`twinSIR`

or `twinstim`

`checkResidualProcess.Rd`

Transform the residual process (cf. the
`residuals`

methods for classes
`"twinSIR"`

and `"twinstim"`

) such that the transformed
residuals should be uniformly distributed if the fitted model
well describes the true conditional intensity function. Graphically
check this using `ks.plot.unif`

.
The transformation for the residuals `tau`

is
`1 - exp(-diff(c(0,tau)))`

(cf. Ogata, 1988).
Another plot inspects the serial correlation between the transformed
residuals (scatterplot between u_i and u_i+1).

`checkResidualProcess(object, plot = 1:2, mfrow = c(1,length(plot)), ...)`

- object
- plot
logical (or integer index) vector indicating if (which) plots of the transformed residuals should be produced. The

`plot`

index 1 corresponds to a`ks.plot.unif`

to check for deviations of the transformed residuals from the uniform distribution. The`plot`

index 2 corresponds to a scatterplot of \(u_i\) vs. \(u_{i+1}\). By default (`plot = 1:2`

), both plots are produced.- mfrow
see

`par`

.- ...
further arguments passed to

`ks.plot.unif`

.

A list (returned invisibly, if `plot = TRUE`

) with the following
components:

- tau
the residual process obtained by

`residuals(object)`

.- U
the transformed residuals which should be distributed as U(0,1).

- ks
the result of the

`ks.test`

for the uniform distribution of`U`

.

Ogata, Y. (1988)
Statistical models for earthquake occurrences and residual analysis
for point processes.
*Journal of the American Statistical Association*, 83, 9-27

Sebastian Meyer

`ks.plot.unif`

and the
`residuals`

-method for classes
`"twinSIR"`

and `"twinstim"`

.

```
data("hagelloch")
fit <- twinSIR(~ household, data = hagelloch) # a simplistic model
## extract the "residual process", i.e., the fitted cumulative intensities
residuals(fit)
## assess goodness of fit based on these residuals
checkResidualProcess(fit) # could be better
```