plot.locstppm: Plot of the fitted intensity of a local spatio-temporal Poisson process model

Description

The function plots the local fitted intensity, displayed both in space and in space and time.

Usage

# S3 method for locstppm
plot(
  x,
  scaler = c("silverman", "IQR", "sd", "var"),
  do.points = TRUE,
  print.bw = FALSE,
  zap = 1e-05,
  par = TRUE,
  ...
)

Arguments

x: An object of class locstppm
scaler: Optional. Controls the value for a scalar representation of the spatial scale of the data. Either a character string, "silverman" (default), "IQR", "sd", or "var"; or positive numeric value(s). See OS.
do.points: Add points to plot
print.bw: It prints the estimated oversmoothing (OS) bandwidth selector
zap: Noise threshold factor (default to 0.00001). A numerical value greater than or equal to 1. If the range of pixel values is less than zap * .Machine$double.eps, the image will be treated as constant. This avoids displaying images which should be constant but contain small numerical errors.
par: Default to TRUE.
...: additional unused argument

Author

Nicoletta D'Angelo

References

D'Angelo, N., Adelfio, G., and Mateu, J. (2023). Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes. Computational Statistics & Data Analysis, 180, 107679.

Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437.

Terrell, G.R. (1990). The maximal smoothing principle in density estimation, Journal of the American Statistical Association, 85, 470-477.

Examples

Run this code


set.seed(2)
inh <- rstpp(lambda = function(x, y, t, a) {exp(a[1] + a[2]*x)}, 
             par = c(0.005, 5))
inh_local <- locstppm(inh, formula = ~ x)

plot(inh_local)