R: Variance-Covariance Matrix for a Fitted Spatial Logistic...

vcov.slrm {spatstat.core} | R Documentation

## Variance-Covariance Matrix for a Fitted Spatial Logistic Regression

### Description

Returns the variance-covariance matrix of the estimates of the parameters of a point process model that was fitted by spatial logistic regression.

### Usage

      ## S3 method for class 'slrm'
    vcov(object, ...,
             what=c("vcov", "corr", "fisher", "Fisher"))

### Arguments

`object` | A fitted point process model of class `"slrm"`.
`...` | Ignored.
`what` | Character string (partially-matched) that specifies what matrix is returned. Options are `"vcov"` for the variance-covariance matrix, `"corr"` for the correlation matrix, and `"fisher"` or `"Fisher"` for the Fisher information matrix.

### Details

This function computes the asymptotic variance-covariance matrix of the estimates of the canonical parameters in the point process model `object`. It is a method for the generic function `vcov`.

`object` should be an object of class `"slrm"`, typically produced by `slrm`. It represents a Poisson point process model fitted by spatial logistic regression.

The canonical parameters of the fitted model `object` are the quantities returned by `coef.slrm(object)`. The function `vcov` calculates the variance-covariance matrix for these parameters.

The argument `what` provides three options:

`what="vcov"`

  return the variance-covariance matrix of the parameter estimates

`what="corr"`

  return the correlation matrix of the parameter estimates

`what="fisher"`

  return the observed Fisher information matrix.

In all three cases, the result is a square matrix. The rows and columns of the matrix correspond to the canonical parameters given by `coef.slrm(object)`. The row and column names of the matrix are also identical to the names in `coef.slrm(object)`.

Note that standard errors and 95% confidence intervals for the coefficients can also be obtained using `confint(object)` or `coef(summary(object))`.

Standard errors for the fitted intensity can be obtained using `predict.slrm`.

### Value

A square matrix.

### Error messages

An error message that reports system is computationally singular indicates that the determinant of the Fisher information matrix was either too large or too small for reliable numerical calculation. This can occur because of numerical overflow or collinearity in the covariates.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner r.turner@auckland.ac.nz .

### References

Baddeley, A., Berman, M., Fisher, N.I., Hardegen, A., Milne, R.K., Schuhmacher, D., Shah, R. and Turner, R. (2010) Spatial logistic regression and change-of-support for spatial Poisson point processes. Electronic Journal of Statistics 4, 1151–1201. doi: 10.1214/10-EJS581

### See Also

`vcov` for the generic,

`slrm` for information about fitted models,

`predict.slrm` for other kinds of calculation about the model,

`confint` for confidence intervals.

### Examples

      X <- rpoispp(42)
      fit <- slrm(X ~ x + y)
      vcov(fit)
      vcov(fit, what="corr")
      vcov(fit, what="f")

[Package spatstat.core version 1.65-5 Index]