Prediction for Fitted Multiple Point Process Model

Given a fitted multiple point process model obtained by mppm, evaluate the spatial trend and/or the conditional intensity of the model. By default, predictions are evaluated over a grid of locations, yielding pixel images of the trend and conditional intensity. Alternatively predictions may be evaluated at specified locations with specified values of the covariates.

models, spatial
## S3 method for class 'mppm':
predict(object, ..., newdata = NULL, type = c("trend", "cif"),
             ngrid = 40, locations=NULL, verbose=FALSE)
The fitted model. An object of class "mppm" obtained from mppm.
New values of the covariates, for which the predictions should be computed. If newdata=NULL, predictions are computed for the original values of the covariates, to which the model was fitted. Otherwise newdata should
Type of predicted values required. A character string or vector of character strings. Options are "trend" for the spatial trend (first-order term) and "cif" or "lambda" for the conditional intensity.
Dimensions of the grid of spatial locations at which prediction will be performed (if locations=NULL). An integer or a pair of integers.
Optional. The locations at which predictions should be performed. A list of point patterns, with one entry for each row of newdata.
Logical flag indicating whether to print progress reports.

This function computes the spatial trend and the conditional intensity of a fitted multiple spatial point process model. See Baddeley and Turner (2000) and Baddeley et al (2007) for explanation and examples. Note that by ``spatial trend'' we mean the (exponentiated) first order potential and not the intensity of the process. [For example if we fit the stationary Strauss process with parameters $\beta$ and $\gamma$, then the spatial trend is constant and equal to $\beta$.] The conditional intensity $\lambda(u,X)$ of the fitted model is evaluated at each required spatial location u, with respect to the response point pattern X.

If locations=NULL, then predictions are performed at an ngrid by ngrid grid of locations in the window for each response point pattern. The result will be a hyperframe containing a column of images of the trend (if selected) and a column of images of the conditional intensity (if selected). The result can be plotted.

If locations is given, then it should be a list of point patterns (objects of class "ppp"). Predictions are performed at these points. The result is a hyperframe containing a column of marked point patterns where the locations each point.


  • A hyperframe with columns named trend and cif.

    If locations=NULL, the entries of the hyperframe are pixel images.

    If locations is not null, the entries are marked point patterns constructed by attaching the predicted values to the locations point patterns.


Baddeley, A. and Turner, R. Practical maximum pseudolikelihood for spatial point patterns. Australian and New Zealand Journal of Statistics 42 (2000) 283--322. Baddeley, A., Bischof, L., Sintorn, I.-M., Haggarty, S., Bell, M. and Turner, R. Analysis of a designed experiment where the response is a spatial point pattern. In preparation.

See Also

mppm, fitted.mppm, hyperframe

  • predict.mppm
h <- hyperframe(Bugs=waterstriders)
  fit <- mppm(Bugs ~ x, data=h, interaction=Strauss(7))
  # prediction on a grid
  p <- predict(fit)
  # prediction at specified locations
  loc <- with(h, runifpoint(20, Bugs$window))
  p2 <- predict(fit, locations=loc)
Documentation reproduced from package spatstat, version 1.41-1, License: GPL (>= 2)

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