R: Leverage and Influence Measures for Spatial Point Process...

ppmInfluence {spatstat} | R Documentation

## Leverage and Influence Measures for Spatial Point Process Model

### Description

Calculates all the leverage and influence measures described in `influence.ppm`, `leverage.ppm` and `dfbetas.ppm`.

### Usage

       ppmInfluence(fit,
                    what = c("leverage", "influence", "dfbetas"),
                    ...,
                    iScore = NULL, iHessian = NULL, iArgs = NULL,
                    drop = FALSE,
                    fitname = NULL)

### Arguments

`fit` | A fitted point process model of class `"ppm"`.
`what` | Character vector specifying which quantities are to be calculated. Default is to calculate all quantities.
`...` | Ignored.
`iScore,iHessian` | Components of the score vector and Hessian matrix for the irregular parameters, if required. See Details.
`iArgs` | List of extra arguments for the functions `iScore`, `iHessian` if required.
`drop` | Logical. Whether to include (`drop=FALSE`) or exclude (`drop=TRUE`) contributions from quadrature points that were not used to fit the model.
`fitname` | Optional character string name for the fitted model `fit`.

### Details

This function calculates all the leverage and influence measures described in `influence.ppm`, `leverage.ppm` and `dfbetas.ppm`.

When analysing large datasets, the user can call `ppmInfluence` to perform the calculations efficiently, then extract the leverage and influence values as desired. For example the leverage can be extracted either as `result$leverage` or `leverage(result)`.

If the point process model trend has irregular parameters that were fitted (using `ippm`) then the influence calculation requires the first and second derivatives of the log trend with respect to the irregular parameters. The argument `iScore` should be a list, with one entry for each irregular parameter, of R functions that compute the partial derivatives of the log trend (i.e. log intensity or log conditional intensity) with respect to each irregular parameter. The argument `iHessian` should be a list, with p^2 entries where p is the number of irregular parameters, of R functions that compute the second order partial derivatives of the log trend with respect to each pair of irregular parameters.

### Value

A list containing the leverage and influence measures specified by `what`. The result also belongs to the class `"ppmInfluence"`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

### See Also

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

### Examples

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

       fitlev <- fI$leverage
       fitlev <- leverage(fI)

       fitinf <- fI$influence
       fitinf <- influence(fI)

       fitdfb <- fI$dfbetas
       fitdfb <- dfbetas(fI)

[Package spatstat version 1.63-3 Index]