R: Residuals for Fitted Spatial Logistic Regression Model

residuals.slrm {spatstat.core} | R Documentation

## Residuals for Fitted Spatial Logistic Regression Model

### Description

Given a spatial logistic regression model fitted to a point pattern, compute the residuals for each pixel.

### Usage

      ## S3 method for class 'slrm'
    residuals(object,
                               type=c("raw", "deviance", "pearson", "working",
                                      "response", "partial", "score"),
                               ...)

### Arguments

`object` | The fitted point process model (an object of class `"ppm"`) for which residuals should be calculated.
`type` | String (partially matched) indicating the type of residuals to be calculated.
`...` | Ignored.

### Details

This function computes several kinds of residuals for the fit of a spatial logistic regression model to a spatial point pattern dataset.

The argument `object` must be a fitted spatial logistic regression model (object of class `"slrm"`). Such objects are created by the fitting algorithm `slrm`.

The residuals are computed for each pixel that was used to fit the original model. The residuals are returned as a pixel image (if the residual values are scalar), or a list of pixel images (if the residual values are vectors).

The type of residual is chosen by the argument `type`.

For a given pixel, suppose p is the fitted probability of presence of a point, and y is the presence indicator (equal to 1 if the pixel contains any data points, and equal to 0 otherwise). Then

- `type="raw"` or `type="response"` specifies the response residual

  r = y - p

- `type="pearson"` is the Pearson residual

  rP = (y-p)/sqrt(p * (1-p))

- `type="deviance"` is the deviance residual

  rD = (-1)^(y+1) (-2(y log p + (1-y) log(1-p)))^(1/2)

- `type="score"` specifies the score residuals

  rS = (y-p) x

  where `x` is the vector of canonical covariate values for the pixel

- `type="working"` specifies the working residuals as defined in `residuals.glm`

- `type="partial"` specifies the partial residuals as defined in `residuals.glm`

### Value

A pixel image (if the residual values are scalar), or a list of pixel images (if the residual values are vectors).

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

### See Also

`residuals.glm`, `residuals.ppm`

### Examples

       d <- if(interactive()) 128 else 32
       H <- unmark(humberside)
       fit <- slrm(H ~ x + y, dimyx=d)

       plot(residuals(fit))

       plot(residuals(fit, type="score"))

[Package spatstat.core version 2.4-4 Index]