# checkResidualProcess: Check the residual process of a fitted 'twinSIR' or... In surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

 checkResidualProcess R Documentation

## Check the residual process of a fitted `twinSIR` or `twinstim`

### Description

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}`).

### Usage

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

### Arguments

 `object` an object of class `"twinSIR"` or `"twinstim"`. `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`.

### Value

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`.

Sebastian Meyer

### References

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

`ks.plot.unif` and the `residuals`-method for classes `"twinSIR"` and `"twinstim"`.

### Examples

``````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
``````

surveillance documentation built on Nov. 2, 2023, 6:05 p.m.