mpl,
evaluate the spatial trend and the conditional intensity of the model
at new locations.predict.ppm(object, nx = 40, ny = NULL, type="trend")
predict.ppm(object, newdata, type="trend")mpl. An object of
class "ppm" (see ppm.objectnx,ny)nx by ny) rectangular grid of positions in the
original window of observation.
(Incompatible with newdata)nx above; ny defaults to the same value as nx"trend" for the spatial trend, and
"cif" or "lambda" for the conditional intensity.newdata is given:
a vector of predicted values for the spatial locations
(and marks and covariates) given in the rows of newdata. If newdata is not given:
a list with components
x, y, z giving the
coordinates and predicted values for a rectangular grid of points.
If object is an unmarked point process,
z is a matrix such that z[i,j]
gives the predicted value at location (x[i],y[j]);
for marked point processes,
z is a three-dimensional array
such that z[i,j,m] gives the predicted value for a
point at location (x[i],y[j]) with mark m.
The ``predicted values'' are either values of the spatial trend
(if type="trend") or values of the conditional intensity
(if type="cif" or type="lambda").
predict.glm
so that prediction is wrong if the trend formula in
object involves terms in ns(),
bs() or poly().
This is a weakness of predict.glm itself!
Error messages may be very opaque,
as they tend to come from deep in the workings of
predict.glm.
If you are passing the newdata argument
and the function crashes,
it is advisable to start by checking that all the conditions
listed above are satisfied.mpl. This
returns an object of class "ppm" representing
the fitted point process model (see ppm.object).
The parameter estimates in this fitted model can be read off
simply by printing the ppm object.
The spatial trend and conditional intensity of the
fitted model are evaluated using this function predict.ppm.
This is feasible in such generality because the Berman-Turner-Baddeley method
(Berman and Turner, 1992; Baddeley and Turner, 2000)
reduces maximum pseudolikelihood estimation to the fitting
of a GLM or GAM, and because Splus/Rcontains predict methods for
glm and gam objects. The current implementation
invokes predict.glm.
The default action is to create a rectangular 40 by 40 grid of points
in the observation window of the data point pattern, and evaluate
the spatial trend and conditional intensity at these locations.
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 data point pattern $X$.
The argument newdata, if given, is a data frame
giving the values of any spatial covariates
at the locations where the trend and conditional intensity should be
computed.
If the trend formula in the fitted model
involves spatial covariates (other than
the Cartesian coordinates x, y)
then newdata is required.
If newdata is present then it must contain variables
matching all the variable names featuring in the trend formula
and the Cartesian coordinates x and y
and the mark values marks.
[This is different from the role of the data
argument in mpl which must not contain
x and y variables.] Note that if you only want to use prediction in order to
generate a plot of the predicted values,
it may be easier to use plot.ppm which calls
this function and plots the results.
mpl,
ppm.object,
plot.ppm,
print.ppmlibrary(spatstat)
data(cells)
m <- mpl(cells, ~ polynom(x,y,2), Strauss(0.05), rbord=0.05)
trend <- predict(m, type="trend")
image(trend)
points(cells)
cif <- predict(m, type="cif")
persp(cif)Run the code above in your browser using DataCamp Workspace