R: plot a Fitted Multiple Point Process Model

plot.mppm {spatstat.core} | R Documentation

## 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"))

### 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.

### 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 Adrian.Baddeley@curtin.edu.au, Ida-Maria Sintorn and Leanne Bischoff. Implemented by Adrian Baddeley Adrian.Baddeley@curtin.edu.au

Rolf Turner r.turner@auckland.ac.nz

and Ege Rubak rubak@math.aau.dk

### References

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. London: 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)

[Package spatstat.core version 2.4-4 Index]