1-Dimensional NonHomogeneous Poisson example.
Point data and count data, together with intensity function and expected counts for a unimodal nonhomogeneous 1-dimensional Poisson process example.
data(Poisson2_1D)
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.
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)
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