R: Coefficients of Point Process Model Fitted to Multiple Point...

coef.mppm {spatstat} | R Documentation

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

### Description

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

### Usage

      ## S3 method for class 'mppm'
    coef(object, ...)

### 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 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.

### Value

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

### Author(s)

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.

### 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

`fixef.mppm` and `ranef.mppm` for the fixed and random effect coefficients in a model that includes random effects.

`print.mppm`, `mppm`

### Examples

        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)

[Package spatstat version 1.63-3 Index]