plot.mppm {spatstat.model}

plot a Fitted Multiple Point Process Model

Description

Given a point process model fitted to multiple point patterns by mppm, compute spatial trend or conditional intensity surface of the model, in a form suitable for plotting, and (optionally) plot this surface.

Usage

  ## S3 method for class 'mppm'
plot(x, ...,
                trend=TRUE, cif=FALSE, se=FALSE,
                how=c("image", "contour", "persp"),
                main)

Arguments

x

A point process model fitted to multiple point patterns, typically obtained from the model-fitting algorithm mppm. An object of class "mppm".

...

Arguments passed to plot.ppm or plot.anylist controlling the plot.

trend

Logical value indicating whether to plot the fitted trend.

cif

Logical value indicating whether to plot the fitted conditional intensity.

se

Logical value indicating whether to plot the standard error of the fitted trend.

how

Single character string indicating the style of plot to be performed.

main

Character string for the main title of the plot.

Details

This is the plot method for the class "mppm" of point process models fitted to multiple point patterns (see mppm).

It invokes subfits to compute the fitted model for each individual point pattern dataset, then calls plot.ppm to plot these individual models. These individual plots are displayed using plot.anylist, which generates either a series of separate plot frames or an array of plot panels on a single page.

Value

NULL.

Author(s)

Adrian Baddeley [e-mail], Ida-Maria Sintorn and Leanne Bischoff. Implemented by Adrian Baddeley [e-mail]

Rolf Turner [e-mail]

and Ege Rubak [e-mail]

References

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

See Also

plot.ppm, mppm, plot.listof

Examples

  # Synthetic data from known model
  n <- 9
  H <- hyperframe(V=1:n,
                  U=runif(n, min=-1, max=1))
  H$Z <- setcov(square(1))
  H$U <- with(H, as.im(U, as.rectangle(Z)))
  H$Y <- with(H, rpoispp(eval.im(exp(2+3*Z))))

  fit <- mppm(Y ~Z + U + V, data=H)

  plot(fit)