plot.locstppm {stopp}

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 class '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(s)

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.

See Also

locstppm, print.locstppm, summary.locstppm

Examples

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)