plot.ppm

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plot a Fitted Point Process Model

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.

Keywords
spatial
Usage
plot.ppm(x, ngrid = c(40,40), superimpose = TRUE, 
                  trend = TRUE, cif = TRUE, pause = TRUE,
                  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.
pause
logical flag indicating whether to pause with a prompt after each plot. Set pause=FALSE if plotting to a file. 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.

See Also

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

Aliases
  • plot.ppm
Examples
data(cells)
 m <- ppm(cells, ~1, Strauss(0.05))
 pm <- plot(m) # The object ``pm'' will be plotted as well as saved
               # for future plotting.
Documentation reproduced from package spatstat, version 1.6-2, License: GPL version 2 or newer

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