ppm,
evaluate the spatial trend or the conditional intensity of the model
at new locations.## S3 method for class 'ppm':
predict(object, window=NULL, ngrid=NULL, locations=NULL,
covariates=NULL,
type=c("trend", "cif", "intensity", "count"),
se=FALSE,
interval=c("none", "confidence", "prediction"),
level = 0.95,
X=data.ppm(object), correction,
..., new.coef=NULL, check=TRUE, repair=TRUE)ppm. An object of
class "ppm" (see ppm.object)."owin")
delimiting the locations where predictions
should be computed. Defaults to the window of the
original data used to fit the model object.window where the predictions should be computed.
An integer, or an integer vector of length 2,
specifying the number of grid points in the $y$ and $x$
directions. x and
y, or a binary imag"trend" for the spatial trend,
"cif" or "lambda" for the conditional intensity,
"intensity"interval="none", the default)
or a confidence interval (interval="confidence")
or a prediction interval (interval="prediction")."ppp")
to be taken as the data point pattern when calculating the
conditional intensity. The default is to use the original data
to which the model was fitted."border" and "none".
Other options may include "periodic",
"isotropic" and "translatcoef(object).object. If there is any possibility that this object
has been restored from a dump file, or has otherwise lost track of
the environment where it was originally compuobject, if it is found to be damaged.total is given:
a numeric vector or matrix.
If locations is given and is a data frame:
a vector of predicted values for the spatial locations
(and marks, if required) given in locations. If ngrid is given, or if locations is given
and is a binary image mask or a pixel image:
If object is an unmarked point process,
the result is a pixel image object (of class "im", see
im.object) containing the predictions.
If object is a multitype point process,
the result is a list of pixel images, containing the predictions
for each type at the same grid of locations.
The ``predicted values'' are either values of the spatial trend
(if type="trend"), values of the conditional intensity
(if type="cif" or type="lambda"),
values of the intensity (if type="intensity")
or numbers of points (if type="count").
If se=TRUE, then the result is a list with two entries,
the first being the predicted values in the format described above,
and the second being the standard errors in the same format.
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 covariates argument
and the function crashes,
it is advisable to start by checking that all the conditions
listed above are satisfied."ppm"). For a Poisson point process
it can compute the fitted intensity function, or the expected number of
points in a region. For a Gibbs point process it can compute the
spatial trend (first order potential), conditional intensity,
and approximate intensity of the process.
Point estimates, standard errors,
confidence intervals and prediction intervals are available.
Given a point pattern dataset, we may fit
a point process model to the data using the
model-fitting algorithm ppm. 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, conditional intensity and intensity of the
fitted model are evaluated using this function predict.ppm.The default action is to create a rectangular grid of points in the observation window of the data point pattern, and evaluate the spatial trend at these locations.
The argument type specifies the values that are desired:
[object Object],[object Object],[object Object],[object Object]
The spatial trend, conditional intensity, and intensity
are all equivalent if the fitted model is a Poisson point process.
However, if the model is not a Poisson process, then they are
all different. The ``spatial trend'' is 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 $\beta$,
while the intensity is a smaller value.]
The default is to compute an estimate of the desired quantity.
If interval="confidence" or interval="prediction",
the estimate is replaced by a confidence interval or prediction interval.
If se=TRUE, then a standard error is also calculated,
and is returned together with the (point or interval) estimate.
The spatial locations where predictions are required,
are determined by the (incompatible)
arguments ngrid and locations.
ngridis present, then
predictions are performed at a rectangular
grid of locations in the windowwindow.
The result of prediction will be a pixel image or images.locationsis present, then predictions
will be performed at the spatial locations given by
this dataset. These may be an arbitrary list of spatial locations,
or they may be a rectangular grid.
The result of prediction will be either a numeric vector
or a pixel image or images.ngridnorlocationsis given, thenngridis assumed. The value ofngriddefaults tospatstat.options("npixel"), which is initialised to 128
whenlocations may be a point pattern,
a data frame or a list specifying arbitrary locations;
or it may be a binary image mask (an object of class "owin"
with type "mask") or a pixel image (object of class
"im") specifying (a subset of) a rectangular
grid of locations.
locationsis a point pattern (object of class"ppp"),
then prediction will be performed at the points of the point pattern.
The result of prediction will be a vector of predicted values,
one value for each point.
If the model is a marked point process, thenlocationsshould be a marked point pattern, with marks of the
same kind as the model; prediction will be performed at these
marked points.
The result of prediction will be a vector of predicted values,
one value for each (marked) point.locationsis a data frame or list, then it must contain
vectorslocations$xandlocations$yspecifying the$x,y$coordinates of the prediction locations. Additionally, if
the model is a marked point process, thenlocationsmust also contain
a factorlocations$marksspecifying the marks of the
prediction locations. These vectors must have equal length.
The result of prediction will be a vector of predicted values,
of the same length.locationsis a binary image mask, then prediction will be
performed at each pixel in this binary image where the pixel value
isTRUE(in other words, at each pixel that is inside the
window). If the fitted model is an unmarked point process, then the
result of prediction will be an image. If the fitted model is a
marked point process, then prediction will
be performed for each possible value of the mark at each such
location, and the result of prediction will be a
list of images, one for each mark value.locationsis a pixel image (object of class"im"),
then prediction will be performed at each pixel in this image where
the pixel value is defined (i.e.\ where the pixel value is notNA).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 format and use of covariates are analogous to those of the
argument of the same name in ppm.
It is either a data frame or a list of images.
covariatesis a list of images, then
the names of the entries should correspond to
the names of covariates in the model formulatrend.
Each entry in the list must be an image object (of class"im",
seeim.object).
The software will look up
the pixel values of each image at the quadrature points.covariatesis a data frame, then theith row ofcovariatesis assumed to contain covariate data for theith location.
Whenlocationsis a data frame,
this just means that each row ofcovariatescontains the
covariate data for the location specified in the corresponding row oflocations. Whenlocationsis a binary image
mask, the rowcovariates[i,]must correspond to the locationx[i],y[i]wherex = as.vector(raster.x(locations))andy = as.vector(raster.y(locations)). 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.
ppm,
ppm.object,
plot.ppm,
print.ppm,
fitted.ppm,
spatstat.options<testonly>op <- spatstat.options(npixel=32)</testonly>
m <- ppm(cells ~ polynom(x,y,2), Strauss(0.05))
trend <- predict(m, type="trend")
image(trend)
points(cells)
cif <- predict(m, type="cif")
persp(cif)
data(japanesepines)
mj <- ppm(japanesepines ~ harmonic(x,y,2))
se <- predict(mj, se=TRUE)
# prediction interval for total number of points
predict(mj, type="count", interval="p")
# prediction at arbitrary locations
predict(mj, locations=data.frame(x=0.3, y=0.4))
X <- runifpoint(5, Window(japanesepines))
predict(mj, locations=X, se=TRUE)
# multitype
rr <- matrix(0.06, 2, 2)
ma <- ppm(amacrine ~ marks, MultiStrauss(rr))
Z <- predict(ma)
Z <- predict(ma, type="cif")
predict(ma, locations=data.frame(x=0.8, y=0.5,marks="on"), type="cif")
<testonly>spatstat.options(op)</testonly>Run the code above in your browser using DataLab