inlabru (version 2.1.9)

Poisson2_1D: 1-Dimensional NonHomogeneous Poisson example.

Description

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:

x

The grid cell midpoint.

count

The number of detections in the cell.

exposure

The width of the cell.

Examples

# NOT RUN {
library(ggplot2)
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)
# }