R: Leverage and Influence Diagnostics for Spatial Logistic...

leverage.slrm {spatstat.core} | R Documentation

## Leverage and Influence Diagnostics for Spatial Logistic Regression

### Description

For a fitted spatial logistic regression model, these functions compute diagnostics of leverage and influence.

### Usage

    ## S3 method for class 'slrm'
    leverage(model, ...)
    ## S3 method for class 'slrm'
    influence(model, ...)
    ## S3 method for class 'slrm'
    dfbetas(model, ...)
    ## S3 method for class 'slrm'
    dffit(object, ...)

### Arguments

`model,object` | A fitted spatial logistic regression model (object of class `"slrm"`).
`...` | Arguments passed to methods.

### Details

These functions are methods for the generics `leverage`, `influence`, `dfbetas` and `dffit` for the class `"slrm"`.

These functions adapt the standard diagnostics for logistic regression (see `influence.measures`) to a fitted spatial logistic regression model (object of class `"slrm"`). This adaptation was described by Baddeley, Chang and Song (2013).

`leverage.slrm` computes the leverage value (diagonal of the hat matrix) for the covariate data in each pixel. The result is a pixel image.

`influence.slrm` computes the likelihood influence for the data (covariates and presence/absence of points) in each pixel. The result is a pixel image.

`dfbetas.slrm` computes the parameter influence for the data (covariates and presence/absence of points) in each pixel. The result is a list of pixel images, one image for each of the model coefficients in `coef(model)`. The list can be plotted immediately.

`dffit.slrm` computes the total influence for the data (covariates and presence/absence of points) in each pixel. The result is a pixel image.

### Value

A pixel image, or a list of pixel images.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au.

### References

Baddeley, A., Chang, Y.M. and Song, Y. (2013) Leverage and influence diagnostics for spatial point process models. Scandinavian Journal of Statistics 40, 86–104.

### See Also

`influence.measures`.

`leverage.ppm`, `influence.ppm`, `dfbetas.ppm`, `dffit.ppm`

### Examples

       H <- unmark(humberside)
       fit <- slrm(H ~ x+y, dimyx=32)
       plot(leverage(fit))
       plot(influence(fit))
       plot(dfbetas(fit))
       plot(dffit(fit))

[Package spatstat.core version 2.3-1 Index]