Prediction for Fitted Multiple Point Process Model
Given a fitted multiple point process model obtained by
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.
## 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
- New values of the covariates, for which the predictions should be computed.
newdata=NULL, predictions are computed for the original values of the covariates, to which the model was fitted. Otherwise
- Type of predicted values required. A character string or vector of
character strings. Options are
"trend"for the spatial trend (first-order term) and
"lambda"for the conditional intensity.
- Dimensions of the grid of spatial locations at which prediction will be
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
- 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.
locations=NULL, then predictions are performed
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.
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
- A hyperframe with columns named
locations=NULL, the entries of the hyperframe are pixel images.
locationsis not null, the entries are marked point patterns constructed by attaching the predicted values to the
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.
data(waterstriders) h <- hyperframe(Bugs=waterstriders) fit <- mppm(Bugs ~ x, data=h, interaction=Strauss(7)) # prediction on a grid p <- predict(fit) plot(p$trend) # prediction at specified locations loc <- with(h, runifpoint(20, Bugs$window)) p2 <- predict(fit, locations=loc) plot(p2$trend)