# coef.mppm

0th

Percentile

##### Coefficients of Point Process Model Fitted to Multiple Point Patterns

Given a point process model fitted to a list of point patterns, extract the coefficients of the fitted model. A method for coef.

Keywords
models, methods, spatial
##### Usage
## S3 method for class 'mppm':
coef(object, \dots)
##### Arguments
object
The fitted point process model (an object of class "mppm")
...
Ignored.
##### Details

This function is a method for the generic function coef. The argument object must be a fitted point process model (object of class "mppm") produced by the fitting algorithm mppm). This represents a point process model that has been fitted to a list of several point pattern datasets. See mppm for information.

This function extracts the vector of coefficients of the fitted model. This is the estimate of the parameter vector $\theta$ such that the conditional intensity of the model is of the form $$\lambda(u,x) = \exp(\theta S(u,x))$$ where $S(u,x)$ is a (vector-valued) statistic.

For example, if the model object is the uniform Poisson process, then coef(object) will yield a single value (named "(Intercept)") which is the logarithm of the fitted intensity of the Poisson process.

Use print.mppm to print a more useful description of the fitted model.

##### Value

• A vector containing the fitted coefficients.

print.mppm, mppm

• coef.mppm
##### Examples
data(waterstriders)
H <- hyperframe(X=waterstriders)
fit.Poisson <- mppm(X ~ 1, H)
coef(fit.Poisson)

# The single entry "(Intercept)"
# is the log of the fitted intensity of the Poisson process

fit.Strauss <- mppm(X~1, H, Strauss(7))
coef(fit.Strauss)

# The two entries "(Intercept)" and "Interaction"
# are respectively log(beta) and log(gamma)
# in the usual notation for Strauss(beta, gamma, r)
Documentation reproduced from package spatstat, version 1.37-0, License: GPL (>= 2)

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