# dffit.ppm

##### Case Deletion Effect Measure of Fitted Model

Computes the case deletion effect measure `DFFIT`

for a fitted model.

##### Usage

`dffit(object, …)`# S3 method for 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 (2018).

##### Value

A measure (object of class `"msr"`

).

##### References

Baddeley, A., Rubak, E. and Turner, R. (2018)
*Leverage and influence diagnostics for Gibbs spatial point
processes*.
In preparation.

##### See Also

##### Examples

```
# NOT RUN {
# }
# NOT RUN {
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X ~x+y)
# }
# NOT RUN {
plot(dffit(fit))
plot(dffit(fit, collapse=TRUE))
# }
# NOT RUN {
# }
```

*Documentation reproduced from package spatstat, version 1.57-1, License: GPL (>= 2)*