Fitted Conditional Intensity for Point Process Model
Given a point process model fitted to a point pattern, compute the fitted conditional intensity or fitted trend of the model at the points of the pattern, or at the points of the quadrature scheme used to fit the model.
## S3 method for class 'ppm'
fitted(object, ..., type="lambda",
dataonly=FALSE, new.coef=NULL, leaveoneout=FALSE,
drop=FALSE, check=TRUE, repair=TRUE,
ignore.hardcore=FALSE, dropcoef=FALSE)object |
The fitted point process model (an object of class |
... |
Ignored. |
type |
String (partially matched) indicating whether the fitted value is the
conditional intensity ( |
dataonly |
Logical. If |
new.coef |
Numeric vector of parameter values to replace the
fitted model parameters |
leaveoneout |
Logical. If |
drop |
Logical value determining whether to delete quadrature points that were not used to fit the model. |
check |
Logical value indicating whether to check the internal format
of |
repair |
Logical value indicating whether to repair the internal format
of |
ignore.hardcore |
Advanced use only. Logical value specifying whether to compute only the finite part of the interaction potential (effectively removing any hard core interaction terms). |
dropcoef |
Internal use only. |
The argument object must be a fitted point process model
(object of class "ppm"). Such objects are produced by the
model-fitting algorithm ppm).
This function evaluates the conditional intensity
lambdahat(u,x)
or spatial trend bhat(u) of the fitted point process
model for certain locations u,
where x is the original point pattern dataset to which
the model was fitted.
The locations u at which the fitted conditional intensity/trend
is evaluated, are the points of the
quadrature scheme used to fit the model in ppm.
They include the data points (the points of the original point pattern
dataset x) and other “dummy” points
in the window of observation.
If leaveoneout=TRUE, fitted values will be computed
for the data points only, using a ‘leave-one-out’ rule:
the fitted value at X[i] is effectively computed by
deleting this point from the data and re-fitting the model to the
reduced pattern X[-i], then predicting the value at
X[i]. (Instead of literally performing this calculation,
we apply a Taylor approximation using the influence function
computed in dfbetas.ppm.
The argument drop is explained in quad.ppm.
Use predict.ppm to compute the fitted conditional
intensity at other locations or with other values of the
explanatory variables.
A vector containing the values of the fitted conditional intensity, fitted spatial trend, or logarithm of the fitted conditional intensity.
Entries in this vector correspond to the quadrature points (data or
dummy points) used to fit the model. The quadrature points can be
extracted from object by union.quad(quad.ppm(object)).
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes (with discussion). Journal of the Royal Statistical Society, Series B 67, 617–666.
str <- ppm(cells ~x, Strauss(r=0.1))
lambda <- fitted(str)
# extract quadrature points in corresponding order
quadpoints <- union.quad(quad.ppm(str))
# plot conditional intensity values
# as circles centred on the quadrature points
quadmarked <- setmarks(quadpoints, lambda)
plot(quadmarked)
if(!interactive()) str <- ppm(cells ~ x)
lambdaX <- fitted(str, leaveoneout=TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.