```
fitSccsModel(
sccsIntervalData,
prior = createPrior("laplace", useCrossValidation = TRUE),
control = createControl(cvType = "auto", selectorType = "byPid", startingVariance =
0.1, seed = 1, resetCoefficients = TRUE, noiseLevel = "quiet"),
profileGrid = NULL,
profileBounds = c(log(0.1), log(10))
)
```

## Arguments

- sccsIntervalData
An object of type SccsIntervalData as created using the
createSccsIntervalData function.

- prior
The prior used to fit the model. See Cyclops::createPrior for
details.

- control
The control object used to control the cross-validation used to determine the
hyperparameters of the prior (if applicable). See
Cyclops::createControl for details.

- profileGrid
A one-dimensional grid of points on the log(relative risk) scale where
the likelihood for coefficient of variables is sampled. See details.

- profileBounds
The bounds (on the log relative risk scale) for the adaptive sampling
of the likelihood function.

## Value

An object of type `SccsModel`

. Generic functions `print`

, `coef`

, and
`confint`

are available.

## Details

Fits the SCCS model as a conditional Poisson regression. When allowed, coefficients for some or all
covariates can be regularized.

Likelihood profiling is only done for variables for which `profileLikelihood`

is set to `TRUE`

when
calling `createEraCovariateSettings()`

. Either specify the `profileGrid`

for a completely user-
defined grid, or `profileBounds`

for an adaptive grid. Both should be defined on the log IRR scale.
When both `profileGrid`

and `profileGrid`

are `NULL`

likelihood profiling is disabled.

## References

Suchard, M.A., Simpson, S.E., Zorych, I., Ryan, P., and Madigan, D. (2013). Massive parallelization
of serial inference algorithms for complex generalized linear models. ACM Transactions on Modeling
and Computer Simulation 23, 10