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 mppm
coef(object, …)
```

object

The fitted point process model (an object of class `"mppm"`

)

…

Ignored.

Either a vector containing the fitted coefficients, or a data frame containing the fitted coefficients for each point pattern.

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.

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.

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.

# NOT RUN { 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) # }