Fit the SCCS model

fitSccsModel(
sccsIntervalData,
prior = createPrior("laplace", useCrossValidation = TRUE),
control = createControl(cvType = "auto", selectorType = "byPid", startingVariance =
0.1, noiseLevel = "quiet")
)

## Arguments

sccsIntervalData An object of type SccsIntervalData as created using the createSccsIntervalData function. The prior used to fit the model. See Cyclops::createPrior for details. The control object used to control the cross-validation used to determine the hyperparameters of the prior (if applicable). See Cyclops::createControl for details.

## 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.

## 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