# ppmInfluence

##### Leverage and Influence Measures for Spatial Point Process Model

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"`

.

##### See Also

##### Examples

```
# NOT RUN {
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X ~ x+y)
fI <- ppmInfluence(fit)
fI$influence
influence(fI)
fI$leverage
fI$dfbetas
# }
```

*Documentation reproduced from package spatstat, version 1.55-1, License: GPL (>= 2)*