plot.stlgcppm {stopp}

Plot of the fitted intensity of a LGCP model

Description

The function plots the fitted intensity, displayed both in space and in space and time. In the case of local covariance parameters, the function returns the mean of the random intensity, displayed both in space and in space and time.

Usage

## S3 method for class 'stlgcppm'
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 stlgcppm

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 and Giada Adelfio

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.

Siino, M., Adelfio, G., and Mateu, J. (2018). Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes. Stochastic environmental research and risk assessment, 32(12), 3525-3539.

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

See Also

stlgcppm, print.stlgcppm, summary.stlgcppm, localsummary, localplot

Examples

catsub <- stp(greececatalog$df[1:200, ])

lgcp_loc <- stlgcppm(catsub, formula = ~ x, first = "local")

plot(lgcp_loc)