fitted.mppm
Fitted Conditional Intensity for Multiple Point Process Model
Given a point process model fitted to multiple point patterns, compute the fitted conditional intensity of the model at the points of each data pattern, or at the points of the quadrature schemes used to fit the model.
Usage
# S3 method for mppm
fitted(object, ..., type = "lambda", dataonly = FALSE)Arguments
- object
The fitted model. An object of class
"mppm"obtained frommppm.- …
Ignored.
- type
Type of fitted values: either
"trend"for the spatial trend, or"lambda"or"cif"for the conditional intensity.- dataonly
If
TRUE, fitted values are computed only for the points of the data point patterns. IfFALSE, fitted values are computed for the points of the quadrature schemes used to fit the model.
Details
This function evaluates the conditional intensity \(\hat\lambda(u,x)\) or spatial trend \(\hat{b(u)}\) of the fitted point process model for certain locations \(u\), for each of the original point patterns \(x\) to which the model was fitted.
The locations \(u\) at which the fitted conditional intensity/trend is
evaluated, are the points of the quadrature schemes used to fit the
model in mppm. They include the data points (the points of the
original point pattern datasets) and other ``dummy'' points in
the window of observation.
Use predict.mppm to compute the fitted conditional intensity at
other locations or with other values of the explanatory variables.
Value
A list of vectors (one for each row of the original hyperframe,
i.e. one vector for each of the original point patterns)
containing the values of the fitted conditional intensity
or (if type="trend") the fitted spatial trend.
Entries in these vector correspond to the quadrature points (data
or dummy points) used to fit the model. The quadrature points can
be extracted from object by quad.mppm(object).
References
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. London: Chapman and Hall/CRC Press.
See Also
Examples
# NOT RUN {
model <- mppm(Bugs ~ x, data=hyperframe(Bugs=waterstriders),
interaction=Strauss(7))
cifs <- fitted(model)
# }