[,"D.0"]) * 0.01
# region.N(possum.model.Ds, regionmask)
#
## ---------------------------------------------
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