predictDsurface: Predict Density Surface

Description

Predict density at each point on a raster mask from a fitted secr model.

Usage

predictDsurface(object, mask = NULL, se.D = FALSE, cl.D = FALSE, alpha =
0.05, parameter = c('D', 'noneuc'))

Arguments

object

fitted secr object

mask

secr mask object

se.D

logical for whether to compute prediction SE

cl.D

logical for whether to compute confidence limits

alpha

alpha level for 100(1 -- alpha)% confidence intervals

parameter

character for real parameter to predict

Value

Object of class `Dsurface' inheriting from `mask'. Predicted densities are added to the covariate dataframe (attribute `covariates') as column(s) with prefix `D.' If the model uses multiple groups, multiple columns will be distinguished by the group name (e.g., "D.F" and "D.M"). If groups are not defined the column is named "D.0".

For multi-session models the value is a multi-session mask.

The pointwise prediction SE is saved as a covariate column prefixed `SE.' (or multiple columns if multiple groups). Confidence limits are likewise saved with prefixes `lcl.' and `ucl.'.

Details

Predictions use the linear model for density on the link scale in the fitted secr model `object', or the fitted user-defined function, if that was specified in secr.fit.

If `mask' is NULL then predictions are for the mask component of `object'.

SE and confidence limits are computed only if specifically requested. They are not available for user-defined density functions.

Density is adjusted automatically for the number of clusters in `mashed' models (see mash).

Examples

Run this code

# NOT RUN {
## 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)

# }
# NOT RUN {
# }

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