emend.slrm: Force Spatial Logistic Regression Model to be Valid

In spatstat.core: Core Functionality of the 'spatstat' Family

Force Spatial Logistic Regression Model to be Valid

Description

Ensures that a fitted spatial logistic regression specifies a well-defined model.

Usage

## S3 method for class 'slrm'
emend(object, ..., fatal=FALSE, trace=FALSE)

Arguments

object	Fitted point process model (object of class "slrm").
...	Ignored.
fatal	Logical value indicating whether to generate an error if the model cannot be projected to a valid model.
trace	Logical value indicating whether to print a trace of the decision process.

Details

emend.slrm is a method for the generic emend,

The purpose of the function is to ensure that a fitted model is valid.

The model-fitting function slrm fits spatial logistic regression models to point pattern data.

In some circumstances, the fitted model returned by slrm may not specify a well-defined model, because some of the fitted coefficients of the model may be NA or infinite values. This usually occurs when the data are insufficient to estimate all the parameters. The model is said to be unidentifiable or confounded.

The function emend.slrm modifies the model object so that the model is valid. It identifies the terms in the model object that are associated with illegal parameter values (i.e. parameter values which are either NA, infinite, or outside their permitted range). It considers all possible sub-models of object obtained by deleting one or more of these terms. It identifies which of these submodels are valid, and chooses the valid submodel with the largest pseudolikelihood. The result of emend.slrm or project.slrm is the true maximum pseudolikelihood fit to the data.

For large datasets or complex models, the algorithm used in emend.slrm may be time-consuming, because it takes time to compute all the sub-models. A faster, approximate algorithm can be applied by setting spatstat.options(project.fast=TRUE). This produces a valid submodel, which may not be the maximum likelihood submodel.

Use the function valid.slrm to check whether a fitted model object specifies a well-defined model.

Value

Another point process model (object of class "slrm").

Author(s)

\spatstatAuthors

.

See Also

slrm, valid.slrm, emend, spatstat.options

Examples

   fit <- slrm(redwood ~ x + I(x))
   coef(fit)
   fit2 <- emend(fit)
   coef(fit2)

spatstat.core documentation built on May 18, 2022, 9:05 a.m.