R: Case Deletion Effect Measure of Fitted Model

dffit.ppm {spatstat.core} | R Documentation

## Case Deletion Effect Measure of Fitted Model

### Description

Computes the case deletion effect measure `DFFIT` for a fitted model.

### Usage

    dffit(object, ...)

    ## S3 method for class 'ppm'
    dffit(object, ..., collapse = FALSE, dfb = NULL)

### Arguments

`object` | A fitted model, such as a point process model (object of class `"ppm"`).
`...` | Additional arguments passed to `dfbetas.ppm`.
`collapse` | Logical value specifying whether to collapse the vector-valued measure to a scalar-valued measure by adding all the components.
`dfb` | Optional. The result of `dfbetas(object)`, if it has already been computed.

### Details

The case deletion effect measure `DFFIT` is a model diagnostic traditionally used for regression models. In that context, `DFFIT[i,j]` is the negative change, in the value of the `j`th term in the linear predictor, that would occur if the `i`th data value was deleted. It is closely related to the diagnostic `DFBETA`.

For a spatial point process model, `dffit` computes the analogous spatial case deletion diagnostic, described in Baddeley, Rubak and Turner (2019).

### Value

A measure (object of class `"msr"`).

### Author(s)

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. (2019) Leverage and influence diagnostics for Gibbs spatial point processes. Spatial Statistics 29, 15–48.

### See Also

`dfbetas.ppm`

### Examples

       X <- rpoispp(function(x,y) { exp(3+3*x) })
       fit <- ppm(X ~x+y)

       plot(dffit(fit))
       plot(dffit(fit, collapse=TRUE))

[Package spatstat.core version 2.3-1 Index]