plot.globaldiag {stopp}

Plot of the global diagnostics of a spatio-temporal point process first-order intensity

Description

This function performs global diagnostics of a model fitted for the first-order intensity of a spatio-temporal point pattern, by returning the plots of the inhomogeneous K-function weighted by the provided intensity to diagnose, its theoretical value, and their difference.

Usage

## S3 method for class 'globaldiag'
plot(x, samescale = TRUE, ...)

Arguments

x

A globaldiag object

samescale

Logical value. It indicates whether to plot the observed and the theoretical K-function in the same or different scale. Default to TRUE.

...

additional unused argument

Value

It plots three panels: the observed K-function, as returned by STLKinhom; the theoretical one; their difference. The function also prints the sum of squared differences between the observed and theoretical K-function on the console.

Author(s)

Nicoletta D'Angelo

References

Adelfio, G., Siino, M., Mateu, J., and Rodríguez-Cortés, F. J. (2020). Some properties of local weighted second-order statistics for spatio-temporal point processes. Stochastic Environmental Research and Risk Assessment, 34(1), 149-168.

D’Angelo, N., Adelfio, G. and Mateu, J. (2022) Local inhomogeneous second-order characteristics for spatio-temporal point processes on linear networks. Stat Papers. https://doi.org/10.1007/s00362-022-01338-4

Gabriel, E., and Diggle, P. J. (2009). Second‐order analysis of inhomogeneous spatio‐temporal point process data. Statistica Neerlandica, 63(1), 43-51.

Gabriel, E., Rowlingson, B. S., & Diggle, P. J. (2013). stpp: An R Package for Plotting, Simulating and Analyzing Spatio-Temporal Point Patterns. Journal of Statistical Software, 53(2), 1–29. https://doi.org/10.18637/jss.v053.i02

Moradi M, Cronie O, and Mateu J (2020). stlnpp: Spatio-temporal analysis of point patterns on linear networks.

Moradi, M. M., and Mateu, J. (2020). First-and second-order characteristics of spatio-temporal point processes on linear networks. Journal of Computational and Graphical Statistics, 29(3), 432-443.

See Also

globaldiag, print.globaldiag, summary.globaldiag

Examples

set.seed(2)
inh <- rstpp(lambda = function(x, y, t, a) {exp(a[1] + a[2]*x)}, 
               par = c(.3, 6))

mod1 <- stppm(inh, formula = ~ 1)
mod2 <- stppm(inh, formula = ~ x)

g1 <- globaldiag(inh, mod1$l)
g2 <- globaldiag(inh, mod2$l)

plot(g1)
plot(g2)