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.
## S3 method for class 'mppm' coef(object, ...)
object |
The fitted point process model (an object of class |
... |
Ignored. |
This function is a method for the generic function coef.
This function extracts the vector of coefficients of the fitted model. This is the estimate of the parameter vector θ such that the conditional intensity of the model is of the form
λ(u,x) = \exp(θ . 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.
If the fitted model includes random effects (i.e. if the argument
random was specified in the call to mppm),
then the fitted coefficients are different for each point pattern
in the original data, so coef(object) is a data frame
with one row for each point pattern, and one column for each
parameter. Use fixef.mppm to extract the vector of fixed effect
coefficients, and ranef.mppm to extract the random
effect coefficients at each level.
Use print.mppm to print a more useful
description of the fitted model.
Either a vector containing the fitted coefficients, or a data frame containing the fitted coefficients for each point pattern.
Adrian Baddeley, Ida-Maria Sintorn and Leanne Bischoff. Implemented in spatstat by Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. London: Chapman and Hall/CRC Press.
fixef.mppm and ranef.mppm
for the fixed and random effect coefficients in a model that includes
random effects.
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)
# Tweak data to exaggerate differences
H$X[[1]] <- rthin(H$X[[1]], 0.3)
# Model with random effects
fitran <- mppm(X ~ 1, H, random=~1|id)
coef(fitran)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.