R: plot a Fitted Point Process Model

plot.ppm {spatstat.core} | R Documentation

## plot a Fitted Point Process Model

### Description

Given a fitted point process model obtained by `ppm`, create spatial trend and conditional intensity surfaces of the model, in a form suitable for plotting, and (optionally) plot these surfaces.

### Usage

      ## S3 method for class 'ppm'
    plot(x, ngrid = c(40,40), superimpose = TRUE,
                      trend = TRUE, cif = TRUE, se = TRUE, pause = interactive(),
                      how=c("persp","image", "contour"), plot.it = TRUE,
                      locations = NULL, covariates=NULL, ...)

### Arguments

`x` | A fitted point process model, typically obtained from the model-fitting algorithm `ppm`. An object of class `"ppm"`.
`ngrid` | The dimensions for a grid on which to evaluate, for plotting, the spatial trend and conditional intensity. A vector of 1 or 2 integers. If it is of length 1, `ngrid` is replaced by `c(ngrid,ngrid)`.
`superimpose` | logical flag; if `TRUE` (and if `plot=TRUE`) the original data point pattern will be superimposed on the plots.
`trend` | logical flag; if `TRUE`, the spatial trend surface will be produced.
`cif` | logical flag; if `TRUE`, the conditional intensity surface will be produced.
`se` | logical flag; if `TRUE`, the estimated standard error of the spatial trend surface will be produced.
`pause` | logical flag indicating whether to pause with a prompt after each plot. Set `pause=FALSE` if plotting to a file. (This flag is ignored if `plot=FALSE`).
`how` | character string or character vector indicating the style or styles of plots to be performed. Ignored if `plot=FALSE`.
`plot.it` | logical scalar; should a plot be produced immediately?
`locations` | If present, this determines the locations of the pixels at which predictions are computed. It must be a binary pixel image (an object of class `"owin"` with type `"mask"`). (Incompatible with `ngrid`).
`covariates` | Values of external covariates required by the fitted model. Passed to `predict.ppm`.
`...` | extra arguments to the plotting functions `persp`, `image` and `contour`.

### Details

This is the `plot` method for the class `"ppm"` (see `ppm.object` for details of this class).

It invokes `predict.ppm` to compute the spatial trend and conditional intensity of the fitted point process model. See `predict.ppm` for more explanation about spatial trend and conditional intensity.

The default action is to create a rectangular grid of points in (the bounding box of) the observation window of the data point pattern, and evaluate the spatial trend and conditional intensity of the fitted spatial point process model `x` at these locations. If the argument `locations=` is supplied, then the spatial trend and conditional intensity are calculated at the grid of points specified by this argument.

The argument `locations`, if present, should be a binary image mask (an object of class `"owin"` and type `"mask"`). This determines a rectangular grid of locations, or a subset of such a grid, at which predictions will be computed. Binary image masks are conveniently created using `as.mask`.

The argument `covariates` gives the values of any spatial covariates at the prediction locations. If the trend formula in the fitted model involves spatial covariates (other than the Cartesian coordinates `x`, `y`) then `covariates` is required.

The argument `covariates` has the same format and interpretation as in `predict.ppm`. It may be either a data frame (the number of whose rows must match the number of pixels in `locations` multiplied by the number of possible marks in the point pattern), or a list of images. If argument `locations` is not supplied, and `covariates` is supplied, then it must be a list of images.

If the fitted model was a marked (multitype) point process, then predictions are made for each possible mark value in turn.

If the fitted model had no spatial trend, then the default is to omit calculating this (flat) surface, unless `trend=TRUE` is set explicitly.

If the fitted model was Poisson, so that there were no spatial interactions, then the conditional intensity and spatial trend are identical, and the default is to omit the conditional intensity, unless `cif=TRUE` is set explicitly.

If `plot.it=TRUE` then `plot.plotppm()` is called upon to plot the class `plotppm` object which is produced. (That object is also returned, silently.)

Plots are produced successively using `persp`, `image` and `contour` (or only a selection of these three, if `how` is given). Extra graphical parameters controlling the display may be passed directly via the arguments `...` or indirectly reset using `spatstat.options`.

### Value

An object of class `plotppm`. Such objects may be plotted by `plot.plotppm()`.

This is a list with components named `trend` and `cif`, either of which may be missing. They will be missing if the corresponding component does not make sense for the model, or if the corresponding argument was set equal to `FALSE`.

Both `trend` and `cif` are lists of images. If the model is an unmarked point process, then they are lists of length 1, so that `trend[[1]]` is an image of the spatial trend and `cif[[1]]` is an image of the conditional intensity.

If the model is a marked point process, then `trend[[i]]` is an image of the spatial trend for the mark `m[i]`, and `cif[[1]]` is an image of the conditional intensity for the mark `m[i]`, where `m` is the vector of levels of the marks.

### Warnings

See warnings in `predict.ppm`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk

### See Also

`plot.plotppm`, `ppm`, `ppm.object`, `predict.ppm`, `print.ppm`, `persp`, `image`, `contour`, `plot`, `spatstat.options`

### Examples

     m <- ppm(cells ~1, Strauss(0.05))
     pm <- plot(m) # The object ``pm'' will be plotted as well as saved
                   # for future plotting.
     pm

[Package spatstat.core version 2.3-1 Index]