Point data and count data, together with intensity function and expected counts for a unimodal nonhomogeneous 1-dimensional Poisson process example.

## Usage

data(Poisson2_1D)

## Format

The data contain the following R objects:

lambda2_1D:

A function defining the intensity function of a nonhomogeneous Poisson process. Note that this function is only defined on the interval (0,55).

cov2_1D:

A function that gives what we will call a 'habitat suitability' covariate in 1D space.

E_nc2

The expected counts of the gridded data.

pts2

The locations of the observed points (a data frame with one column, named x).

countdata2

A data frame with three columns, containing the count data:

## Examples

# \donttest{
if (require("ggplot2", quietly = TRUE)) {
data(Poisson2_1D)
p1 <- ggplot(countdata2) +
geom_point(data = countdata2, aes(x = x, y = count), col = "blue") +
ylim(0, max(countdata2$count, E_nc2)) + geom_point( data = countdata2, aes(x = x), y = 0, shape = "+", col = "blue", cex = 4 ) + geom_point( data = data.frame(x = countdata2$x, y = E_nc2), aes(x = x),
y = E_nc2, shape = "_", cex = 5
) +
xlab(expression(bold(s))) +
ylab("count")
ss <- seq(0, 55, length = 200)
lambda <- lambda2_1D(ss)
p2 <- ggplot() +
geom_line(
data = data.frame(x = ss, y = lambda),
aes(x = x, y = y), col = "blue"
) +
ylim(0, max(lambda)) +
geom_point(data = pts2, aes(x = x), y = 0.2, shape = "|", cex = 4) +
xlab(expression(bold(s))) +
ylab(expression(lambda(bold(s))))
multiplot(p1, p2, cols = 1)
}

# }